Miracle-dz/UniFAR-FTUs huggingface dataset:
DH-Net: Deformed Microstructure Homogenization via 3D Convolutional Neural Networks

With the rapid development of additive manufacturing, microstructures are attracting both academic and industrial interests. As an eﬃcient way of analyzing the mechanical behaviors of microstructures, the homogenization method has been well studied in the literature. However, the classic homogenization method still faces challenges. Its computational cost is high for topological optimization that requires highly repeated calculation. The computation is more expensive when the microstructure is deformed from a regular cubic, causing changes for the virtual homogeneous material properties. To conquer this problem, we introduce a ﬁne-designed 3D convolutional neural network (CNN), named DH-Net, to predict the homogenized properties of deformed microstructures. The novelty of DH-Net is that it predicts the local displacement rather than the homogenized properties. The macroscopic strains are considered as a constant in the loss function based on minimum potential energy. Thus DH-Net is label-free and more computation eﬃcient than existing deep learning methods with the mean square loss function. We apply the shape-material transformation that a deformed microstructure with isotropic material can be bi-transformed into a regular structure with a transformed base material, such that the input with a CNN-friendly form feeds in DH-Net. DH-Net predicts homogenized properties with hundreds of acceleration compared to the standard homogenization method and even supports online computing. Moreover, it does not require a labeled dataset
NAS-VAD: Neural Architecture Search for Voice Activity Detection

Various neural network-based approaches have been proposed for more robust and accurate voice activity detection (VAD). Manual design of such neural architectures is an error-prone and time-consuming process, which prompted the development of neural architecture search (NAS) that automatically design and optimize network architectures. While NAS has been suc-cessfully applied to improve performance in a variety of tasks, it has not yet been exploited in the VAD domain. In this pa-per, we present the ﬁrst work that utilizes NAS approaches on the VAD task. To effectively search architectures for the VAD task, we propose a modiﬁed macro structure and a new search space with a much broader range of operations that includes attention operations. The results show that the network structures found by the propose NAS framework outperform previous manually designed state-of-the-art VAD models in various noise-added and real-world-recorded datasets. We also show that the architectures searched on a particular dataset achieve improved generalization performance on unseen audio datasets. Our code and models are available at https://github.com/daniel03c1/NAS VAD.
Role of porosity and diffusion coefficient in porous electrode used in supercapacitors – Correlating theoretical and experimental studies

Role of porosity and diffusion coefficient in porous electrode used in supercapacitorsCorrelating theoretical and experimental studies Puja De, Joyanti Halder, Chinmayee Chowde Gowda, Sakshi Kansal, Surbhi Priya, Satvik Anshu, Ananya Chowdhury, Debabrata Mandal, Sudipta Biswas, Brajesh Kumar Dubey and Amreesh Chandra 2, 3* Department of Physics, School of Nano Science and Technology, School of Energy Science and Engineering, Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India-7213202.
Multipath ghost elimination for through‐wall radar imaging

In through-wall radar imaging, the multipath echoes caused by specular reflections at interior walls of an enclosed room will lead to multipath ghosts in the resulting image. The ghosts appear like the true targets and therefore tend to increase the number of false alarms. To solve this problem, a new ghost elimination method that removes multipath echoes from the raw radar data is proposed in this study. With a priori knowledge of the room, a concise multipath model is first developed, based on which the locations of the multipath echoes can be determined. Then, a cancellation process that consists of two steps is performed wherein the multipath echoes are cancelled in a specific order depending on the positions of the targets they correspond to. The major advantage of this method is that it can preserve the true targets while eliminating the ghosts even if the targets overlap with those ghosts. The effectiveness of the proposed method is validated by the processed imaging results using both the finite-difference time-domain-simulated data and the real experimental data.
Robust Trajectory and Communication Design in IRS-Assisted UAV Communication under Malicious Jamming

