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Active matter consists of units that generate mechanical work by consuming energy. Examples include living systems, such as assemblies of bacteria and biological tissues, biopolymers driven by molecular motors, and suspensions of synthetic self-propelled particles. A central question in the field is to understand and control the self-organization of active assemblies in space and time. Most active systems exhibit either spatial order mediated by interactions that coordinate the spatial structure and the motion of active agents or the temporal synchronization of individual oscillatory dynamics. The simultaneous control of spatial and temporal organization is more challenging and generally requires complex interactions, such as reaction-diffusion hierarchies or genetically engineered cellular circuits. Here, we report a novel and simple means to simultaneously control the spatial and temporal self-organization of bacterial active matter. By confining an active bacterial suspension and manipulating a single macroscopic parameter, namely the viscoelasticity of the suspending fluid, we have found that the bacterial fluid first self-organizes in space into a millimeter-scale rotating vortex; then displays temporal organization as the giant vortex switches its global chirality periodically with tunable frequency, reminiscent of a torsional pendulum - a self-driven one. Combining experiments with an active matter model, we explain this striking behavior in terms of the interplay between active forcing and viscoelastic stress relaxation. Our findings advance the understanding of bacterial behavior in complex fluids, and demonstrate experimentally for the first time that rheological properties can be harnessed to control active matter flows. Coupled with actuation, our tunable self-oscillating bacterial vortex may be used as a "clock" for locomotion of soft robots and microfluidic pumping.

Electrons on the lattice subject to a strong magnetic field exhibit the fractal spectrum of electrons, which is known as the Hofstadter butterfly. In this work, we investigate unconventional superconductivity in a three-dimensional Hofstadter butterfly system. While it is generally difficult to achieve the Hofstadter regime, we show that the quasi-two-dimensional materials with a tilted magnetic field produce the large-scale superlattices, which generate the Hofstadter butterfly even at the moderate magnetic field strength. We first show that the van-Hove singularities of the butterfly flat bands greatly elevate the superconducting critical temperature, offering a new mechanism of field-enhanced superconductivity. Furthermore, we demonstrate that the quantum geometry of the Landau mini-bands plays a crucial role in the description of the superconductivity, which is shown to be clearly distinct from the conventional superconductors. Finally, we discuss the relevance of our results to the recently discovered re-entrant superconductivity of UTe2 in strong magnetic fields.

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In this paper we study homogeneous polynomials and vector subspaces of a polynomial ring that are divisible by a large power of a linear form. We compute their cactus and border cactus ranks. We show that for $d\geq 5$, the component of the cactus variety $\kappa_{14}(\nu_d(\mathbb{P}^6))$ other than the secant variety $\sigma_{14}(\nu_d(\mathbb{P}^6))$ consists of degree $d$ polynomials divisible by $(d-3)$-th power of a linear form. For $d\geq 6$ we present an algorithm for deciding whether a point in the cactus variety $\kappa_{14}(\nu_d(\mathbb{P}^6))$ belongs to the secant variety $\sigma_{14}(\nu_d(\mathbb{P}^6))$. Analogously, we show that for $d\geq 5$, the component of the Grassmann cactus variety $\kappa_{8,3}(\nu_d(\mathbb{P}^4))$ other than the Grassmann secant variety $\sigma_{8,3}(\nu_d(\mathbb{P}^4))$ consists of subspaces divisible by $(d-2)$-th power of a linear form. Finally, for $d\geq 5$ we present an algorithm for deciding whether a point in the Grassmann cactus variety $\kappa_{8,3}(\nu_d(\mathbb{P}^4))$ belongs to the Grassmann secant variety $\sigma_{8,3}(\nu_d(\mathbb{P}^4))$.

The low-lying structure of semi-magic $^{118}$Sn has been investigated through the $\beta$-decay of $^{118}$In ($T_{1/2}=4.45$ min) to study shape coexistence via the reduced transition probabilities of states in the 2p-2h proton intruder band. This high-statistics study was carried out at TRIUMF-ISAC with the GRIFFIN spectrometer. In total, 99 transitions have been placed in the level scheme with 43 being newly observed. Three low-lying $\gamma$-ray transitions with energies near 285 keV have been resolved from which the 2$^+_{\mathrm{intr.}} \rightarrow 0^+_{\mathrm{intr.}}$ 284.52-keV transition was determined to have half of the previous branching fraction leading to a $B(E2;2^+_2\rightarrow 0^+_2)$ of 21(4) W.u. compared to 39(7) W.u. from the previous measurement. Calculations using $sd$ IBM-2 with mixing have also been made to compare the experimental $B(E2)$ values to the theoretical values and to make comparisons to the $^{114,116}$Sn isotopes previously studied using the same theoretical model.

