Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room

Parametric sound field synthesis methods, such as the Spatial Decomposition Method (SDM) and Higher-Order Spatial Impulse Response Rendering (HO-SIRR), are widely used for the analysis and auralization of sound fields. This paper studies the performances of various sound field synthesis methods in the context of the auralization of a critical listening room. The influence on the perceived spatial and timbral fidelity of the following factors is considered: the rendering framework, direction of arrival (DOA) estimation method, microphone array structure, and use of a dedicated center reference microphone with SDM. Listening tests compare the synthesized sound fields to a reference binaural rendering condition. Several acoustic parameters are measured to gain insights into objective differences between methods. A high-quality pressure microphone improves the SDM framework's timbral fidelity. Additionally, SDM and HO-SIRR show similarities in spatial fidelity. Performance variation between SDM configurations is influenced by the DOA estimation method and microphone array construction. The binaural SDM (BSDM) presentations display temporal artifacts impacting sound quality.
Simulated TEM imaging of a heavily irradiated metal

We recast the Howie-Whelan equations for generating simulated transmission electron microscope (TEM) images, replacing the dependence on local atomic displacements with atomic positions only. This allows very rapid computation of simulated TEM images for arbitrarily complex atomistic configurations of lattice defects and dislocations in the dynamical two beam approximation. Large scale massively-overlapping cascade simulations performed with molecular dynamics, are used to generate representative high-dose nanoscale irradiation damage in tungsten at room temperature, and we compare the simulated TEM images to experimental TEM images with similar irradiation and imaging conditions. The simulated TEM shows 'white-dot' damage in weak-beam dark-field imaging conditions, in line with our experimental observations and as expected from previous studies, and in bright-field conditions a dislocation network is observed. In this work we can also compare the images to the nanoscale lattice defects in the original atomic structures, and find that at high dose the white spots are not only created by small dislocation loops, but rather arise from nanoscale fluctuations in strains around curved sections of dislocation lines.
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process

Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent methods can robustly identify these latent causal variables, they rely on strict assumptions about the invertible generation process from latent variables to observed data. However, these assumptions are often hard to satisfy in real-world applications containing information loss. For instance, the visual perception process translates a 3D space into 2D images, or the phenomenon of persistence of vision incorporates historical data into current perceptions. To address this challenge, we establish an identifiability theory that allows for the recovery of independent latent components even when they come from a nonlinear and non-invertible mix. Using this theory as a foundation, we propose a principled approach, CaRiNG, to learn the CAusal RepresentatIon of Non-invertible Generative temporal data with identifiability guarantees. Specifically, we utilize temporal context to recover lost latent information and apply the conditions in our theory to guide the training process. Through experiments conducted on synthetic datasets, we validate that our CaRiNG method reliably identifies the causal process, even when the generation process is non-invertible. Moreover, we demonstrate that our approach considerably improves temporal understanding and reasoning in practical applications.
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms

Euphemisms are found across the world’s languages, making them a universal linguistic phenomenon. As such, euphemistic data may have useful properties for computational tasks across languages. In this study, we explore this premise by training a multilingual transformer model (XLM-RoBERTa) to disambiguate potentially euphemistic terms (PETs) in multilingual and cross-lingual settings. In line with current trends, we demonstrate that zero-shot learning across languages takes place. We also show cases where multilingual models perform better on the task compared to monolingual models by a statistically significant margin, indicating that multilingual data presents additional opportunities for models to learn about cross-lingual, computational properties of euphemisms. In a follow-up analysis, we focus on universal euphemistic “categories” such as death and bodily functions among others. We test to see whether cross-lingual data of the same domain is more important than within-language data of other domains to further understand the nature of the cross-lingual transfer.
Effects of Climate Variability on Growths of Two Dominant Tree Species with Lower Canopy at the Sakaerat Environmental Research Station (SERS), Nakhon Ratchasima Province, Thailand

The objective of this research was to investigate effects of climate variability on monthly growths of Aglaia odoratissima and Hydnocarpus ilicifolia which were dominant tree species in lower canopies of dry evergreen forest at the Sakaerat Environmental Research Station (SERS), Nakhon Ratchasima province, Thailand. For a year of the investigation, wood anatomy was described and monthly data of leaf phenology, inside bark diameter (IBD) and outside bark diameter (OBD) increments were examined. These data were related to soil moisture content and climatic data of monthly rainfall, temperature and relative humidity. The results showed that leaf phenology of A. odoratissima and H. ilicifolia illustrated leaf maturation throughout the year, while young leaves were abundantly found in rainy season and leaf abscission rarely found throughout the year. IBD increments of these species on transverse surfaces could be detected throughout the year and the rapidest increments were found in rainy season, while OBD increments of both species shrank in dry season and swelled in rainy season. Using path analysis (PA), climate variability was significantly related to leaf phenology of A. odoratissima and IBD increments of H. ilicifolia. It was also significantly related to outside OBD increments of both species. Keywords: Leaf phenology / Outside bark diameter / Path analysis / Inside bark diameter / Aglaia odoratissima / Hydnocarpus ilicifolia
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation

As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attractive and engaging places. However, the accurate recommendation capability relies on the powerful server collecting a vast amount of users' historical check-in data, posing significant risks of privacy breaches. Although several collaborative learning (CL) frameworks for POI recommendation enhance recommendation resilience and allow users to keep personal data on-device, they still share personal knowledge to improve recommendation performance, thus leaving vulnerabilities for potential attackers. Given this, we design a new Physical Trajectory Inference Attack (PTIA) to expose users' historical trajectories. Specifically, for each user, we identify the set of interacted POIs by analyzing the aggregated information from the target POIs and their correlated POIs. We evaluate the effectiveness of PTIA on two real-world datasets across two types of decentralized CL frameworks for POI recommendation. Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories. Furthermore, Local Differential Privacy (LDP), the traditional privacy-preserving method for CL frameworks, has also been proven ineffective against PTIA. In light of this, we propose a novel defense mechanism (AGD) against PTIA based on an adversarial game to eliminate sensitive POIs and their information in correlated POIs. After conducting intensive experiments, AGD has been proven precise and practical, with minimal impact on recommendation performance.
Super-exponential quantum advantage for finding the center of a sphere

This article considers the geometric problem of finding the center of a sphere in vector space over finite fields, given samples of random points on the sphere. We propose a quantum algorithm based on continuous-time quantum walks that needs only a constant number of samples to find the center. We also prove that any classical algorithm for the same task requires approximately as many samples as the dimension of the vector space, by a reduction to an old and basic algebraic result -- Warning's second theorem. Thus, a super-exponential quantum advantage is revealed for the first time for a natural and intuitive geometric problem.
Generative Expressive Robot Behaviors using Large Language Models

People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying"excuse me"to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is adaptable and composable, building upon each other. Our approach utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github. io/.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications and the expectation of the broad public, even though the most powerful OpenAI's GPT-4 and Google's Gemini have been deployed. This paper strives to enhance understanding of the gap through the lens of a qualitative study on the generalizability, trustworthiness, and causal reasoning capabilities of recent proprietary and open-source MLLMs across four modalities: ie, text, code, image, and video, ultimately aiming to improve the transparency of MLLMs. We believe these properties are several representative factors that define the reliability of MLLMs, in supporting various downstream applications. To be specific, we evaluate the closed-source GPT-4 and Gemini and 6 open-source LLMs and MLLMs. Overall we evaluate 230 manually designed cases, where the qualitative results are then summarized into 12 scores (ie, 4 modalities times 3 properties). In total, we uncover 14 empirical findings that are useful to understand the capabilities and limitations of both proprietary and open-source MLLMs, towards more reliable downstream multi-modal applications.
Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings.
Decentralized Zeno-Free Event-Triggered Control For Multiple Networks Subject to Stochastic Network Delays and Poisson Pulsing Attacks

By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks. The time delay in the network denotes the length from a transmission instant to the corresponding update instant, and is supposed to be a continuous random variable subject to certain continuous probability distribution; while the attacks' cardinal number is a discrete random variable supposed to be subject to Poisson distribution, so the inter-attack time, i.e., the time between two consecutive attack instants, is subject to exponential distribution. The considered system is modeled as a stochastic hybrid formalism, where the randomness enters through the jump map into the reset value (the inter-attack time directly related) of each triggered strategy. By only sampling/transmitting state measurements when needed and simultaneously by taking the specific medium access protocols into account, the designed event-triggered strategies are synthesized in a state-based and decentralized form, which are robust (tolerable well) to stochastic network delays, under different tradeoff-conditions between the minimum inter-event times, maximum allowable delays (i.e., potentially tolerable delays) and the frequencies of attacks. Using stochastic hybrid tools to combine attack-active parts with attack-over parts, the designed triggered strategies, if designed well according to the actual system needs, can tolerate (be resilient to) the Pp-DoS attacks and stochastic network delays without jeopardizing the stability and Zeno-freeness.
Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.
Non-Relativistic M2-Branes and the AdS/CFT Correspondence

A non-relativistic limit of the AdS/CFT correspondence is studied in the context of M2-branes. On the field theory side this corresponds to a near-BPS limit of ABJM that localises onto solutions of Hitchin's equations. It is shown that the symmetries of the theory include an infinite-dimensional enhancement of the spatial symmetry algebra corresponding to time-dependent holomorphic transformations. Taking the limit of the gravitational dual splits the geometry into three 'large' directions and eight 'small' directions, which for the near-horizon limit of the M2-brane metric has the effect of reducing the $AdS_4$ factor to an $AdS_2$ factor. Evidence is presented that the duality is maintained after the limit.
Sliding Secure Symmetric Multilevel Diversity Coding

