Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximating the Log-Partition Function

Variational approximation, such as mean-ﬁeld (MF) and tree-reweighted (TRW), provide a computationally efﬁcient approximation of the log-partition function for a generic graphical model. TRW provably provides an upper bound, but the approximation ratio is generally not quantiﬁed. As the primary contribution of this work, we provide an approach to quantify the approximation ratio through the property of the underlying graph structure. Speciﬁcally, we argue that (a variant of) TRW produces an estimate that is within factor 1 √ κ ( G ) of the true log-partition function for any discrete pairwise graphical model over graph G , where κ ( G ) ∈ (0 , 1] captures how far G is from tree structure with κ ( G ) = 1 for trees and 2 /N for the complete graph over N vertices. As a consequence, the approximation ratio is 1 for trees, (cid:112) ( d + 1) / 2 for any graph with maximum average degree d , and β →∞ ≈ 1+1 / (2 β ) for graphs with girth (shortest cycle) at least β log N . In general, κ ( G ) is the solution of a max-min problem associated with G that can be evaluated in polynomial time for any graph. Using samples from the uniform distribution over the spanning trees of G, we provide a near linear-time variant that achieves an approximation ratio equal to the inverse of square-root of minimal (across edges) effective resistance of the graph. We connect our results to the graph partition-based approximation method and thus provide a uniﬁed perspective.
Linear Classifiers in Mixed Constant Curvature Spaces

Embedding methods for mixed-curvature spaces are powerful techniques for low-distortion and low-dimensional representation of complex data structures. Nevertheless, little is known regarding downstream learning and optimization in the embedding space. Here, we address for the first time the problem of linear classification in a product space form -- a mix of Euclidean, spherical, and hyperbolic spaces with different dimensions. First, we revisit the definition of a linear classifier on a Riemannian manifold by using geodesics and Riemannian metrics which generalize the notions of straight lines and inner products in vector spaces, respectively. Second, we prove that linear classifiers in $d$-dimensional constant curvature spaces can shatter exactly $d+1$ points: Hence, Euclidean, hyperbolic and spherical classifiers have the same expressive power. Third, we formalize linear classifiers in product space forms, describe a novel perceptron classification algorithm, and establish rigorous convergence results. We support our theoretical findings with simulation results on several datasets, including synthetic data, MNIST and Omniglot. Our results reveal that learning methods applied to small-dimensional embeddings in product space forms significantly outperform their algorithmic counterparts in Euclidean spaces.
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM

Automatically detecting violence from surveillance footage is a subset of activity recognition that deserves special attention because of its wide applicability in unmanned security monitoring systems, internet video filtration, etc. In this work, we propose an efficient two-stream deep learning architecture leveraging Separable Convolutional LSTM (SepConvLSTM) and pre-trained MobileNet where one stream takes in background suppressed frames as inputs and other stream processes difference of adjacent frames. We employed simple and fast input pre-processing techniques that highlight the moving objects in the frames by suppressing non-moving backgrounds and capture the motion in-between frames. As violent actions are mostly characterized by body movements these inputs help produce discriminative features. SepConvLSTM is constructed by replacing convolution operation at each gate of ConvLSTM with a depthwise separable convolution that enables producing robust long-range Spatio-temporal features while using substantially fewer parameters. We experimented with three fusion methods to combine the output feature maps of the two streams. Evaluation of the proposed methods was done on three standard public datasets. Our model outperforms the accuracy on the larger and more challenging RWF-2000 dataset by more than a 2% margin while matching state-of-the-art results on the smaller datasets. Our experiments lead us to conclude, the proposed models are superior in terms of both computational efficiency and detection accuracy.
Progressive Depth Learning for Single Image Dehazing

The formulation of the hazy image is mainly dominated by the reflected lights and ambient airlight. Existing dehazing methods often ignore the depth cues and fail in distant areas where heavier haze disturbs the visibility. However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase. In turn, the good transmission estimation could facilitate the depth estimation for hazy images. In this paper, a deep end-to-end model that iteratively estimates image depths and transmission maps is proposed to perform an effective depth prediction for hazy images and improve the dehazing performance with the guidance of depth information. The image depth and transmission map are progressively refined to better restore the dehazed image. Our approach benefits from explicitly modeling the inner relationship of image depth and transmission map, which is especially effective for distant hazy areas. Extensive results on the benchmarks demonstrate that our proposed network performs favorably against the state-of-the-art dehazing methods in terms of depth estimation and haze removal.
Cascades and reconnection in interacting vortex filaments

