Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent Linguistic Structures in Neural Networks are Fragile

Large Language Models (LLMs) have been reported to have strong performance on natural language processing tasks. However, performance metrics such as accuracy do not measure the quality of the model in terms of its ability to robustly represent complex linguistic structures. In this paper, focusing on the ability of language models to represent syntax, we propose a framework to assess the consistency and robustness of linguistic representations. To this end, we introduce measures of robustness of neural network models that leverage recent advances in extracting linguistic constructs from LLMs via probing tasks, i.e., simple tasks used to extract meaningful information about a single facet of a language model, such as syntax reconstruction and root identification. Empirically, we study the performance of four LLMs across six different corpora on the proposed robustness measures by analysing their performance and robustness with respect to syntax-preserving perturbations. We provide evidence that context-free representation (e.g., GloVe) are in some cases competitive with context-dependent representations from modern LLMs (e.g., BERT), yet equally brittle to syntax-preserving perturbations. Our key observation is that emergent syntactic representations in neural networks are brittle. We make the code, trained models and logs available to the community as a contribution to the debate about the capabilities of LLMs.
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the diversity of training scenarios. A lack of safety-critical scenarios during the training phase could result in poor generalization performance in real-world driving applications. We propose a novel framework in which the weaknesses of the training set are explored through black-box verification methods. After discovering AD failure scenarios, the RL agent's training is re-initiated via transfer learning to improve the performance of previously unsafe scenarios. Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications and significantly reduces the number of vehicle collisions through iterative applications of our method. The source code is publicly available at https://github.com/data-and-decision-lab/self-improving-RL.
Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework

Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors. Despite the effectiveness of recommending accurately, there exist several factors that are essential but unexplored for evaluating various facets of recommendation systems, e.g., fairness, diversity, and limited resources. To address these issues, we propose Track2Vec, a GPU-free customizable-driven framework for fairness music recommendation. In order to take both accuracy and fairness into account, our solution consists of three modules, a customized fairness-aware groups for modeling different features based on configurable settings, a track representation learning module for learning better user embedding, and an ensemble module for ranking the recommendation results from different track representation learning modules. Moreover, inspired by TF-IDF which has been widely used in natural language processing, we introduce a metric called Miss Rate - Inverse Ground Truth Frequency (MR-ITF) to measure the fairness. Extensive experiments demonstrate that our model achieves a 4th price ranking in a GPU-free environment on the leaderboard in the EvalRS @ CIKM 2022 challenge, which is superior to the official baseline by about 200% in terms of the official scores. In addition, the ablation study illustrates the necessity of ensembling each group to acquire both accurate and fair recommendations.
The consensus in the two-feature two-state one-dimensional Axelrod model revisited

The Axelrod model for the dissemination of culture exhibits a rich spatial distribution of cultural domains, which depends on the values of the two model parameters: F, the number of cultural features and q, the common number of states each feature can assume. In the one-dimensional model with F = q = 2, which is closely related to the constrained voter model, Monte Carlo simulations indicate the existence of multicultural absorbing configurations in which at least one macroscopic domain coexist with a multitude of microscopic ones in the thermodynamic limit. However, rigorous analytical results for the infinite system starting from the configuration where all cultures are equally likely show convergence to only monocultural or consensus configurations. Here we show that this disagreement is due simply to the order that the time-asymptotic limit and the thermodynamic limit are taken in the simulations. In addition, we show how the consensus-only result can be derived using Monte Carlo simulations of finite chains.
Augmented Reality and Mixed Reality Measurement Under Different Environments: A Survey on Head-Mounted Devices