In this paper, we study an unmanned aerial vehicle (UAV) communication system, where a ground node (GN) commu-nicate with a UAV assisted by intelligent reflecting surface (IRS) in the presence of a jammer with imperfect location information. We aim to improve the achievable average rate via the joint robust design of UAV trajectory, IRS passive beamforming and GN's power allocation. However, the formulated optimization problem is challenging to solve due to its non-convexity and coupled variables. To overcome the difficulty, we propose an alternating optimization (AO) based algorithm to solve it sub-optimally by leveraging semidefinite relaxation (SDR), successive convex approximation (SCA), and S-procedure methods. Simulation results show that by deploying the IRS near the GN, our proposed algorithm always improves the uplink achievable average rate significantly compared with the benchmark algorithms, while deploying the IRS nearby the jammer is effective only when the jammer's location is perfectly known.
Household Technology and the Social Construction of Housework

Historians and social scientists generally agree that the forces of industrialization and the growth of the market economy have progressively absorbed much of the household's economic function. Furthermore, popular belief assumes that the vestiges of old forms of production in the home will surely be eliminated by the application of technological rationality. Put another way, it is thought by the public and many academics that "technology," broadly defined, has "freed" women for other, nonhousework tasks-in particular, employment in the paid labor market. These assumptions are reflected in popular terminology such as "fast foods" (to save time), "convenience foods" (to increase ease of preparation), and "laborsaving devices" (conducive to easing the work load generally). This imagery has such power that much traditional research takes these effects for granted instead of demonstrating or disproving them empirically. Yet even with the unprecedented growth of the market sector and the almost universal availability of certain items of household equipment and goods, recent studies show that labor in the home still accounts for approximately half of this country's total work time.1 In this article our goal is to investigate the evidence concerning the effects of technological developments on household work. We believe that popular beliefs about the positive effects are inadequately sub-
Efficient and Robust Classification for Sparse Attacks

In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy. Parallel to this, we have also witnessed how fragile the very same prediction models are: tiny perturbations to the inputs can cause misclassification errors throughout entire datasets. In this paper, we consider perturbations bounded by the ℓ0–norm, which have been shown as effective attacks in the domains of image-recognition, natural language processing, and malware-detection. To this end, we propose a novel defense method that consists of "truncation" and "adversarial training". We then theoretically study the Gaussian mixture setting and prove the asymptotic optimality of our proposed classifier. Motivated by the insights we obtain, we extend these components to neural network classifiers. We conduct numerical experiments in the domain of computer vision using the MNIST and CIFAR datasets, demonstrating significant improvement for the robust classification error of neural networks.
Digital Information Seeking and Sharing Behavior During the First Wave of the COVID-19 Pandemic

People turn to search engines and social media to seek information during population-level events, such as during civil unrest, disease outbreaks, fires, or flood. They also tend to participate in discussions and disseminate information and opinions via social media forums, and smartphone messaging applications. COVID-19 pandemic was not any different. However, the proper medical awareness and correct information dissemination is critical during a pandemic. An unprecedented amount of internet traffic related to this topic was generated during the start of the pandemic all over the world. In this work, we have analysed the electronic data generated by users from Pakistan on Google Search Engine and WhatsApp to understand their information-seeking behavior during the first wave of the pandemic. The paper aims at analysing how the Pakistani public developed their understanding about the disease, (its origin, cures, and preventive measures to name a few) by analysing digital data. We found that the public actively searched and discussed information at the start of the pandemic. However, their interest waned with time and was reinvigorated only when something novel or shocking seemed to have occurred. Understanding this information seeking behavior will allow corrective actions to be taken by health policymakers to better inform the public for possible future waves of a pandemic through electronic media, as well as and the social media companies and search engines to address misinformation among the users in the emergent markets.
Large-Dimensional Multibody Dynamics Simulation Using Contact Nodalization and Diagonalization

In this article, we propose a novel multibody dynamics simulation framework that can efficiently deal with large-dimensionality and complementarity multicontact conditions. Typical contact simulation approaches require performing contact impulse fixed-point iteration, which has high time-complexity from large-size matrix factorization and multiplication, as well as susceptibility to ill-conditioned contact situations. To circumvent this, we propose a novel framework based on velocity fixed-point iteration (V-FPI), which, by utilizing a certain surrogate dynamics and contact nodalization (with virtual nodes), we achieve not only intercontact decoupling but also their interaxes decoupling (i.e., contact diagonalization) at each iteration step. This then enables us to one-shot/parallel-solve the contact problem during each V-FPI iteration-loop, while avoiding large-size/dense matrix inversion/multiplication, thereby, significantly speeding up the simulation time with improved convergence property. We theoretically show that the solution of our framework is consistent with that of the original problem and, further, elucidate mathematical conditions for the convergence of our proposed solver. Performance and properties of our proposed simulation framework are also demonstrated and experimentally validated for various large-dimensional/multicontact scenarios including deformable objects.
External Stability Auditing to Test the Validity of Personality Prediction in AI Hiring

