Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU Domain Specialization via Composable On-Package Architecture

As GPUs scale their low-precision matrix math throughput to boost deep learning (DL) performance, they upset the balance between math throughput and memory system capabilities. We demonstrate that a converged GPU design trying to address diverging architectural requirements between FP32 (or larger)-based HPC and FP16 (or smaller)-based DL workloads results in sub-optimal configurations for either of the application domains. We argue that a Composable On-PAckage GPU (COPA-GPU) architecture to provide domain-specialized GPU products is the most practical solution to these diverging requirements. A COPA-GPU leverages multi-chip-module disaggregation to support maximal design reuse, along with memory system specialization per application domain. We show how a COPA-GPU enables DL-specialized products by modular augmentation of the baseline GPU architecture with up to 4× higher off-die bandwidth, 32× larger on-package cache, and 2.3× higher DRAM bandwidth and capacity, while conveniently supporting scaled-down HPC-oriented designs. This work explores the microarchitectural design necessary to enable composable GPUs and evaluates the benefits composability can provide to HPC, DL training, and DL inference. We show that when compared to a converged GPU design, a DL-optimized COPA-GPU featuring a combination of 16× larger cache capacity and 1.6× higher DRAM bandwidth scales per-GPU training and inference performance by 31% and 35%, respectively, and reduces the number of GPU instances by 50% in scale-out training scenarios.
Bias Correction in Deterministic Policy Gradient Using Robust MPC

In this paper, we discuss the deterministic policy gradient using the Actor-Critic methods based on the linear compatible advantage function approximator, where the input spaces are continuous. When the policy is restricted by hard constraints, the exploration may not be Centred or Isotropic (non-CI). As a result, the policy gradient estimation can be biased. We focus on constrained policies based on Model Predictive Control (MPC) schemes and to address the bias issue, we propose an approximate Robust MPC approach accounting for the exploration. The RMPC-based policy ensures that a Centered and Isotropic (CI) exploration is approximately feasible. A posterior projection is used to ensure its exact feasibility, we formally prove that this approach does not bias the gradient estimation.
An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, based on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions. Meanwhile, the second model, referred to as VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, evaluating whether these activation functions work well in different datasets as well as different network architectures.
Learnable Dynamic Temporal Pooling for Time Series Classification

With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines. Recent studies on time series classification show that convolutional neural networks (CNN) achieved the state-of-the-art performance as a single classifier. In this work, pointing out that the global pooling layer that is usually adopted by existing CNN classifiers discards the temporal information of high-level features, we present a dynamic temporal pooling (DTP) technique that reduces the temporal size of hidden representations by aggregating the features at the segment-level. For the partition of a whole series into multiple segments, we utilize dynamic time warping (DTW) to align each time point in a temporal order with the prototypical features of the segments, which can be optimized simultaneously with the network parameters of CNN classifiers. The DTP layer combined with a fully-connected layer helps to extract further discriminative features considering their temporal position within an input time series. Extensive experiments on both univariate and multivariate time series datasets show that our proposed pooling significantly improves the classification performance.
Topological Green function of interacting systems

We construct a Green function, which can identify the topological nature of interacting systems. It is equivalent to the single-particle Green function of effective non-interacting particles, the Bloch Hamiltonian of which is given by the inverse of the full Green function of the original interacting particles at zero frequency. The topological nature of the interacting insulators is originated from the coincidence of the poles and the zeros of the diagonal elements of the constructed Green function. The cross of the zeros in the momentum space closely relates to the topological nature of insulators. As a demonstration, using the zero's cross, we identify the topological phases of magnetic insulators, where both the ionic potential and the spin exchange between conduction electrons and magnetic moments are present together with the spin-orbital coupling. The topological phase identification is consistent with the topological invariant of the magnetic insulators. We also found an antiferromagnetic state with topologically breaking of the spin symmetry, where electrons with one spin orientation are in topological insulating state, while electrons with the opposite spin orientation are in topologically trivial one.
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation

This paper is about action segmentation under weak supervision in training, where the ground truth provides only a set of actions present, but neither their temporal ordering nor when they occur in a training video. We use a Hidden Markov Model (HMM) grounded on a multilayer perceptron (MLP) to label video frames, and thus generate a pseudo-ground truth for the subsequent pseudo-supervised training. In testing, a Monte Carlo sampling of action sets seen in training is used to generate candidate temporal sequences of actions, and select the maximum posterior sequence. Our key contribution is a new anchor-constrained Viterbi algorithm (ACV) for generating the pseudo-ground truth, where anchors are salient action parts estimated for each action from a given ground-truth set. Our evaluation on the tasks of action segmentation and alignment on the benchmark Breakfast, MPII Cooking2, Hollywood Extended datasets demonstrates our superior performance relative to that of prior work.
Automatic Micro-Expression Apex Frame Spotting using Local Binary Pattern from Six Intersection Planes

