Miracle-dz/UniFAR-FTUs huggingface dataset:
Micromagnetics of magnetic chemical modulations in soft-magnetic cylindrical nanowires

We analyze the micromagnetics of short longitudinal modulations of a high-magnetization material in cylindrical nanowires made of a soft-magnetic material of lower magnetization such as permalloy, combining magnetic microscopy, analytical modeling, and micromagnetic simulations. The mismatch of magnetization induces curling of magnetization around the axis in the modulations, in an attempt to screen the interfacial magnetic charges. The curling angle increases with modulation length, until a plateau is reached with nearly full charge screening for a specific length scale~$\Delta_\mathrm{mod}$, larger than the dipolar exchange length of any of the two materials. The curling circulation can be switched by the Oersted field arising from a charge current with typical magnitude $10^{12} A/m^{2}$ for a diameter of $\sim$100 nm, and reaching a maximum for $\Delta_\mathrm{mod}$.
Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering

A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects. Existing methods mostly seek to align the word representations with the video regions. However, word representations are often not able to convey a complete description of textual concepts, which are in general described by the compositions of certain words. To address this is-sue, we propose to ﬁrst build a syntactic dependency tree for each question with an off-the-shelf tool and use it to guide the extraction of meaningful word compositions. Based on the extracted compositions, a hypergraph is further built by viewing the words as nodes and the compositions as hyperedges. Hypergraph convolutional networks (HCN) are then employed to learn the initial representations of word compositions. Afterwards, an optimal transport based method is proposed to perform cross-modal semantic alignment for the textual and visual semantic space. To reﬂect the cross-modal inﬂuences, the cross-modal information is incorporated into the initial representations, leading to a model named cross-modality-aware syntactic HCN. Experimental results on three benchmarks show that our method outperforms all strong baselines. Further analyses demonstrate the effectiveness of each component, and show that our model is good at modeling different levels of semantic compositions and ﬁltering out irrelevant information.
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset

During the past decade, neural network models have made tremendous progress on in-domain semantic role labeling (SRL). However, performance drops dramatically under the out-of-domain setting. In order to facilitate research on cross-domain SRL, this paper presents MuCPAD, a multi-domain Chinese predicate-argument dataset, which consists of 30,897 sentences and 92,051 predicates from six different domains. MuCPAD exhibits three important features. 1) Based on a frame-free annotation methodology, we avoid writing complex frames for new predicates. 2) We explicitly annotate omitted core arguments to recover more complete semantic structure, considering that omission of content words is ubiquitous in multi-domain Chinese texts. 3) We compile 53 pages of annotation guidelines and adopt strict double annotation for improving data quality. This paper describes in detail the annotation methodology and annotation process of MuCPAD, and presents in-depth data analysis. We also give benchmark results on cross-domain SRL based on MuCPAD.
Skew-sparse matrix multiplication

Based on the observation that Q ( p − 1) × ( p − 1) is isomorphic to a quotient skew polynomial ring, we propose a new method for ( p − 1) × ( p − 1) matrix multiplication over Q , where p is a prime number. The main feature of our method is the acceleration for matrix multiplication if the product is skew-sparse. Based on the new method, we design a deterministic algorithm with complexity O( T ω − 2 p 2 ), where T ≤ p − 1 is a parameter determined by the skew-sparsity of input matrices and ω is the asymptotic exponent of matrix multiplication. Moreover, by introducing randomness, we also propose a probabilistic algorithm with complexity O ∼ ( t ω − 2 p 2 + p 2 log 1 ν ), where t ≤ p − 1 is the skew-sparsity of the product and ν is the probability parameter.
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech, accurate recognition of dysarthric and elderly speech remains a highly challenging task to date. It is difficult to collect large quantities of such data for ASR system development due to the mobility issues often found among these users. To this end, data augmentation techniques play a vital role. In contrast to existing data augmentation techniques only modifying the speaking rate or overall shape of spectral contour, fine-grained spectro-temporal differences between dysarthric, elderly and normal speech are modelled using a novel set of speaker-dependent (SD) generative adversarial networks (GAN) based data augmentation approaches in this article. These flexibly allow both: a) temporal or speed perturbed normal speech spectra to be modified and closer to those of an impaired speaker when parallel speech data is available; and b) for non-parallel data, the SVD decomposed normal speech spectral basis features to be transformed into those of a target elderly speaker before being re-composed with the temporal bases to produce the augmented data for state-of-the-art TDNN and Conformer ASR system training. Experiments are conducted on four tasks: the English UASpeech and TORGO dysarthric speech corpora; the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets. The proposed GAN based data augmentation approaches consistently outperform the baseline speed perturbation method by up to 0.91% and 3.0% absolute (9.61% and 6.4% relative) WER reduction on the TORGO and DementiaBank data respectively. Consistent performance improvements are retained after applying LHUC based speaker adaptation.
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments

Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipulators in real-world human environments. Therefore, we propose a shielding mechanism that ensures ISO- verified human safety while training and deploying RL algorithms on manipulators. We utilize a fast reachability analysis of humans and manipulators to guarantee that the manipulator comes to a complete stop before a human is within its range. Our proposed method guarantees safety and significantly improves the RL performance by preventing episode-ending collisions. We demonstrate the performance of our proposed method in simulation using human motion capture data.
Monocular Human Digitization via Implicit Re-projection Networks

We present an approach to generating 3D human models from images. The key to our framework is that we predict double-sided orthographic depth maps and color images from a single perspective projected image. Our framework consists of three networks. The ﬁrst network predicts normal maps to recover geometric details such as wrinkles in the clothes and facial regions. The second network predicts shade-removed images for the front and back views by utilizing the predicted normal maps. The last multi-headed network takes both normal maps and shade-free images and predicts depth maps while selectively fusing photometric and geometric information through multi-headed attention gates. Experimental results demonstrate that our method shows visually plausible results and competitive performance in terms of various evaluation metrics over state-of-the-art methods.
Modeling Human Behavior Part I - Learning and Belief Approaches

There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to “learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.
Collaborative Multi-agent Stochastic Linear Bandits

We study a collaborative multi-agent stochastic linear bandit setting, where N agents that form a network communicate locally to minimize their overall regret. In this setting, each agent has its own linear bandit problem (its own reward parameter) and the goal is to select the best global action w.r. t. the average of their reward parameters. At each round, each agent proposes an action, and one action is randomly selected and played as the network action. All the agents observe the corresponding rewards of the played action, and use an accelerated consensus procedure to compute an estimate of the average of the rewards obtained by all the agents. We propose a distributed upper confidence bound (UCB) algorithm and prove a high probability bound on its T-round regret in which we include a linear growth of regret associated with each communication round. Our regret bound is of order $\mathcal{O}\left( {\sqrt {\frac{T}{{N\log \left( {1/\left| {{\lambda _2}} \right|} \right)}}} \cdot {{\left( {\log T} \right)}^2}} \right)$, where λ2 is the second largest (in absolute value) eigenvalue of the communication matrix.
Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning

The high demand for data rate in the next generation of wireless communication could be ensured by Non-Orthogonal Multiple Access (NOMA) approach in the millimetre-wave (mmW) frequency band. Joint power allocation and beamforming of mmW-NOMA systems is mandatory which could be met by optimization approaches. To this end, we have exploited Deep Reinforcement Learning (DRL) approach due to policy generation leading to an optimized sum-rate of users. Actor-critic phenomena are utilized to measure the immediate reward and provide the new action to maximize the overall Q-value of the network. The immediate reward has been deﬁned based on the summation of the rate of two users regarding the minimum guaranteed rate for each user and the sum of consumed power as the constraints. The simulation results represent the superiority of the proposed approach rather than the Time-Division Multiple Access (TDMA) and another NOMA optimized strategy in terms of sum-rate of users.
A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example

Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e.g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry. In this paper, we propose to represent utterance with a simpler concept named Dialogue Action, upon which we construct a tree-structured TaskFlow and further build task-oriented chatbot with TaskFlow as core component. A framework is presented to automatically construct TaskFlow from large-scale dialogues and deploy online. Our experiments on real-world after-sale customer services show TaskFlow can satisfy the major needs, as well as reduce the developer burden effectively.
Knowledge Distillation Meets Open-Set Semi-Supervised Learning

