Miracle-dz/UniFAR-FTUs huggingface dataset:
RevaMp3D: Architecting the Processor Core and Cache Hierarchy for Systems with Monolithically-Integrated Logic and Memory

Recent nano-technological advances enable the Monolithic 3D (M3D) integration of multiple memory and logic layers in a single chip with fine-grained connections. M3D technology leads to significantly higher main memory bandwidth and shorter latency than existing 3D-stacked systems. We show for a variety of workloads on a state-of-the-art M3D system that the performance and energy bottlenecks shift from the main memory to the core and cache hierarchy. Hence, there is a need to revisit current core and cache designs that have been conventionally tailored to tackle the memory bottleneck. Our goal is to redesign the core and cache hierarchy, given the fundamentally new trade-offs of M3D, to benefit a wide range of workloads. To this end, we take two steps. First, we perform a design space exploration of the cache and core's key components. We highlight that in M3D systems, (i) removing the shared last-level cache leads to similar or larger performance benefits than increasing its size or reducing its latency; (ii) improving L1 latency has a large impact on improving performance; (iii) wider pipelines are increasingly beneficial; (iv) the performance impact of branch speculation and pipeline frontend increases; (v) the current synchronization schemes limit parallel speedup. Second, we propose an optimized M3D system, RevaMp3D, where (i) using the tight connectivity between logic layers, we efficiently increase pipeline width, reduce L1 latency, and enable fine-grained synchronization; (ii) using the high-bandwidth and energy-efficient main memory, we alleviate the amplified energy and speculation bottlenecks by memoizing the repetitive fetched, decoded, and reordered instructions and turning off the relevant parts of the core pipeline when possible. RevaMp3D provides, on average, 81% speedup, 35% energy reduction, and 12.3% smaller area compared to the baseline M3D system.
Principled Pruning of Bayesian Neural Networks Through Variational Free Energy Minimization

Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models. Until now, Bayesian model reduction has been applied mainly in the computational neuroscience community on simple models. In this paper, we formulate and apply Bayesian model reduction to perform principled pruning of Bayesian neural networks, based on variational free energy minimization. Direct application of Bayesian model reduction, however, gives rise to approximation errors. Therefore, a novel iterative pruning algorithm is presented to alleviate the problems arising with naive Bayesian model reduction, as supported experimentally on the publicly available UCI datasets for different inference algorithms. This novel parameter pruning scheme solves the shortcomings of current state-of-the-art pruning methods that are used by the signal processing community. The proposed approach has a clear stopping criterion and minimizes the same objective that is used during training. Next to these benefits, our experiments indicate better model performance in comparison to state-of-the-art pruning schemes.
Determinants Influencing Intention to Use Social Commerce for Shopping in developing countries: A Case Study of Oman

– Social media has had a significant impact on our individual lives, including our behavior regarding the purchasing of daily products. This study investigates the factors influencing Omani nationals' intentions to obtain products via social commerce. The researcher surveyed 202 participants and utilized the Technology Acceptance Model to develop the theoretical framework. The data collection was analyzed statistically using an appropriate testing mechanism. Statistical methods, including Cronbach's alpha and multiple linear regression, were utilized for reliability and hypotheses testing. After analyzing the collected data and testing the hypotheses, the findings indicated that perceived usefulness, enjoyment, and ease of use of social commerce affect positively on Omani nationals' intentions to utilize social commerce for shopping. The independent variables had a statistically significant impact on the intention to use social commerce shopping for products; these explain 69.9% of the variation on customers’ intention to utilize social commerce for shopping.
A General Framework for Auditing Differentially Private Machine Learning

We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated low statistical power. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations, combining improved privacy search and verification methods with a toolkit of influence-based poisoning attacks. We demonstrate significantly improved auditing power over previous approaches on a variety of models including logistic regression, Naive Bayes, and random forest. Our method can be used to detect privacy violations due to implementation errors or misuse. When violations are not present, it can aid in understanding the amount of information that can be leaked from a given dataset, algorithm, and privacy specification.
Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)