We study the non-equilibrium response of a 4x3 Hubbard model at U=8 under the influence of a short electric field pulse, with the main focus on multiple photon excitations and on the change of double occupancy after the pulse. The behavior mainly depends on the driving frequency of the electric field. The largest change of double occupancy occurs during the pulse. For frequencies below the Mott gap, we observe multiphoton excitations at large field intensities. For frequencies beyond the gap energy, there is a region where Auger recombination reduces the double occupancy after the pulse. Impact ionization (Multi Exciton Generation), namely a growing double occupancy after the pulse, occurs for frequencies larger than twice the Mott gap. From the Loschmidt amplitude we compute the eigenstate spectrum of the quantum state after the pulse, observing multiple distinct photon excitation peaks, in line with expectations from a quasiparticle picture. We introduce a technique with which we analyze the time evolution of double occupancy in each peak individually. The long-term behavior of the double occupancy almost only depends on the absorbed energy, and we explore the connection of this property to the Eigenstate Thermalization Hypothesis.

Distance-based Quantum Classifier (DBQC) is a quantum machine learning model for pattern recognition. However, DBQC has a low accuracy on real noisy quantum processors. We present a modification of DBQC named Quantum One-class Classifier (QOCC) to improve accuracy on NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a computer provided by IBM Quantum Experience and show that QOCC has improved accuracy over DBQC.

A scientific biography of John Ward, who was responsible for the Ward Identity in quantum electrodynamics; the first detailed calculation of the quantum entanglement of two photons in electron-positron annihilation with Maurice Pryce; the prediction of neutral weak currents in electroweak theory with Shelly Glashow and Abdus Salam, and many other major calculations in theoretical physics.

The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video based vehicle counting system. In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles' in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models' addresses challenges associated to different weather conditions, occlusion and low-light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 hours' traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, Detectron2 and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.

We perform resolvent analysis to examine the perturbation dynamics over the laminar separation bubble (LSB) that forms near the leading edge of a NACA 0012 airfoil at a chord-based Reynolds number of 500,000 and an angle of attack of 8 degrees. Randomized SVD is adopted in the present analysis to relieve the computational cost associated with the high-Re global base flow. To examine the local physics over the LSB, we consider the use of exponential discounting to limit the time horizon that allows for the instability to develop with respect to the base flow. With discounting, the gain distribution over frequency accurately captures the spectral content over the LSB obtained from flow simulation. The peak-gain frequency also agrees with previous flow control results on suppressing dynamic stall over a pitching airfoil. According to the gain distribution and the modal structures, we conclude that the dominant energy-amplification mechanism is the Kelvin-Helmholtz instability. In addition to discounting, we also examine the use of spatial windows for both the forcing and response. From the response-windowed analysis, we find that the LSB serves the main role of energy amplifier, with the amplification saturating at the reattachment point. The input window imposes the constraint of surface forcing, and the results show that the optimal actuator location is slightly upstream of the separation point. The surface-forcing mode also suggest the optimal momentum forcing in the surface-tangent direction, with strong uni-directionality that is ideal for synthetic-jet-type actuators. This study demonstrates the strength of randomized resolvent analysis in tackling high-Reynolds-number base flows and calls attention to the care needed for base-flow instabilities.

Neural speaker embeddings trained using classification objectives have demonstrated state-of-the-art performance in multiple applications. Typically, such embeddings are trained on an out-of-domain corpus on a single task e.g., speaker classification, albeit with a large number of classes (speakers). In this work, we reformulate embedding training under the meta-learning paradigm. We redistribute the training corpus as an ensemble of multiple related speaker classification tasks, and learn a representation that generalizes better to unseen speakers. First, we develop an open source toolkit to train x-vectors that is matched in performance with pre-trained Kaldi models for speaker diarization and speaker verification applications. We find that different bottleneck layers in the architecture variedly favor different applications. Next, we use two meta-learning strategies, namely prototypical networks and relation networks, to improve over the x-vector embeddings. Our best performing model achieves a relative improvement of 12.37% and 7.11% in speaker error on the DIHARD II development corpus and the AMI meeting corpus, respectively. We analyze improvements across different domains in the DIHARD corpus. Notably, on the challenging child speech domain, we study the relation between child age and the diarization performance. Further, we show reductions in equal error rate for speaker verification on the SITW corpus (7.68%) and the VOiCES challenge corpus (8.78%). We observe that meta-learning particularly offers benefits in challenging acoustic conditions and recording setups encountered in these corpora. Our experiments illustrate the applicability of meta-learning as a generalized learning paradigm for training deep neural speaker embeddings.