Rodolfo Ostilla-Mónico,1 Ryan McKeown,2 Michael P. Brenner,2 Shmuel M. Rubinstein,2 and Alain Pumir3, 4 Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS, Laboratoire de Physique, 69342 Lyon, France Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany (Dated: February 23, 2021)
Twisted BRST symmetry in gauge theories on $\kappa$-Minkowski

Algebraic properties of the BRST symmetry associated to the twisted gauge symmetry occurring in the κ-Poincaré invariant gauge theories on the κ-Minkowski space are investigated. We find that the BRST operation associated to the gauge invariance of the action functional can be continuously deformed together with its corresponding Leibniz rule, into a nilpotent twisted BRST operation, leading to a twisted BRST symmetry algebra which may be viewed as a noncommutative analog of the usual Yang-Mills BRST algebra. 1 ar X iv :2 10 2. 10 86 0v 1 [ he pth ] 2 2 Fe b 20 21
Modelling of heat transfer in internal combustion engines with variable density effect

Heat transfer is one of the major factors affecting the performance, efficiency, and emissions of internal combustion engines. As convection heat transfer is dominant in engine heat transfer, accurate modelling of the boundary layer heat transfer is required. In engine computational fluid dynamics (CFD) simulations, the wall function approach has been widely used to model the near-wall flow and temperature field. The present paper suggests a modified wall function approach to model heat transfer in internal combustion engines. Special emphasis has been placed on introducing the effect of variable density and variable viscosity in the model formulation. A non-dimensional temperature corrector is suggested to incorporate the variable density effect on the wall function approach. The suggested model is applied in KIVA-3V and is validated against experimental data of a homogeneous charge compression-ignition engine, showing improved predictions for pressure and emissions compared with the standard wall function model.
Nested sampling with normalizing flows for gravitational-wave inference

We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler's settings.
Synthesizing Irreproducibility in Deep Networks

The success and superior performance of deep networks is spreading their popularity and use to an increasing number of applications. Very recent works, however, demonstrate that modern day deep networks suffer from irreproducibility (also referred to as nondeterminism or underspecification). Two or more models that are identical in architecture, structure, training hyper-parameters, and parameters, and that are trained on exactly the same training data, yield different predictions on individual previously unseen examples. Thus, a model that performs well on controlled test data, may perform in unexpected ways when deployed in the real world, whose data is expected to be similar to the test data. We study simple synthetic models and data to understand the origins of these problems. We show that even with a single nonlinearity and for very simple data and models, irreproducibility occurs. Our study demonstrates the effects of randomness in initialization, training data shuffling window size, and activation functions on prediction irreproducibility, even under very controlled synthetic data. While, as one would expect, randomness in initialization and in shuffling the training examples exacerbates the phenomenon, we show that model complexity and the choice of nonlinearity also play significant roles in making deep models irreproducible.
Cognitive Reserve and Digital Confidence among Older Adults as New Paradigm for Resilient Aging

Objective The purpose of this study was to examine the digital confidence of elder adults and identify behavioral patterns for technology that are related to cognitive abilities among elders. Method An observational study was conducted using a sample of 94 elders, aged 53 to 86 years. Neuropsychological and emotional measures were used, and technology use was assessed. Results Finding showed elders by resilient neuropsychological aspects can have a good affinity for technology. We examined the role of not only cognitive reserve levels but also demographic characteristics (i.e., age, educational level) and found that elderly were more adherent to digital resources. Technology can be a beneficial resource to those with medium levels of cognitive reserve and make them feel “like they are leading an active lifestyle. ” Conclusions The focal point of our findings is the relevance of cognitive reserve during older adulthood as a key factor that should be examined in investigations on successful aging; it would be more interesting to examine these factors within the context of analyses on the impact of technology on aging and digital living.
STDP enhances learning by backpropagation in a spiking neural network