Augmented reality (AR) and mixed reality (MR) have been two of the most explosive research topics in the last few years. Head-mounted devices (HMDs) are essential intermediums for using AR and MR technology, playing an important role in the research progress in these two areas. Behavioral research with users is one way of evaluating the technical progress and effectiveness of HMDs. In addition, AR and MR technology is dependent upon virtual interactions with the real environment. Thus, conditions in real environments can be a significant factor for AR and MR measurements with users. In this article, we survey 87 environmental-related HMD papers with measurements from users, spanning over 32 years. We provide a thorough review of AR- and MR-related user experiments with HMDs under different environmental factors. Then, we summarize trends in this literature over time using a new classification method with four environmental factors, the presence or the absence of user feedback in behavioral experiments, and ten main categories to subdivide these papers (e.g., domain and method of user assessment). We also categorize characteristics of the behavioral experiments, showing similarities and differences among papers.
Kepler K2 observations of the intermediate polar FO Aquarii

We present photometry of the intermediate polar FO Aquarii (FO Aqr) obtained as part of the K2 mission using the Kepler space telescope. The amplitude spectrum of the data confirms the orbital period of 4.8508(4) h, and the shape of the light curve is consistent with the outer edge of the accretion disc being eclipsed when folded on this period. The average flux of FO Aqr changed during the observations, suggesting a change in the mass accretion rate. There is no evidence in the amplitude spectrum of a longer period that would suggest disc precession. The amplitude spectrum also shows the white dwarf spin period of 1254.3401(4) s, the beat period of 1351.329(2) s, and 31 other spin and orbital harmonics. The detected period is longer than the last reported period of 1254.284(16) s, suggesting that FO Aqr is now spinning down, and has a positive P . There is no detectable variation in the spin period over the course of the K2 observations, but the phase of the spin cycle is correlated with the system brightness. We also find that the amplitude of the beat signal is correlated with the system brightness.
Kelvin-Helmholtz instabilities in smoothed particle hydrodynamics

In this paper we investigate whether smoothed particle hydrodynamics (SPH), equipped with artificial conductivity (AC), is able to capture the physics of density/energy discontinuities in the case of the so-called shearing layers test, a test for examining Kelvin–Helmholtz (KH) instabilities. We can trace back each failure of SPH to show KH rolls to two causes: (i) shock waves travelling in the simulation box and (ii) particle clumping, or more generally, particle noise. The probable cause of shock waves is the local mixing instability, previously identified in the literature. Particle noise on the other hand is a problem because it introduces a large error in the SPH momentum equation. The shocks are hard to avoid in SPH simulations with initial density gradients because the most straightforward way of removing them, i.e. relaxing the initial conditions, is not viable. Indeed, by the time sufficient relaxing has taken place the density and energy gradients have become prohibitively wide. The particle disorder introduced by the relaxation is also a problem. We show that setting up initial conditions with a suitably smoothed density gradient dramatically improves results: shock waves are reduced whilst retaining relatively sharp gradients and avoiding unnecessary particle disorder. Particle clumping is easy to overcome, the most straightforward method being the use of a suitable smoothing kernel with non-zero first central derivative. We present results to that effect using a new smoothing kernel: the linear quartic kernel. We also investigate the role of AC. Although AC is necessary in the simulations to avoid ‘oily’ features in the gas due to artificial surface tension, we fail to find any relation between using AC and the appearance of seeded KH rolls. Including AC is necessary for the long-term behaviour of the simulation (e.g. to get λ= 1/2, 1 KH rolls). In sensitive hydrodynamical simulations great care is however needed in selecting the AC signal velocity, with the default formulation leading to too much energy diffusion. We present new signal velocities that lead to less diffusion. The effects of the shock waves and of particle disorder become less important as the time-scale of the physical problem (for the shearing layers problem: lower density contrast and higher Mach numbers) decreases. At the resolution of current galaxy formation simulations mixing is probably not important. However, mixing could become crucial for next-generation simulations.
GowFed - A novel Federated Network Intrusion Detection System

Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties - violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GowFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GowFed have been developed based on state-of the-art knowledge: (1) a vanilla version; and (2) a version instrumented with an attention mechanism. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance - across a set of designed experiments/scenarios. Overall, GowFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.
Choi–Williams Distribution