Facial expressions are one of the most effective ways for non-verbal communications, which can be expressed as the Micro-Expression (ME) in the high-stake situations. The MEs are involuntary, rapid, and, subtle, and they can reveal real human intentions. However, their feature extraction is very challenging due to their low intensity and very short duration. Although Local Binary Pattern from Three Orthogonal Plane (LBP-TOP) feature extractor is useful for the ME analysis, it does not consider essential information. To address this problem, we propose a new feature extractor called Local Binary Pattern from Six Intersection Planes (LBP-SIPl). This method extracts LBP code on six intersection planes, and then it combines them. Results show that the proposed method has superior performance in apex frame spotting automatically in comparison with the relevant methods on the CASME database. Simulation results show that, using the proposed method, the apex frame has been spotted in 43% of subjects in the CASME database, automatically. Also, the mean absolute error of 1.76 is achieved, using our novel proposed method.
A universal state sum

We define a universal state sum construction which specializes to most previously known state sums (Turaev-Viro, Dijkgraaf-Witten, Crane-Yetter, Douglas-Reutter, Witten-ReshetikhinTuraev surgery formula, Brown-Arf). The input data for the state sum is an n-category satisfying various conditions, including finiteness, semisimplicity and n-pivotality. From this n-category one constructs an n+1-dimensional TQFT, and applying the TQFT gluing rules to a handle decomposition of an n+1-manifold produces the state sum.
Probing supermassive stars and massive black hole seeds through gravitational wave inspirals

We propose a novel source of gravitational wave emission: the inspirals of compact fragments inside primordial supermassive stars (SMSs). Such systems are thought to be an essential channel in the as-yet little understood formation of supermassive black holes (SMBHs). One model suggests that high accretion rates of 0.1-1 M /yr attainable in atomically-cooled primordial halos can lead to the formation of a nuclear-burning SMS. This will ultimately undergo collapse through a relativistic instability, leaving a massive BH remnant. Recent simulations suggest that supermassive stars rarely form in isolation, and that companion stars and even black holes formed may be captured/accreted and inspiral to the SMS core due to gas dynamical friction. Here, we explore the GW emission produced from such inspirals, which could probe the formation and evolution of SMS and seeds of the first supermassive black holes. We use a semi-analytic gas-dynamical friction model of the inspirals in the SMS to characterize their properties. We find such sources could potentially be observable by upcoming space-born GW-detectors at their formation redshifts with the benefit of gravitational lensing. Mergers within closely-related quasi-stars may produce a much stronger signal, though disambiguating such events from other high-z events may prove challenging.
Propagation mechanism of surface plasmons coupled with surface-enhanced resonant Raman scattering light through a one-dimensional hotspot along a silver nanowire dimer junction

We investigate the propagation of surface-enhanced resonant Raman scattering (SERRS) light by several micrometers through a one-dimensional hotspot (1D HS) located between a plasmonic nanowire dimer (NWD). The propagation exhibits the properties, e.g. an effective propagation induced by excitation and detection polarization orthogonal to the 1D HS long axis, the propagation profiles composed of bright short and dark long propagations, SERRS spectral shapes independent of localized plasmon (LP) resonance of NWDs, redshifts in the SERRS spectra at the edges of 1D HSs, and considerable NWD-by-NWD variations in the propagation lengths. These properties are well reproduced by numerical calculations based on electromagnetism. These calculations reveals the following propagation mechanism: excitation
Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Veriﬁcation System

In this work we study some of the challenges associated with scaling speaker recognition systems to multiple languages. To the best of our knowledge, this is the ﬁrst study of speaker veriﬁcation systems at the scale of 46 languages. Training models for each of the many languages can be time and energy demanding in addition to costly. Low resource languages present additional difﬁculties. The problem is framed from the perspective of using a smart speaker device with interactions consisting of a wake-up keyword (text-dependent) followed by a speech query (text-independent). We examine the use of a hybrid setup consisting of multilingual text-dependent and text-independent components. Experimental evidence suggests that training on multiple languages can generalize to unseen varieties while maintaining performance on seen varieties. We also found that it can reduce computational requirements for training models by an order of magnitude. Furthermore, during model inference on English data, we observe that leveraging a triage framework can reduce the number of calls to the more computationally expensive text-independent system by 73% (and reduce latency by 60%) while maintaining an EER no worse than the text-independent setup.
AMP

Synthesizing graceful and life-like behaviors for physically simulated characters has been a fundamental challenge in computer animation. Data-driven methods that leverage motion tracking are a prominent class of techniques for producing high fidelity motions for a wide range of behaviors. However, the effectiveness of these tracking-based methods often hinges on carefully designed objective functions, and when applied to large and diverse motion datasets, these methods require significant additional machinery to select the appropriate motion for the character to track in a given scenario. In this work, we propose to obviate the need to manually design imitation objectives and mechanisms for motion selection by utilizing a fully automated approach based on adversarial imitation learning. High-level task objectives that the character should perform can be specified by relatively simple reward functions, while the low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips, without any explicit clip selection or sequencing. For example, a character traversing an obstacle course might utilize a task-reward that only considers forward progress, while the dataset contains clips of relevant behaviors such as running, jumping, and rolling. These motion clips are used to train an adversarial motion prior, which specifies style-rewards for training the character through reinforcement learning (RL). The adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from the dataset. Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips. Composition of disparate skills emerges automatically from the motion prior, without requiring a high-level motion planner or other task-specific annotations of the motion clips. We demonstrate the effectiveness of our framework on a diverse cast of complex simulated characters and a challenging suite of motor control tasks.
A large-scale study on human-cloned changes for automated program repair