Existing knowledge distillation methods mostly focus on distillation of teacher's prediction and intermediate activation. However, the structured representation, which arguably is one of the most critical ingredients of deep models, is largely overlooked. In this work, we propose a novel {\em \modelname{}} ({\bf\em \shortname{})} method dedicated for distilling representational knowledge semantically from a pretrained teacher to a target student. The key idea is that we leverage the teacher's classifier as a semantic critic for evaluating the representations of both teacher and student and distilling the semantic knowledge with high-order structured information over all feature dimensions. This is accomplished by introducing a notion of cross-network logit computed through passing student's representation into teacher's classifier. Further, considering the set of seen classes as a basis for the semantic space in a combinatorial perspective, we scale \shortname{} to unseen classes for enabling effective exploitation of largely available, arbitrary unlabeled training data. At the problem level, this establishes an interesting connection between knowledge distillation with open-set semi-supervised learning (SSL). Extensive experiments show that our \shortname{} outperforms significantly previous state-of-the-art knowledge distillation methods on both coarse object classification and fine face recognition tasks, as well as less studied yet practically crucial binary network distillation. Under more realistic open-set SSL settings we introduce, we reveal that knowledge distillation is generally more effective than existing Out-Of-Distribution (OOD) sample detection, and our proposed \shortname{} is superior over both previous distillation and SSL competitors. The source code is available at \url{https://github.com/jingyang2017/SRD\_ossl}.
Superconducting and structural properties of the noncentrosymmetric 
Re6Hf
 superconductor under high pressure

We report the effect of high pressure on the superconducting, vortex pinning, and structural properties of a polycrystalline non-centrosymmetric superconductor Re 6 Hf. The superconducting transition temperature, 𝑇 𝑐 , reveals a modest decrease as pressure (𝑃) increases with a slope -0.046 K/GPa (-0.065 K/GPa) estimated from resistivity measurements up to 8 GPa (magnetization measurement ~ 1.1 GPa). Structural analysis up to ~18 GPa reveals monotonic decreases of lattice constant without undergoing any structural transition and a high value of bulk modulus 𝐵 0 ≈ 333.63 GPa, indicating the stability of the structure. Furthermore, the upper critical field and lower critical field at absolute temperature (𝐻 𝑐2 (0)& 𝐻 𝑐1 (0)) decreases slightly from the ambient pressure value as pressure increases up to 2.5 GPa. In addition, analysis of 𝜌(𝑇, 𝐻) up to 𝑃 ~ 2.5 GPa using thermally activated flux flow of vortices
Modular Baskets Queue

A modular version of the baskets queue of Hoffman, Shalev and Shavit is presented. It manipulates the head and tail using a novel object called load-link/increment-conditional , which can be implemented using only READ / WRITE instructions, and admits implementations that spread contention. This suggests that there might be an alternative to the seemingly inherent bottleneck in previous queue implementations that manipulate the head and the tail using read-modify-write instructions over a single shared register.
OF MULTIPLE SHOCKS IN THE MODULAR BURGERS EQUATION

A traveling viscous shock was previously studied in the Burgers equation with the modular advection term. It was shown that small, smooth, and exponentially decaying in space perturbations to the viscous shock decay in time. The present work addresses multiple shocks of the same model. We ﬁrst prove that no traveling viscous shocks with multiple interfaces exist. We then suggest with the help of a priori energy estimates and numerical simulations that the evolution of viscous shocks with multiple interfaces leads to the ﬁnite-time extinction of compact regions between two consequent interfaces. We specify a precise scaling law of the ﬁnite-time extinction supported by the interface equations and by numerical simulations.
Weakly Supervised Text Classification using Supervision Signals from a Language Model

Solving text classification in a weakly supervised manner is important for real-world applications where human annotations are scarce. In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals. We design a prompt which combines the document itself and"this article is talking about [MASK]. "A masked language model can generate words for the [MASK] token. The generated words which summarize the content of a document can be utilized as supervision signals. We propose a latent variable model to learn a word distribution learner which associates generated words to pre-defined categories and a document classifier simultaneously without using any annotated data. Evaluation on three datasets, AGNews, 20Newsgroups, and UCINews, shows that our method can outperform baselines by 2%, 4%, and 3%.
Optimizing Apportionment of Redundancies in Hierarchical RAID