Approaches for stochastic nonlinear model predictive control (SNMPC) typically make restrictive assumptions about the system dynamics and rely on approximations to characterize the evolution of the underlying uncertainty distributions. For this reason, they are often unable to capture more complex distributions (e.g., non-Gaussian or multi-modal) and cannot provide accurate guarantees of performance. In this letter, we present a sampling-based SNMPC approach that leverages recently derived sample complexity bounds to certify the performance of a feedback policy without making assumptions about the system dynamics or underlying uncertainty distributions. By parallelizing our approach, we are able to demonstrate real-time receding-horizon SNMPC with statistical safety guarantees in simulation and on hardware using a 1/10th scale rally car and a 24-inch wingspan fixed-wing unmanned aerial vehicle (UAV).
Towards relation extraction from speech

Relation extraction typically aims to extract semantic relationships between entities from the unstructured text.One of the most essential data sources for relation extraction is the spoken language, such as interviews and dialogues.However, the error propagation introduced in automatic speech recognition (ASR) has been ignored in relation extraction, and the end-to-end speech-based relation extraction method has been rarely explored.In this paper, we propose a new listening information extraction task, i.e., speech relation extraction.We construct the training dataset for speech relation extraction via text-to-speech systems, and we construct the testing dataset via crowd-sourcing with native English speakers.We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE.We conduct comprehensive experiments to distinguish the challenges in speech relation extraction, which may shed light on future explorations. We share the code and data on https://github.com/wutong8023/SpeechRE.
Cross-layer Attention Network for Fine-grained Visual Categorization

Learning discriminative representations for subtle localized details plays a signiﬁcant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based works, our work does not explicitly deﬁne or localize the part regions of interest; instead, we leverage the complementary properties of different stages of the network, and build a mutual reﬁnement mechanism between the mid-level feature maps and the top-level feature map by our proposed Cross-layer Attention Network (CLAN). Speciﬁ-cally, CLAN is composed of 1) the Cross-layer Context Attention (CLCA) module, which enhances the global context information in the intermediate feature maps with the help of the top-level feature map, thereby improving the expressive power of the middle layers, and 2) the Cross-layer Spatial Attention (CLSA) module, which takes advantage of the local attention in the mid-level feature maps to boost the feature extraction of local regions at the top-level feature maps. Experimental results show our approach achieves state-of-the-art on three publicly available ﬁne-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach. Experimental results show our approach achieves state-of-the-art on three publicly available ﬁne-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft).
Invariant theory of free bicommutative algebras

The variety B of bicommutative algebras consists of all nonassociative algebras satisfying the polynomial identities of right- and left-commutativity ( x 1 x 2 ) x 3 = ( x 1 x 3 ) x 2 and x 1 ( x 2 x 3 ) = x 2 ( x 1 x 3 ). Let F d ( B ) be the free d generated bicommutative algebra over a ﬁeld K of characteristic 0. We study the algebra F d ( B ) G of invariants of a subgroup G of the general linear group GL d ( K ). When G is ﬁnite we search for analogies of classical results of invariant theory of ﬁnite groups acting on polynomial algebras: the Endlichkeitssatz of Emmy Noether, the Molien formula and the Chevalley-Shephard-Todd theorem and show the similarities and the diﬀerences in the case of bicommutative algebras. We also describe the symmetric polynomials in F d ( B ).
Classification of Web Phishing Kits for early detection by platform providers

Phishing kits are tools that dark side experts provide to the community of criminal phishers to facilitate the construction of malicious Web sites. As these kits evolve in sophistication, providers of Web-based services need to keep pace with continuous complexity. We present an original classification of a corpus of over 2000 recent phishing kits according to their adopted evasion and obfuscation functions. We carry out an initial deterministic analysis of the source code of the kits to extract the most discriminant features and information about their principal authors. We then integrate this initial classification through supervised machine learning models. Thanks to the ground-truth achieved in the first step, we can demonstrate whether and which machine learning models are able to suitably classify even the kits adopting novel evasion and obfuscation techniques that were unseen during the training phase. We compare different algorithms and evaluate their robustness in the realistic case in which only a small number of phishing kits are available for training. This paper represents an initial but important step to support Web service providers and analysts in improving early detection mechanisms and intelligence operations for the phishing kits that might be installed on their platforms.
Well-definedness of Physical Law Learning: The Uniqueness Problem