Research in automatic program repair has shown that real bugs can be automatically fixed. However, there are several challenges involved in such a task that are not yet fully addressed. As an example, consider that a test-suite-based repair tool performs a change in a program to fix a bug spotted by a failing test case, but then the same or another test case fails. This could mean that the change is a partial fix for the bug or that another bug was manifested. However, the repair tool discards the change and possibly performs other repair attempts. One might wonder if the applied change should be also applied in other locations in the program so that the bug is fully fixed. In this paper, we are interested in investigating the extent of bug fix changes being cloned by developers within patches. Our goal is to investigate the need of multi-location repair by using identical or similar changes in identical or similar contexts. To do so, we analyzed 3,049 multi-hunk patches from the ManySStuBs4J dataset, which is a large dataset of single statement bug fix changes. We found out that 68 % of the multi-hunk patches contain at least one change clone group. Moreover, most of these patches (70 %) are strictly-cloned ones, which are patches fully composed of changes belonging to one single change clone group. Finally, most of the strictly-cloned patches (89 %) contain change clones with identical changes, independently of their contexts. We conclude that automated solutions for creating patches composed of identical or similar changes can be useful for fixing bugs.
An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information

In this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguish instances with high semantic similarity. To alleviate the bias, we propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples. Furthermore, to recognize the complex pattern in intra-document samples, we propose a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document. Experimental results show the effectiveness of our approach to alleviate the bias and learn well-aligned multimodal representations.
Inverse properties of a class of pentadiagonal matrices related to higher order difference operators

This paper analyzes the convergence of fixed-point iterations of the form u = f(u) and the properties of the inverse of the related pentadiagonal matrices, associated with the fourth-order nonlinear beam equation. This nonlinear problem is discretized using the finite difference method with the clamped-free and clamped-clamped boundary conditions in the one dimension. Explicit formulas for the inverse of the matrices and norms of the inverse are derived. In iterative process, the direct computation of inverse matrix allows to achieve an efficiency. Numerical results were provided.
Calibratable Hetero-NodeRank for measuring node influence

Node influence metrics have been applied to many applications, including ranking webpages on internet, or locations on spatial networks. PageRank is a popular and effective algorithm for estimating node influence. However, conventional PageRank method considers neither the heterogeneity of network structures nor additional network information, causing a major impedance to performance improvement and an underestimation of non-hub nodes’ importance. As these problems are only partially studied, existing solutions are still not satisfying. This paper addresses the problems by presenting a general PageRank-based model framework, dubbed Hetero-NodeRank, that accounts for heterogeneous network topology and incorporates node attribute information to capture both linkand node-based effects in measuring node influence. Moreover, the framework enables the calibration of the proposed model against empirical data, which transforms the original deductive approach into an inductive one that could be useful for hypothesis testing and causal-effect analysis. As the original unsupervised task becomes a supervised one, optimization methods can be leveraged for model calibrations. Experiments on real data from the national city network of China demonstrated that the proposed model outperforms several widely used algorithms.
The VANDELS Survey: new constraints on the high-mass X-ray binary populations in normal star-forming galaxies at 3 < z < 5.5

We use VANDELS spectroscopic data overlapping with the ≃7 Ms Chandra Deep Field South survey to extend studies of high-mass X-ray binary systems (HMXBs) in 301 normal star-forming galaxies in the redshift range 3 < z < 5.5. Our analysis evaluates correlations between X-ray luminosities (LX), star formation rates (SFRs), and stellar metallicities (Z⋆) to higher redshifts and over a wider range in galaxy properties than hitherto. Using a stacking analysis performed in bins of both redshift and SFR for sources with robust spectroscopic redshifts without AGN signatures, we find convincing evolutionary trends in the ratio LX/SFR to the highest redshifts probed, with a stronger trend for galaxies with lower SFRs. Combining our data with published samples at lower redshift, the evolution of LX/SFR to z ≃ 5 proceeds as (1 + z)1.03 ± 0.02. Using stellar metallicities derived from photospheric absorption features in our spectroscopic data, we confirm indications at lower redshifts that LX/SFR is stronger for metal-poor galaxies. We use semi-analytic models to show that metallicity dependence of LX/SFR alone may not be sufficient to fully explain the observed redshift evolution of X-ray emission from HMXBs, particularly for galaxies with SFR < 30 M⊙ yr−1. We speculate that reduced overall stellar ages and ‘burstier’ star formation histories in the early Universe may lead to higher LX/SFR for the same metallicity. We then define the redshift-dependent contribution of HMXBs to the integrated X-ray luminosity density and, in comparison with models, find that the contribution of HMXBs to the cosmic X-ray background at z > 6 may be ≳0.25 dex higher than previously estimated.
A Facial Feature Discovery Framework for Race Classification Using Deep Learning

Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a raceclassification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on the DCNN. We assessed the performance of the proposed race classification method on four standard face datasets, reporting superior results compared with previous studies.