Palm vein identification (PVI) is a modern biometric security technique used for increasing security and authentication systems. The key characteristics of palm vein patterns include, its uniqueness to each individual, unforgettable, non-intrusive and cannot be taken by an unauthorized person. However, the extracted features from the palm vein pattern are huge with high redundancy. In this paper, we propose a combine model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) to enhance prediction of vein palm patterns. The 2D-DWT Extracts features from palm vein images, PCA reduces the redundancy in palm vein features. The system has been trained in selecting high reverent features based on the wrapper model. The PSO feeds wrapper model by an optimal subset of features. The proposed system uses four classifiers as an objective function to determine VPI which include Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT) and Naïve Bayes (NB). The empirical result shows the proposed system Iit satisfied best results with SVM. The proposed 2D-DWTPP model has been evaluated and the results shown remarkable efficiency in comparison with Alexnet and classifier without feature selection. Experimentally, our model has better accuracy reflected by (98.65) while Alexnet has (63.5) and applied classifier without feature selection has (78.79).

The liquid xenon (LXe) time projection chamber (TPC) technology is leading the dark matter direct detection in a wide range of dark matter masses from sub-GeV to a few TeV. To further improve its sensitivity to sub-GeV dark matter and its application in reactor neutrino monitoring via coherent elastic neutrino-nucleus scattering (CEvNS), more understanding and suppression of single/few electrons background rate are needed. Here we report on the design and performance of a sealed LXeTPC with a graphene-coated fused silica window as the cathode. The purpose of the sealed TPC is for isolating the liquid xenon target volume from the majority of outgassing materials in the detector vessel, thus improving the liquid xenon purification efficiency and reducing the impurity-induced single/few electrons background. We investigated the out-gassing rate and purification efficiency using the data from the sealed TPC with a simple purification model. The single electron signals from the photoionization of impurities in LXe are obtained and their correlation with the LXe purity is investigated. The photo-electron emission rate on the graphene-coated electrode is compared to that from stainless steel, the electrode material typically used in LXe detectors. We discuss the possible further improvement and potential applications of the sealed TPC for the next generation liquid xenon experiments for dark matter and neutrino physics.

Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input. Evaluating simultaneous translation models is more complex than offline models because the latency is another factor to consider in addition to translation quality. The research community, despite its growing focus on novel modeling approaches to simultaneous translation, currently lacks a universal evaluation procedure. Therefore, we present SimulEval, an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation. A server-client scheme is introduced to create a simultaneous translation scenario, where the server sends source input and receives predictions for evaluation and the client executes customized policies. Given a policy, it automatically performs simultaneous decoding and collectively reports several popular latency metrics. We also adapt latency metrics from text simultaneous translation to the speech task. Additionally, SimulEval is equipped with a visualization interface to provide better understanding of the simultaneous decoding process of a system. SimulEval has already been extensively used for the IWSLT 2020 shared task on simultaneous speech translation. Code will be released upon publication.

We study partitioning to parallelize multiplication of one or more dense vectors by a sparse matrix (SpMV or SpMM). We consider contiguous partitions, where the rows (or columns) of the matrix are split into $K$ parts without reordering. We present exact and approximate contiguous partitioning algorithms that minimize the runtime of the longest-running processor under cost models that combine work factors and hypergraph communication factors. This differs from traditional graph or hypergraph partitioning models which minimize total communication under a work balance constraint. We address regimes where partitions of the row space and column space are expected to match (the symmetric case) or are allowed to differ (the nonsymmetric case). Our algorithms use linear space. Our exact algorithm runs in linear time when $K^2$ is sublinear. Our $(1 + \epsilon)$-approximate algorithm runs in linear time when $K\log(1/\epsilon)$ is sublinear. We combine concepts from high-performance computing and computational geometry. Existing load balancing algorithms optimize a linear model of per-processor work. We make minor adaptations to optimize arbitrary nonuniform monotonic increasing or decreasing cost functions which may be expensive to evaluate. We then show that evaluating our model of communication is equivalent to planar dominance counting. We specialize Chazelle's dominance counting algorithm to points in the bounded integer plane and generalize it to trade reduced construction time for increased query time, since our partitioners make very few queries. Our algorithms split the original row (or column) ordering into parts to optimize diverse cost models. Combined with reordering or embedding techniques, our algorithms might be used to build more general heuristic partitioners, as they can optimally round one-dimensional embeddings of direct $K$-way noncontiguous partitioning problems.