Symmetric multilevel diversity coding (SMDC) is a source coding problem where the independent sources are ordered according to their importance. It was shown that separately encoding independent sources (referred to as ``\textit{superposition coding}") is optimal. In this paper, we consider an $(L,s)$ \textit{sliding secure} SMDC problem with security priority, where each source $X_{\alpha}~(s\leq \alpha\leq L)$ is kept perfectly secure if no more than $\alpha-s$ encoders are accessible. The reconstruction requirements of the $L$ sources are the same as classical SMDC. A special case of an $(L,s)$ sliding secure SMDC problem that the first $s-1$ sources are constants is called the $(L,s)$ \textit{multilevel secret sharing} problem. For $s=1$, the two problems coincide, and we show that superposition coding is optimal. The rate regions for the $(3,2)$ problems are characterized. It is shown that superposition coding is suboptimal for both problems. The main idea that joint encoding can reduce coding rates is that we can use the previous source $X_{\alpha-1}$ as the secret key of $X_{\alpha}$. Based on this idea, we propose a coding scheme that achieves the minimum sum rate of the general $(L,s)$ multilevel secret sharing problem. Moreover, superposition coding of the $s$ sets of sources $X_1$, $X_2$, $\cdots$, $X_{s-1}$, $(X_s, X_{s+1}, \cdots, X_L)$ achieves the minimum sum rate of the general sliding secure SMDC problem.
Hold Tight: Identifying Behavioral Patterns During Prolonged Work in VR through Video Analysis

VR devices have recently been actively promoted as tools for knowledge workers and prior work has demonstrated that VR can support some knowledge worker tasks. However, only a few studies have explored the effects of prolonged use of VR such as a study observing 16 participants working in VR and a physical environment for one work-week each and reporting mainly on subjective feedback. As a nuanced understanding of participants' behavior in VR and how it evolves over time is still missing, we report on the results from an analysis of 559 hours of video material obtained in this prior study. Among other findings, we report that (1) the frequency of actions related to adjusting the headset reduced by 46% and the frequency of actions related to supporting the headset reduced by 42% over the five days; (2) the HMD was removed 31% less frequently over the five days but for 41% longer periods; (3) wearing an HMD is disruptive to normal patterns of eating and drinking, but not to social interactions, such as talking. The combined findings in this work demonstrate the value of long-term studies of deployed VR systems and can be used to inform the design of better, more ergonomic VR systems as tools for knowledge workers.
A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols in Mobile Networks

Due to an ever-increasing number of participants and new areas of application, the demands on mobile communications systems are continually increasing. In order to deliver higher data rates, enable mobility and guarantee QoS requirements of subscribers, these systems and the protocols used are becoming more complex. By using higher frequency spectrums, cells become smaller and more base stations have to be deployed. This leads to an increased number of handovers of user equipments between base stations in order to enable mobility, resulting in potentially more frequent radio link failures and rate reduction. The persistent switching between the same base stations, commonly referred to as"ping-pong", leads to a consistent reduction of data rates. In this work, we propose a method for handover optimization by using proximal policy optimization in mobile communications to learn an adaptive handover protocol. The resulting agent is highly flexible regarding different travelling speeds of user equipments, while outperforming the standard 5G NR handover protocol by 3GPP in terms of average data rate and number of radio link failures. Furthermore, the design of the proposed environment demonstrates remarkable accuracy, ensuring a fair comparison with the standard 3GPP protocol.
A Multilingual Paraphrasary of Multiwords

This paper introduces the novel concept of a Multilingual Paraphrasary addressing its need for paraphrasing and translation. The multilingual paraphrasary is an ongoing work carried out in compliance with the CLUE-Alignments, a set of linguistically informed multilingual alignments, comprising several categories of multiword units. The CLUE-Alignments set has all possible combinations between English, French, Portuguese, and Spanish parallel texts of the common test set of the Europarl corpus. The gold collection of the manually annotated CLUE-Alignments is a refined Gold-CLUE. The paper also presents the CLUE-Aligner tool 1 , developed to facilitate the alignment of the meaning and translation units in the bi-texts, including the alignment of non-contiguous units. Our approach benefits from the Logos Model for machine translation, namely the semantico-syntactic abstraction language SAL and the semantic table SemTab. Finally, the paper illustrates how the collected paraphrases are used in the paraphrase generation tool eS-PERTo 2 , developed for Portuguese, as part of a larger multilingual generation project involving paraphrasing and translation.
BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.