A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments show that the proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. This feature has not been achieved by existing semi-supervised learning methods of discriminative models. It is possible to implement the proposed learning method for event-driven systems. Hence, it would be highly e ﬃ cient in real-time problems if it were implemented on neuromorphic hardware. The results suggest that STDP plays an important role other than self-organization when applied after supervised learning, which di ﬀ ers from the previous method of using STDP as pre-training interpreted as self-organization.
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints

Optimization problems under affine constraints appear in various areas of machine learning. We consider the task of minimizing a smooth strongly convex function F(x) under the affine constraint Kx=b, with an oracle providing evaluations of the gradient of F and multiplications by K and its transpose. We provide lower bounds on the number of gradient computations and matrix multiplications to achieve a given accuracy. Then we propose an accelerated primal-dual algorithm achieving these lower bounds. Our algorithm is the first optimal algorithm for this class of problems.
Exploring dark sector parameters in light of neutron star temperatures

Abstract Using neutron stars (NS) as a dark matter (DM) probe has gained broad attention recently, either from heating due to DM annihilation or its stability under the presence of DM. In this work, we investigate spin-1/2 fermionic DM χ charged under the U(1)X in the dark sector. The massive gauge boson V of U(1)X gauge group can be produced in NS via DM annihilation. The produced gauge boson can decay into Standard Model (SM) particles before it exits the NS, despite its tiny couplings to SM particles. Thus, we perform a systematic study on χχ̄ → 2V → 4SM as a new heating mechanism for NS in addition to χχ̄ → 2SM and kinetic heating from DMbaryon scattering. The self-trapping due to χV scattering is also considered. We assume the general framework that both kinetic and mass mixing terms between V and SM gauge bosons are present. This allows both vector and axial-vector couplings between V and SM fermions even for mV mZ . Notably, the contribution from axial-vector coupling is not negligible when particles scatter relativistically. We point out that the above approaches to DM-induced NS heating are not yet adopted in recent analyses. Detectabilities of the aforementioned effects to the NS surface temperature by the future telescopes are discussed as well.
Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operating without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can obtain a perfect CSI at no cost. Then, we provide four achievable schemes, where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay–destination link into two parts and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observations to the destination node. Numerical results show that, with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds that come quite close to the upper bound on a wide range of relevant system parameters.
Catastrophic production of slow gravitinos

We study gravitational particle production of the massive spin-$3/2$ Rarita-Schwinger field, and its close relative, the gravitino, in FRW cosmological spacetimes. For masses lighter than the value of the Hubble expansion rate after inflation, $m_{3/2} \lesssim H$, we find catastrophic gravitational particle production, wherein the number of gravitationally produced particles is divergent, caused by a transient vanishing of the helicity-1/2 gravitino sound speed. In contrast with the conventional gravitino problem, the spectrum of produced particles is dominated by those with momentum at the UV cutoff. This suggests a breakdown of effective field theory, which might be cured by new degrees of freedom that emerge in the UV. We study the UV completion of the Rarita-Schwinger field, namely ${\cal N}=1$, $d=4$, supergravity. We reproduce known results for models with a single superfield and models with an arbitrary number of chiral superfields, find a simple geometric expression for the sound speed in the latter case, and extend this to include nilpotent constrained superfields and orthogonal constrained superfields. We find supergravity models where the catastrophe is cured and models where it persists. Insofar as quantizing the gravitino is tantamount to quantizing gravity, as is the case in any UV completion of supergravity, the models exhibiting catastrophic production are prime examples of 4-dimensional effective field theories that become inconsistent when gravity is quantized, suggesting a possible link to the Swampland program. We propose the Gravitino Swampland Conjecture, which is consistent with and indeed follows from the KKLT and Large Volume scenarios for moduli stabilization in string theory.
Study of phonon transport across several Si/Ge interfaces using full-band phonon Monte Carlo simulation