The Choi–Williams distribution is a transform that represents the spectral content of nonstationary signal as a two-dimensional time–frequency map. It largely avoids one of the main problems of the Wigner–Ville distribution: the presence of interference terms in regions where one would expect zero power values. Keywords: spectral analysis; cardiovascular signals; nonstationary time series; time-varying spectra; Wigner–Ville distribution
Scalable Spectral Clustering with Group Fairness Constraints

There are synergies of research interests and industrial efforts in modeling fairness and correcting algorithmic bias in machine learning. In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints. Group fairness is also known as statistical parity where in each cluster, each protected group is represented with the same proportion as in the entirety. While FairSC algorithm (Kleindessner et al., 2019) is able to find the fairer clustering, it is compromised by high costs due to the kernels of computing nullspaces and the square roots of dense matrices explicitly. We present a new formulation of underlying spectral computation by incorporating nullspace projection and Hotelling's deflation such that the resulting algorithm, called s-FairSC, only involves the sparse matrix-vector products and is able to fully exploit the sparsity of the fair SC model. The experimental results on the modified stochastic block model demonstrate that s-FairSC is comparable with FairSC in recovering fair clustering. Meanwhile, it is sped up by a factor of 12 for moderate model sizes. s-FairSC is further demonstrated to be scalable in the sense that the computational costs of s-FairSC only increase marginally compared to the SC without fairness constraints.
Near-Ultraviolet Continuum Modeling of the 1985 April 12 Great Flare of AD Leo

White-light stellar ﬂares are now reported by the thousands in long-baseline, high-precision, broad-band photometry from missions like Kepler , K2, and TESS. These observations are crucial inputs for assessments of biosignatures in exoplanetary atmospheres and surface ultraviolet radiation dosages for habitable-zone planets around low-mass stars. A limitation of these assessments, however, is the lack of near-ultraviolet spectral observations of stellar ﬂares. To motivate further empirical investigation, we use a grid of radiative-hydrodynamic simulations with an updated treatment of the pressure broadening of hydrogen lines to predict the λ ≈ 1800 − 3300 ˚A continuum ﬂux during the rise and peak phases of a well-studied superﬂare from the dM3e star AD Leo. These predictions are based on semi-empirical superpositions of radiative ﬂux spectra consisting of a high-ﬂux electron beam simulation with a large, low-energy cutoff ( (cid:38) 85 keV) and a lower-ﬂux electron beam simulation with a smaller, low-energy cutoff ( (cid:46) 40 keV). The two-component models comprehensively explain the hydrogen Balmer line broadening, the optical continuum color temperature, the Balmer jump strength, and the far-ultraviolet continuum strength and shape in the rise/peak phase of this ﬂare. We use spatially resolved analyses of solar ﬂare data from the Interface Region Imaging Spectrograph, combined with the results of previous radiative-hydrodynamic modeling of the 2014 March 29 X1 solar ﬂare (SOL20140329T17:48), to interpret the two-component electron beam model as representing the spatial superposition of bright kernels and fainter ribbons over a larger area.
Recursive Reasoning in Minimax Games: A Level k Gradient Play Method

Despite the success of generative adversarial networks (GANs) in generating visually appealing images, they are notoriously challenging to train. In order to stabilize the learning dynamics in minimax games, we propose a novel recursive reasoning algorithm: Level $k$ Gradient Play (Lv.$k$ GP) algorithm. In contrast to many existing algorithms, our algorithm does not require sophisticated heuristics or curvature information. We show that as $k$ increases, Lv. $k$ GP converges asymptotically towards an accurate estimation of players' future strategy. Moreover, we justify that Lv. $\infty$ GP naturally generalizes a line of provably convergent game dynamics which rely on predictive updates. Furthermore, we provide its local convergence property in nonconvex-nonconcave zero-sum games and global convergence in bilinear and quadratic games. By combining Lv. $k$ GP with Adam optimizer, our algorithm shows a clear advantage in terms of performance and computational overhead compared to other methods. Using a single Nvidia RTX3090 GPU and 30 times fewer parameters than BigGAN on CIFAR-10, we achieve an FID of 10.17 for unconditional image generation within 30 hours, allowing GAN training on common computational resources to reach state-of-the-art performance.
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers

Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting Text-to-SQL parsers to new database schemas is a challenging problem owing to a vast diversity of schemas and zero availability of natural language queries in new schemas. We present ReFill, a framework for synthesizing high-quality and textually diverse parallel datasets for adapting Text-to-SQL parsers. Unlike prior methods that utilize SQL-to-Text generation, ReFill learns to retrieve-and-edit text queries in existing schemas and transfer them to the new schema. ReFill utilizes a simple method for retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the new schema. We show that this process leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation models. Through experiments on several databases, we show that adapting a parser by finetuning it on datasets synthesized by ReFill consistently outperforms prior data-augmentation methods.
GRACE: Loss-Resilient Real-Time Video Communication Using Data-Scalable Autoencoder

Across many real-time video applications, we see a growing need (especially in long delays and dynamic bandwidth) to allow clients to decode each frame once any (non-empty) subset of its packets is received and improve quality with each new packet. We call it data-scalable delivery. Unfortunately, existing techniques (e.g., FEC, RS and Fountain Codes) fall short: they require either delivery of a minimum number of packets to decode frames, and/or pad video data with redundancy in anticipation of packet losses, which hurts video quality if no packets get lost. This work explores a new approach, inspired by recent advances of neural-network autoencoders, which make data-scalable delivery possible. We present Grace, a concrete data-scalable real-time video system. With the same video encoding, Grace's quality is slightly lower than traditional codec without redundancy when no packet is lost, but with each missed packet, its quality degrades much more gracefully than existing solutions, allowing clients to flexibly trade between frame delay and video quality. Grace makes two contributions: (1) it trains new custom autoencoders to balance compression efficiency and resilience against a wide range of packet losses; and (2) it uses a new transmission scheme to deliver autoencoder-coded frames as individually decodable packets. We test Grace (and traditional loss-resilient schemes and codecs) on real network traces and videos, and show that while Grace's compression efficiency is slightly worse than heavily engineered video codecs, it significantly reduces tail video frame delay (by 2$\times$ at the 95th percentile) with the marginally lowered video quality
Transmit Optimization for Multi-functional MIMO Systems Integrating Sensing, Communication, and Powering

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system integrating wireless sensing, communication, and powering. In this system, one multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with one multi-antenna information decoding (ID) receiver, wirelessly charge one multi-antenna energy harvesting (EH) receiver, and perform radar sensing for a point target based on the echo signal at the same time. Under this setup, we aim to reveal the fundamental performance tradeoff limits of sensing, communication, and powering, in terms of the estimation Cramér-Rao bound (CRB), achievable communication rate, and harvested energy level, respectively. Towards this end, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain the semi-closed-form optimal transmit covariance solution to the formulated problem by applying advanced convex optimization techniques. Numerical results show the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark scheme based on time switching.
A Versatile Diffusion-based Generative Refiner for Speech Enhancement

Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs. To tackle this problem, we introduce a DNN-based generative refiner, Diffiner, aiming to improve perceptual speech quality pre-processed by an SE method. We train a diffusion-based generative model by utilizing a dataset consisting of clean speech only. Then, our refiner effectively mixes clean parts newly generated via denoising diffusion restoration into the degraded and distorted parts caused by a preceding SE method, resulting in refined speech. Once our refiner is trained on a set of clean speech, it can be applied to various SE methods without additional training specialized for each SE module. Therefore, our refiner can be a versatile post-processing module w.r. t. SE methods and has high potential in terms of modularity. Experimental results show that our method improved perceptual speech quality regardless of the preceding SE methods used.
An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents

Workplace accidents are a critical problem that causes many deaths, injuries, and ﬁnancial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively ﬁnd unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the ﬁrst to analyze prolonged actions involving multiple people or objects. It beneﬁts from combining pose estimation with PPE detection in one platform. Additionally, we propose the ﬁrst open source annotated data set with video data from industrial workplaces annotated with the action classiﬁcations and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.