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media proﬁles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufﬁcient,
Swelling, softening, and elastocapillary adhesion of cooked pasta

The diverse chemical and physical reactions encountered during cooking connect us to science every day. Here, we theoretically and experimentally investigate the swelling and softening of pasta due to liquid imbibition as well as the elastic deformation and adhesion of pasta due to capillary force. As water diffuses into the pasta during cooking, it softens gradually from the outside inward as starch swells and relaxes. The softening follows three sequential regimes: regime I, the hard-glassy region, shows a slow decrease in modulus with cooking time; regime II, the glassy to rubbery transition region, or leathery region, is characterized by a very fast, several orders of magnitude drop in elastic modulus and regime III, the rubbery region, has an asymptotic modulus about four orders of magnitude lower than the raw pasta. We present experiments and theory to capture these regimes and relate them to the heterogeneous microstructure changes associated with swelling. Interestingly, we observe a modulus drop of two orders of magnitude within the range of “al dente” cooking duration, and we find the modulus to be extremely sensitive to the amount of salt added to the boiling water. While most chefs can gauge the pasta by tasting its texture, our proposed experiment, which only requires a measurement with a ruler, can precisely provide an optimal cooking time finely tuned for various kinds of pasta shapes.
Distributed Learning of Generalized Linear Causal Networks

We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) to solve this problem. We model causal structures by a directed acyclic graph that is parameterized with generalized linear models, so that our method is applicable to various types of data. To obtain a high-scoring causal graph, DARLS simulates an annealing process to search over the space of topological sorts, where the optimal graphical structure compatible with a sort is found by a distributed optimization method. This distributed optimization relies on multiple rounds of communication between local and central machines to estimate the optimal structure. We establish its convergence to a global optimizer of the overall score that is computed on all data across local machines. To the best of our knowledge, DARLS is the first distributed method for learning causal graphs with such theoretical guarantees. Through extensive simulation studies, DARLS has shown competing performance against existing methods on distributed data, and achieved comparable structure learning accuracy and test-data likelihood with competing methods applied to pooled data across all local machines. In a real-world application for modeling protein-DNA binding networks with distributed ChIP-Sequencing data, DARLS also exhibits higher predictive power than other methods, demonstrating a great advantage in estimating causal networks from distributed data.
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond

We study the framework of universal dynamic regret minimization with strongly convex losses. We answer an open problem in Baby and Wang 2021 by showing that in a proper learning setup, Strongly Adaptive algorithms can achieve the near optimal dynamic regret of $\tilde O(d^{1/3} n^{1/3}\text{TV}[u_{1:n}]^{2/3} \vee d)$ against any comparator sequence $u_1,\ldots,u_n$ simultaneously, where $n$ is the time horizon and $\text{TV}[u_{1:n}]$ is the Total Variation of comparator. These results are facilitated by exploiting a number of new structures imposed by the KKT conditions that were not considered in Baby and Wang 2021 which also lead to other improvements over their results such as: (a) handling non-smooth losses and (b) improving the dimension dependence on regret. Further, we also derive near optimal dynamic regret rates for the special case of proper online learning with exp-concave losses and an $L_\infty$ constrained decision set.
Computation of weight 1 modular forms with exotic representations

We present a deterministic algorithm for computing spaces of weight 1 modular forms with exotic representations. This algorithm is an improved version of Schaeffer’s Hecke stability method, utilising the author’s previous work on the twist-minimal trace formula for weight 2 holomorphic forms, and presenting a method of lifting forms from characteristic p. The algorithm was used to compute all such forms with level at most 10,000. Together with Sutherland’s computation of dihedral forms, this allows us to present the dimensions of all weight 1 newform spaces up to level 10,000.
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery - a Focus on Affinity Prediction Problems with Noise Annotations

AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use in numerous fields (e.g., ADMET prediction, virtual screening), little research has been conducted on the out-of-distribution (OOD) learning problem with noise. We present DrugOOD, a systematic OOD dataset curator and benchmark for AIDD. Particularly, we focus on the drug-target binding affinity prediction problem, which involves both macromolecule (protein target) and small-molecule (drug compound). DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for graph OOD learning problems. Extensive empirical studies have revealed a significant performance gap between in-distribution and out-of-distribution experiments, emphasizing the need for the development of more effective schemes that permit OOD generalization under noise for AIDD.
Chinese Word Segmentation with Heterogeneous Graph Neural Network

In recent years, deep learning has achieved significant success in the Chinese word segmentation (CWS) task. Most of these methods improve the performance of CWS by leveraging external information, e.g., words, sub-words, syntax. However, existing approaches fail to effectively integrate the multi-level linguistic information and also ignore the structural feature of the external information. Therefore, in this paper, we proposed a framework to improve CWS, named HGNSeg. It exploits multi-level external information sufficiently with the pre-trained language model and heterogeneous graph neural network. The experimental results on six benchmark datasets (e.g., Bakeoff 2005, Bakeoff 2008) validate that our approach can effectively improve the performance of Chinese word segmentation. Importantly, in cross-domain scenarios, our method also shows a strong ability to alleviate the out-of-vocabulary (OOV) problem.
Description-Driven Task-Oriented Dialog Modeling

Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks. Such information is conventionally specified in terms of intents and slots contained in task-specific ontology or schemata. Since these schemata are designed by system developers, the naming convention for slots and intents is not uniform across tasks, and may not convey their semantics effectively. This can lead to models memorizing arbitrary patterns in data, resulting in suboptimal performance and generalization. In this paper, we propose that schemata should be modified by replacing names or notations entirely with natural language descriptions. We show that a language description-driven system exhibits better understanding of task specifications, higher performance on state tracking, improved data efficiency, and effective zero-shot transfer to unseen tasks. Following this paradigm, we present a simple yet effective Description-Driven Dialog State Tracking (D3ST) model, which relies purely on schema descriptions and an"index-picking"mechanism. We demonstrate the superiority in quality, data efficiency and robustness of our approach as measured on the MultiWOZ (Budzianowski et al.,2018), SGD (Rastogi et al., 2020), and the recent SGD-X (Lee et al., 2021) benchmarks.
User Pairing and Outage Analysis in Multi-Carrier NOMA-THz Networks

This paper provides a comprehensive framework to analyze the performance of non-orthogonal multiple access (NOMA) in the downlink transmission of a single-carrier and multi-carrier terahertz (THz) network. Specifically, we first develop a novel user pairing scheme for the THz-NOMA network which ensures the performance gains of NOMA over orthogonal multiple access (OMA) for each individual user in the NOMA pair and adapts according to the molecular absorption. Then, we characterize novel outage probability expressions considering a single-carrier and multi-carrier THz-NOMA network in the presence of various user pairing schemes, Nakagami-$m$ channel fading, and molecular absorption noise. We propose a moment-generating-function (MGF) based approach to analyze the outage probability of users in a multi-carrier THz network. Furthermore, for negligible thermal noise, we provide simplified single-integral expressions to compute the outage probability in a multi-carrier network. Numerical results demonstrate the performance of the proposed user-pairing scheme and validate the accuracy of the derived expressions.
Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs

Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are engaged with a large set of graphs and embedding data on storage, which exhibits complex and irregular preprocessing. We propose a novel deep learning framework on large graphs, HolisticGNN, that provides an easy-to-use, near-storage inference infrastructure for fast, energy-efficient GNN processing. To achieve the best end-to-end latency and high energy efficiency, HolisticGNN allows users to implement various GNN algorithms and directly executes them where the actual data exist in a holistic manner. It also enables RPC over PCIe such that the users can simply program GNNs through a graph semantic library without any knowledge of the underlying hardware or storage configurations. We fabricate HolisticGNN's hardware RTL and implement its software on an FPGA-based computational SSD (CSSD). Our empirical evaluations show that the inference time of HolisticGNN outperforms GNN inference services using high-performance modern GPUs by 7.1x while reducing energy consumption by 33.2x, on average.