Large disk arrays are organized into storage nodes -- SNs or bricks with their own cashed RAID controller for multiple disks. Erasure coding at SN level is attained via parity or Reed-Solomon codes. Hierarchical RAID -- HRAID -- provides an additional level of coding across SNs, e.g., check strips P, Q at intra-SN level and R at the inter-SN level. Failed disks and SNs are not replaced and rebuild is accomplished by restriping, e.g., overwriting P and Q for disk failures and R for an SN failure. For a given total redundancy level we use an approximate reliability analysis method and Monte-Carlo simulation to explore the better apportionment of check blocks for intra- vs inter-SN redundancy. Our study indicates that a higher MTTDL -- Mean-Time-to-Data-Loss -- is attained by associating higher reliability at intra-SN level rather than inter-SN level, which is contrary to that of an IBM study.
Building A Trusted Execution Environment for In-Storage Computing

In-storage computing with modern solid-state drives (SSDs) enables developers to offload programs from the host to the SSD. It has been proven to be an effective approach to alleviating the I/O bottleneck. To facilitate in-storage computing, many frameworks have been proposed. However, few of them consider security as the priority for in-storage computing. Specifically, since modern SSD controllers do not have a trusted execution environment, an offloaded (malicious) program could steal, modify, and even destroy the data stored in the SSD. In this paper, we first investigate the attacks that could be conducted by offloaded in-storage programs. To defend against these attacks, we build IceClave, a lightweight trusted execution environment for in-storage computing. IceClave enables security isolation between in-storage programs and flash management functions. IceClave also achieves security isolation between in-storage programs and enforces memory encryption and integrity verification of in-storage DRAM with low overhead. To protect data loaded from flash chips, IceClave develops a lightweight data encryption/decryption mechanism in flash controllers. We develop IceClave with a full system simulator and evaluate IceClave with a variety of data-intensive applications. Compared to state-of-the-art in-storage computing approaches, IceClave introduces only 7.6% performance overhead, while enforcing security isolation in the SSD controller with minimal hardware cost. IceClave still keeps the performance benefit of in-storage computing by delivering up to 2.31$\times$ better performance than the conventional host-based trusted computing approach.
Electron-phonon interaction and point contact enhanced superconductivity in trigonal PtBi2

PtBi2 is a Weyl semimetal, which demonstrates superconductivity with low critical temperature Tc ∼ 0.6 K in the bulk. Here, we report our study of electron-phonon interaction (EPI) in trigonal PtBi2 by the Yanson point- contact (PC) spectroscopy and present the observation of PC enhanced superconductivity. We show that the Yanson’s PC spectra display a broad maximum around 15 meV, indicating, apparently, EPI mechanism of Cooper pairing in PtBi2. Moreover, we discovered a substantial increase of Tc up to ∼ 3.5 K in PCs. The observed Tc is suf­ficiently higher than the bulk value, as well as detected at hydrostatic pressure. We calculated the phonon density of states and Eliashberg EPI function in PtBi2 within the framework of the density functional theory. A comparison of experimental data with theoretical calculations showed acceptable agreement. The theoretical Tc is 3.5 K, which corresponds to the experimental value.
Privacy Preserving Release of Mobile Sensor Data

Sensors embedded in mobile smart devices can monitor users' activity with high accuracy to provide a variety of services to end-users ranging from precise geolocation, health monitoring, and handwritten word recognition. However, this involves the risk of accessing and potentially disclosing sensitive information of individuals to the apps that may lead to privacy breaches. In this paper, we aim to minimize privacy leakages that may lead to user identification on mobile devices through user tracking and distinguishability while preserving the functionality of apps and services. We propose a privacy-preserving mechanism that effectively handles the sensor data fluctuations (e.g., inconsistent sensor readings while walking, sitting, and running at different times) by formulating the data as time-series modeling and forecasting. The proposed mechanism also uses the notion of correlated noise-series against noise filtering attacks from an adversary, which aims to filter out the noise from the perturbed data to re-identify the original data. Unlike existing solutions, our mechanism keeps running in isolation without the interaction of a user or a service provider. We perform rigorous experiments on benchmark datasets and show that our proposed mechanism limits user tracking and distinguishability threats to a significant extent compared to the original data while maintaining a reasonable level of utility of functionalities. In general, we show that our obfuscation mechanism reduces the user trackability threat by 60\% across all the datasets while maintaining the utility loss below 0.5 Mean Absolute Error (MAE). We also observe that our mechanism is more effective in large datasets. For example, with the Swipes dataset, the distinguishability risk is reduced by 60\% on average while the utility loss is below 0.5 MAE.