We consider strategies for using new datasets to probe scenarios in which light right-handed SM fermions couple to a new gauge group, $U(1)_{T3R}$. This scenario provides a natural explanation for the light flavor sector scale, and a motivation for sub-GeV dark matter. There is parameter space which is currently allowed, but we find that much of it can be probed with future experiments. In particular, cosmological and astrophysical observations, neutrino experiments and experiments which search for displaced visible decay or invisible decay can all play a role. Still, there is a small region of parameter space which even these upcoming experiments will not be able to probe. This model can explain the observed 2.4-3$\sigma$ excess of events at the COHERENT experiment in the parameter space allowed by current laboratory experiments, but the ongoing/upcoming laboratory experiments will decisively probe this possibility.

We show that a recently derived alternative form of the relativistic three-particle quantization condition for identical particles can be rewritten in terms of the R matrix introduced to give a unitary representation of the infinite-volume three-particle scattering amplitude. Combined with earlier work, this shows the equivalence of the relativistic effective field theory approach of Refs.[1,2] and the "finite-volume unitarity" approach of Refs.[3,4]. It also provides a generalization of the latter approach to arbitrary angular momenta of two-particle subsystems.

Within months of birth, children have meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms applied to sensory data, and how much of it requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is currently infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to improvements in data collecting technology and recent progress in deep learning. In this paper, our goal is to achieve such progress by utilizing modern self-supervised deep learning methods and a recent longitudinal, egocentric video dataset recorded from the perspective of several young children (Sullivan et al., 2020). Our results demonstrate the emergence of powerful, high-level visual representations from developmentally realistic natural videos using generic self-supervised learning objectives.

We present a simplified derivation of the relativistic three-particle quantization condition for identical, spinless particles described by a generic relativistic field theory satisfying a $\mathbb Z_2$ symmetry. The simplification is afforded by using a three-particle quasilocal K matrix that is not fully symmetrized, $\widetilde{\mathcal{K}}_{\rm df,3}^{(u,u)}$, and makes extensive use of time-ordered perturbation theory (TOPT). We obtain a new form of the quantization condition. This new form can then be related algebraically to the standard quantization condition, which depends on a fully symmetric three-particle K matrix, $\mathcal{K}_{\rm df,3}$. The new derivation is fully explicit, allowing, for example, a closed-form expression for $\mathcal{K}_{\rm df,3}$ to be given in terms of TOPT amplitudes. The new form of the quantization condition is similar in structure to that obtained in the "finite-volume unitarity" approach, and in a companion paper we make this connection concrete. Our simplified approach should also allow a more straightforward generalization of the quantization condition to nondegenerate particles, and perhaps also to more than three particles.

Current state-of-the-art results in Music Information Retrieval are largely dominated by deep learning approaches. These provide unprecedented accuracy across all tasks. However, the consistently overlooked downside of these models is their stunningly massive complexity, which seems concomitantly crucial to their success. In this paper, we address this issue by proposing a model pruning method based on the lottery ticket hypothesis. We modify the original approach to allow for explicitly removing parameters, through structured trimming of entire units, instead of simply masking individual weights. This leads to models which are effectively lighter in terms of size, memory and number of operations. We show that our proposal can remove up to 90% of the model parameters without loss of accuracy, leading to ultra-light deep MIR models. We confirm the surprising result that, at smaller compression ratios (removing up to 85% of a network), lighter models consistently outperform their heavier counterparts. We exhibit these results on a large array of MIR tasks including audio classification, pitch recognition, chord extraction, drum transcription and onset estimation. The resulting ultra-light deep learning models for MIR can run on CPU, and can even fit on embedded devices with minimal degradation of accuracy.

We apply the classical double copy procedure to a class of regular, non-singular black hole solutions. We give several examples, paying particular attention to a string-theory-corrected black hole solution emerging from T-duality. Non-perturbative stringy corrections introduce an ultraviolet (UV) zero-point length cutoff which results in non-singular black hole spacetimes. Apart from the UV regulator, the solution is equivalent to the Bardeen black hole spacetime. We extend this solution to include an asymptotic de Sitter background. All Yang-Mills field theory quantities associated with the double copy are well-behaved and finite for all values of parameters. We present a thorough analysis of the black hole horizon structure, additionally uncovering a simple yet new connection between horizons on the gravity side and electric fields on the gauge theory side of the double copy.