Physical law learning is the ambiguous attempt at automating the derivation of governing equations with the use of machine learning techniques. The current literature focuses however solely on the development of methods to achieve this goal, and a theoretical foundation is at present missing. This paper shall thus serve as a ﬁrst step to build a comprehensive theoretical framework for learning physical laws, aiming to provide reliability to according algorithms. One key problem consists in the fact that the governing equations might not be uniquely determined by the given data. We will study this problem in the common situation that a physical law is described by an ordinary or partial differential equation. For various different classes of differential equations, we provide both necessary and sufﬁcient conditions for a function to uniquely determine the differential equation which is governing the phenomenon. We then use our results to devise numerical algorithms to determine whether a function solves a differential equation uniquely. Finally, we provide extensive numerical experiments showing that our algorithms in combination with common approaches for learning physical laws indeed allow to guarantee that a unique governing differential equation is learnt, without assuming any knowledge about the function, thereby ensuring reliability.
Provable Phase Retrieval with Mirror Descent

In this paper, we consider the problem of phase retrieval, which consists of recovering an $n$-dimensional real vector from the magnitude of its $m$ linear measurements. We propose a mirror descent (or Bregman gradient descent) algorithm based on a wisely chosen Bregman divergence, hence allowing to remove the classical global Lipschitz continuity requirement on the gradient of the non-convex phase retrieval objective to be minimized. We apply the mirror descent for two random measurements: the \iid standard Gaussian and those obtained by multiple structured illuminations through Coded Diffraction Patterns (CDP). For the Gaussian case, we show that when the number of measurements $m$ is large enough, then with high probability, for almost all initializers, the algorithm recovers the original vector up to a global sign change. For both measurements, the mirror descent exhibits a local linear convergence behaviour with a dimension-independent convergence rate. Our theoretical results are finally illustrated with various numerical experiments, including an application to the reconstruction of images in precision optics.
Inferring Synergistic and Antagonistic Interactions in Mixtures of Exposures

There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as synergistic interaction, while chemicals that inhibit each other's effects have antagonistic interactions. Current approaches for assessing the health effects of chemical mixtures do not explicitly consider synergy or antagonism in the modeling, instead focusing on either parametric or unconstrained nonparametric dose response surface modeling. The parametric case can be too inflexible, while nonparametric methods face a curse of dimensionality that leads to overly wiggly and uninterpretable surface estimates. We propose a Bayesian approach that decomposes the response surface into additive main effects and pairwise interaction effects, and then detects synergistic and antagonistic interactions. Variable selection decisions for each interaction component are also provided. This Synergistic Antagonistic Interaction Detection (SAID) framework is evaluated relative to existing approaches using simulation experiments and an application to data from NHANES.
Realizing quantum gates with optically-addressable $^{171}$Yb$^{+}$ ion qudits

The use of multilevel information carriers, also known as qudits, is a promising path for exploring scalability of quantum computing devices. Here we present a proof-of-principle realization of a quantum processor register that uses optically-addressed $^{171}$Yb$^{+}$ ion qudits in a linear trap. The rich level structure of $^{171}$Yb$^{+}$ ions allows using the Zeeman sublevels of the quadrupole clock transition at 435.5 nm for efficient and robust qudit encoding. We demonstrate the realization of the universal set of gates consisting of single-qudit rotations and a two-qudit Molmer-Sorensen operation with a two-ququart system, which is formally equivalent to a universal gate-based four-qubit processor. Our results paves a way towards further studies of more efficient implementations of quantum algorithms with trapped-ion-based processors and, specifically, exploring properties of $^{171}$Yb$^{+}$ ion qudits.
VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks

Real-time 3D mapping is a critical component in many important applications today including robotics, AR/VR, and 3D visualization. 3D mapping involves continuously fusing depth maps obtained from depth sensors in phones, robots, and autonomous vehicles into a single 3D representative model of the scene. Many important applications, e.g., global path planning and trajectory generation in micro aerial vehicles, require the construction of large maps at high resolutions. In this work, we identify mapping, i.e., construction and updates of 3D maps to be a critical bottleneck in these applications. The memory required and access times of these maps limit the size of the environment and the resolution with which the environment can be feasibly mapped, especially in resource constrained environments such as autonomous robot platforms and portable devices. To address this challenge, we propose VoxelCache: a hardware-software technique to accelerate map data access times in 3D mapping applications. We observe that mapping applications typically access voxels in the map that are spatially co-located to each other. We leverage this temporal locality in voxel accesses to cache indices to blocks of voxels to enable quick lookup and avoid expensive access times. We evaluate VoxelCache on popularly used mapping and reconstruction applications on both GPUs and CPUs. We demonstrate an average speedup of 1.47X (up to 1.66X) and 1.79X (up to 1.91X) on CPUs and GPUs respectively.
Differential Evolution based Dual Adversarial Camouflage: Fooling Human Eyes and Object Detectors

Deep neural network-based object detectors are vulnerable to adversarial examples. Among existing works to fool object detectors, the camouflage-based method is more often adopted due to its adaptation to multi-view scenarios and non-planar objects. However, most of them can still be easily observed by human eyes, which limits their application in the real world. To fool human eyes and object detectors simultaneously, we propose a differential evolution based dual adversarial camouflage method. Specifically, we try to obtain the camouflage texture by the two-stage training, which can be wrapped over the surface of the object. In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene background, making human eyes difficult to distinguish. In the second stage, we design three loss functions to optimize the local texture, which is selected from the global texture, making object detectors ineffective. In addition, we introduce the differential evolution algorithm to search for the near-optimal areas of the object to attack, improving the adversarial performance under certain attack area limitations. Experimental results show that our proposed method can obtain a good trade-off between fooling human eyes and object detectors under multiple specific scenes and objects.
DyFEn: Agent-Based Fee Setting in Payment Channel Networks

In recent years, with the development of easy to use learning environments, implementing and reproducible benchmarking of reinforcement learning algorithms has been largely accelerated by utilizing these frameworks. In this article, we introduce the Dynamic Fee learning Environment (DyFEn), an open-source real-world financial network model. It can provide a testbed for evaluating different reinforcement learning techniques. To illustrate the promise of DyFEn, we present a challenging problem which is a simultaneous multi-channel dynamic fee setting for off-chain payment channels. This problem is well-known in the Bitcoin Lightning Network and has no effective solutions. Specifically, we report the empirical results of several commonly used deep reinforcement learning methods on this dynamic fee setting task as a baseline for further experiments. To the best of our knowledge, this work proposes the first virtual learning environment based on a simulation of blockchain and distributed ledger technologies, unlike many others which are based on physics simulations or game platforms.
Differentiable Hybrid Traffic Simulation

We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients for traffic states across time steps and inhomogeneous lanes. To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well. We also show that we can use analytical gradients to accelerate the overall process and enhance scalability. Thanks to these gradients, our simulator can provide more efficient and scalable solutions for complex learning and control problems posed in traffic engineering than other existing algorithms. Refer to https://sites.google. com/umd.edu/diff-hybrid-traffic-sim for our project.
Evaluating Guiding Spaces for Motion Planning

Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more information, or are more likely to participate in the final solution. In this work, we define the \emph{motion planning guiding space}, which encapsulates many seemingly distinct prior works under the same framework. In addition, we suggest an information theoretic method to evaluate guided planning which places the focus on the quality of the resulting biased sampling. Finally, we analyze several motion planning algorithms in order to demonstrate the applicability of our definition and its evaluation.
Magnetic and elastic properties of YCo5 and LaCo5 under pressure

Daniela Koudela,1 Ulrich Schwarz,2 Helge Rosner,2 Ulrich Burkhardt,2 Axel Handstein,1 Michael Hanfland,3 Michael D. Kuz’min,1 Ingo Opahle,1 Klaus Koepernik,1 K. -H. Muller,1 and Manuel Richter1 1Leibniz-Institut fur Festkorperund Werkstoffforschung, IFW Dresden, PF 270116, 01171 Dresden, Germany 2Max-Planck-Institut fur Chemische Physik fester Stoffe, Nothnitzer Strase 40, 01187 Dresden, Germany 3European Synchrotron Radiation Facility, Boite Postale 220, 38043 Grenoble, France Received 27 September 2007; published 11 January 2008