A Full Band Monte Carlo simulator has been developed to consider phonon transmission across interfaces disposed perpendicularly to the heat flux. This solver of the Boltzmann transport equation does not require any assumption on the shape the phonon distribution and can naturally consider all phonon transport regimes from the diffusive to the fully ballistic regime. This simulator is used to study single and double Si/Ge heterostructures from the micrometer scale down to the nanometer scale, i.e. in all phonon transport regime from fully diffusive to ballistic. A methodology to determine the thermal conductivity at thermal interfaces is presented. It is also shown that the different transport regimes are correlated to different spectral contributions of the phonon modes to the heat flux along the devices. This local indicator of the transport regime gives new insights into the out-of-equilibrium phonon transport near the interfaces.
Towards Teachable Conversational Agents

The traditional process of building interactive machine learning systems can be viewed as a teacher-learner interaction scenario where the machine-learners are trained by one or more human-teachers. In this work, we explore the idea of using a conversational interface to investigate the interaction between human-teachers and interactive machine-learners. Specifically, we examine whether teachable AI agents can reliably learn from human-teachers through conversational interactions, and how this learning compare with traditional supervised learning algorithms. Results validate the concept of teachable conversational agents and highlight the factors relevant for the development of machine learning systems that intend to learn from conversational interactions.
ALTO: adaptive linearized storage of sparse tensors

The analysis of high-dimensional sparse data is becoming increasingly popular in many important domains. However, real-world sparse tensors are challenging to process due to their irregular shapes and data distributions. We propose the Adaptive Linearized Tensor Order (ALTO) format, a novel mode-agnostic (general) representation that keeps neighboring nonzero elements in the multi-dimensional space close to each other in memory. To generate the indexing metadata, ALTO uses an adaptive bit encoding scheme that trades off index computations for lower memory usage and more effective use of memory bandwidth. Moreover, by decoupling its sparse representation from the irregular spatial distribution of nonzero elements, ALTO eliminates the workload imbalance and greatly reduces the synchronization overhead of tensor computations. As a result, the parallel performance of ALTO-based tensor operations becomes a function of their inherent data reuse. On a gamut of tensor datasets, ALTO outperforms an oracle that selects the best state-of-the-art format for each dataset, when used in key tensor decomposition operations. Specifically, ALTO achieves a geometric mean speedup of 8x over the best mode-agnostic (coordinate and hierarchical coordinate) formats, while delivering a geometric mean compression ratio of 4.x relative to the best mode-specific (compressed sparse fiber) formats.
Tame the Wild with Byzantine Linearizability: Reliable Broadcast, Snapshots, and Asset Transfer

We formalize Byzantine linearizability, a correctness condition that specifies whether a concurrent object with a sequential specification is resilient against Byzantine failures. Using this definition, we systematically study Byzantine-tolerant emulations of various objects from registers. We focus on three useful objects -- reliable broadcast, atomic snapshot, and asset transfer. We prove that there is an $f$-resilient implementation of such objects from registers with $n$ processes $f<\frac{n}{2}$.
FAST: FPGA-based Subgraph Matching on Massive Graphs

Subgraph matching is a basic operation widely used in many applications. However, due to its NP-hardness and the explosive growth of graph data, it is challenging to compute subgraph matching, especially in large graphs. In this paper, we aim at scaling up subgraph matching on a single machine using FPGAs. Specifically, we propose a CPU-FPGA co-designed framework. On the CPU side, we first develop a novel auxiliary data structure called candidate search tree (CST) which serves as a complete search space of subgraph matching. CST can be partitioned and fully loaded into FPGAs’ on-chip memory. Then, a workload estimation technique is proposed to balance the load between the CPU and FPGA. On the FPGA side, we design and implement the first FPGA-based subgraph matching algorithm, called FAST. To take full advantage of the pipeline mechanism on FPGAs, task parallelism optimization and task generator separation strategy are proposed for FAST, achieving massive parallelism. Moreover, we carefully develop a BRAM-only matching process to fully utilize FPGA’s on-chip memory, which avoids the expensive intermediate data transfer between FPGA’s BRAM and DRAM. Comprehensive experiments show that FAST achieves up to 462.0x and 150.0x speedup compared with the state-of-the-art algorithm DAF and CECI, respectively. In addition, FAST is the only algorithm that can handle the billion-scale graph using one machine in our experiments.