We study the performance of efficient quantum state tomography methods based on neural network quantum states using measured data from a two-photon experiment. Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e. to positive semi-definite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.

We discuss the phase dependent nonlocal thermoelectric effect in a topological Josephson junction in contact with a normal-metal probe. We show that, due to the helical nature of topological edge states, nonlocal thermoelectricity is generated by a purely Andreev interferometric mechanism. This response can be tuned by imposing a Josephson phase difference, through the application of a dissipationless current between the two superconductors, even without the need of applying an external magnetic field. We discuss in detail the origin of this effect and we provide also a realistic estimation of the nonlocal Seebeck coefficient that results of the order of few $\mu V/K$.

We consider a supercritical branching process and define a contact tracing mechanism on its genealogical tree. We calculate the growth rate of the post tracing process, and give conditions under which the tracing is strong enough to drive the process to extinction.

Let $\mathcal{H}$ be a reproducing kernel Hilbert space of functions on a set $X$. We study the problem of finding a minimal geodesic of the Grassmann manifold of $\mathcal{H}$ that joins two subspaces consisting of functions which vanish on given finite subsets of $X$. We establish a necessary and sufficient condition for existence and uniqueness of geodesics, and we then analyze it in examples. We discuss the relation of the geodesic distance with other known metrics when the mentioned finite subsets are singletons. We find estimates on the upper and lower eigenvalues of the unique self-adjoint operators which define the minimal geodesics, which can be made more precise when the underlying space is the Hardy space. Also for the Hardy space we discuss the existence of geodesics joining subspaces of functions vanishing on infinite subsets of the disk, and we investigate when the product of projections onto this type of subspaces is compact.

We present a characterisation of elementary fibrations, also known as fibrations with equality, that generalises the one for faithful fibrations, and employ it for a comparison with the structures used in the semantics of the identity type of Martin-Löf type theory.

3D integration technologies are seeing widespread adoption in the semiconductor industry to offset the limitations and slowdown of two-dimensional scaling. High-density 3D integration techniques such as face-to-face wafer bonding with sub-10 $\mu$m pitch can enable new ways of designing SoCs using all 3 dimensions, like folding a microprocessor design across multiple 3D tiers. However, overlapping thermal hotspots can be a challenge in such 3D stacked designs due to a general increase in power density. In this work, we perform a thorough thermal simulation study on sign-off quality physical design implementation of a state-of-the-art, high-performance, out-of-order microprocessor on a 7nm process technology. The physical design of the microprocessor is partitioned and implemented in a 2-tier, 3D stacked configuration with logic blocks and memory instances in separate tiers (logic-over-memory 3D). The thermal simulation model was calibrated to temperature measurement data from a high-performance, CPU-based 2D SoC chip fabricated on the same 7nm process technology. Thermal profiles of different 3D configurations under various workload conditions are simulated and compared. We find that stacking microprocessor designs in 3D without considering thermal implications can result in maximum die temperature up to 12\degC higher than their 2D counterparts under the worst-case power-indicative workload. This increase in temperature would reduce the amount of time for which a power-intensive workload can be run before throttling is required. However, logic-over-memory partitioned 3D CPU implementation can mitigate this temperature increase by half, which makes the temperature of the 3D design only 6$^\circ$C higher than the 2D baseline. We conclude that using thermal aware design partitioning and improved cooling techniques can overcome the thermal challenges associated with 3D stacking.

This article is concerned with stochastic differential equations driven by a $d$ dimensional fractional Brownian motion with Hurst parameter $H>1/4$, understood in the rough paths sense. Whenever the coefficients of the equation satisfy a uniform ellipticity condition, we establish a sharp local estimate on the associated control distance function and a sharp local lower estimate on the density of the solution.

In this study, we investigated the inhibition of SARS-CoV-2 spike glycoprotein with HIV drugs and their combinations. This glycoprotein is essential for the reproduction of the SARS-COV-2 virus, so its inhibition opens new avenues for the treatment of patients with COVID-19 disease. In doing so, we used the VINI in silico model of cancer, whose high accuracy in finding effective drugs and their combinations was confirmed in vitro by comparison with existing results from NCI-60 bases, and in vivo by comparison with existing clinical trial results. In the first step, the VINI model calculated the inhibition efficiency of SARS-CoV-2 spike glycoprotein with 44 FDA-approved antiviral drugs. Of these drugs, HIV drugs have been shown to be effective, while others mainly have shown weak or no efficiency. Subsequently, the VINI model calculated the inhibition efficiency of all possible double and triple HIV drug combinations, and among them identified ten with the highest inhibition efficiency. These ten combinations were analyzed by Medscape drug-drug interaction software and LexiComp Drug Interactions. All combinations except the combination of cobicistat_abacavir_rilpivirine appear to have serious interactions (risk rating category D) when dosage adjustments/reductions are required for possible toxicity. Finally, the VINI model compared the inhibition efficiency of cobicistat_abacivir_rilpivirine combination with cocktails and individual drugs already used or planned to be tested against SARS-CoV-2. Combination cobicistat_abacivir_rilpivirine demonstrated the highest inhibition of SARS-CoV-2 spike glycoprotein over others. Thus, this combination seems to be a promising candidate for the further in vitro testing and clinical trials.

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GPUs are increasingly being used in security applications, especially for accelerating encryption/decryption. While GPUs are an attractive platform in terms of performance, the security of these devices raises a number of concerns. One vulnerability is the data-dependent timing information, which can be exploited by adversary to recover the encryption key. Memory system features are frequently exploited since they create detectable timing variations. In this paper, our attack model is a coalescing attack, which leverages a critical GPU microarchitectural feature -- the coalescing unit. As multiple concurrent GPU memory requests can refer to the same cache block, the coalescing unit collapses them into a single memory transaction. The access time of an encryption kernel is dependent on the number of transactions. Correlation between a guessed key value and the associated timing samples can be exploited to recover the secret key. In this paper, a series of hardware/software countermeasures are proposed to obfuscate the memory timing side channel, making the GPU more resilient without impacting performance. Our hardware-based approach attempts to randomize the width of the coalescing unit to lower the signal-to-noise ratio. We present a hierarchical Miss Status Holding Register (MSHR) design that can merge transactions across different warps. This feature boosts performance, while, at the same time, secures the execution. We also present a software-based approach to permute the organization of critical data structures, significantly changing the coalescing behavior and introducing a high degree of randomness. Equipped with our new protections, the effort to launch a successful attack is increased up to 1433X . 178X, while also improving encryption/decryption performance up to 7%.

Nearly all known white dwarf planetary systems contain detectable rocky debris in the stellar photosphere. A glaring exception is the young and still evolving white dwarf WD J0914+1914, which instead harbours a giant planet and a disc of pure gas. The stability boundaries of this disc and the future prospects for this white dwarf to be polluted with rocks depend upon the mass and orbit of the planet, which are only weakly constrained. Here we combine an ensemble of plausible planet orbits and masses to determine where observers should currently expect to find the outer boundary of the gas disc. We do so by performing a sweep of the entire plausible phase space with short-term numerical integrations. We also demonstrate that particle-star collisional trajectories, which would lead to the (unseen) signature of rocky metal pollution, occupy only a small fraction of the phase space, mostly limited to particle eccentricities above 0.75. Our analysis reveals that a highly inflated planet on a near-circular orbit is the type of planet which is most consistent with the current observations.

Graph-based recommender systems (GRSs) analyze the structural information in the graphical representation of data to make better recommendations, especially when the direct user-item relation data is sparse. Ranking-oriented GRSs that form a major class of recommendation systems, mostly use the graphical representation of preference (or rank) data for measuring node similarities, from which they can infer a recommendation list using a neighborhood-based mechanism. In this paper, we propose PGRec, a novel graph-based ranking-oriented recommendation framework. PGRec models the preferences of the users over items, by a novel graph structure called PrefGraph. This graph is then exploited by an improved embedding approach, taking advantage of both factorization and deep learning methods, to extract vectors representing users, items, and preferences. The resulting embedding are then used for predicting users' unknown pairwise preferences from which the final recommendation lists are inferred. We have evaluated the performance of the proposed method against the state of the art model-based and neighborhood-based recommendation methods, and our experiments show that PGRec outperforms the baseline algorithms up to 3.2% in terms of NDCG@10 in different MovieLens datasets.

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community---CaringBridge.org---where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships---patterns of repeated interactions---are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.

Reversible debugging is becoming increasingly popular for locating the source of errors. This technique proposes a more natural approach to debugging, where one can explore a computation from the observable misbehaviour backwards to the source of the error. In this work, we propose a reversible debugging scheme for logic programs. For this purpose, we define an appropriate instrumented semantics (a so-called Landauer embedding) that makes SLD resolution reversible. An implementation of a reversible debugger for Prolog, rever, has been developed and is publicly available.