Miracle-dz/UniFAR-FTUs huggingface dataset:
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference

Privacy-preserving neural networks have attracted increasing attention in recent years, and various algorithms have been developed to keep the balance between accuracy, computational complexity and information security from the cryptographic view. This work takes a different view from the input data and structure of neural networks. We decompose the input data (e.g., some images) into sensitive and insensitive segments according to importance and privacy. The sensitive segment includes some important and private information such as human faces and we take strong homomorphic encryption to keep security, whereas the insensitive one contains some background and we add perturbations. We propose the bi-CryptoNets, i.e., plaintext and ciphertext branches, to deal with two segments, respectively, and ciphertext branch could utilize the information from plaintext branch by unidirectional connections. We adopt knowledge distillation for our bi-CryptoNets by transferring representations from a well-trained teacher neural network. Empirical studies show the effectiveness and decrease of inference latency for our bi-CryptoNets.
Quantum Griffiths singularity in three-dimensional MoTiN superconducting films

Quantum Griffiths singularity (QGS) has been experimentally observed in a range of two-dimensional (2D) superconducting systems. Although it is theoretically suggested that the QGS also exists in three-dimensional (3D) superconductors, there is almost no experimental support to the theoretical prediction. In the present paper, we observe the occurrence of QGS in a series of $\sim$80-nm-thick Mo$_{0.8}$Ti$_{0.2}$N$_x$ ($0.84 \lesssim x \lesssim 1.12$) superconducting films near the field-driven superconductor-metal transition (SMT). These films have a NaCl structure and are 3D with respect to the superconductivity. For each film, the low-temperature magnetoresistance isotherms, measured at magnetic fields being perpendicular or parallel to the film plane, do not cross at a single point but at a clear wide region. The dynamical critical exponents $z\nu_{\perp}$ (for perpendicular field) and $z\nu_{\parallel}$ (for parallel field) obtained by analyzing the related magnetoresistance isotherms increase with decreasing temperature and tend to diverge as $T\rightarrow 0$ K. In addition, the effective resistivity data for the perpendicular and parallel field in the vicinity of the SMTs both obey an activated scaling based on the random transverse-field Ising model. We also fabricate a $\sim$80-nm-thick (Mo$_{0.8}$Ti$_{0.2}$)$_2$N$_{1.06}$ superconducting film with face-centered cubic structure at low nitrogen partial pressure. It is found that the low-temperature magnetoresistance isotherms for the perpendicular (parallel) field cross at a single point and the resistivity data for the perpendicular (parallel) field in the vicinity of the field-induced SMT obey the power-law scaling deduced from the dirty-boson model. Our results provide unambigous experimental evidence for the existence of QGS in 3D superconductors.
Mapping Acceptance: Assessing Emerging Technologies and Concepts through Micro Scenarios

As technology evolves rapidly, understanding public perception becomes increasingly crucial. This article introduces an integrative method for evaluating mental models and social acceptance of various technologies. Our approach utilizes micro scenarios coupled with visual-spatial mapping, offering a comprehensive perspective that contrasts with traditional methods focused on detailed assessments of limited scenarios. This methodology allows for simultaneous quantitative evaluation of multiple technologies on visio-spatial maps, facilitating a comparative ranking based on diverse criteria and an exploration of the interplay between individual factors and technology attributes in shaping public opinion. Our approach provides a framework for researchers and policymakers to gauge critical issues and to identify factors pivotal to acceptance. We illustrate this methodology with examples from our research, offering practical guidelines and R code to enable others in conducting similar studies. This paper aims to bridge the gap between technological advancement and societal perception, offering a tool for more informed decision-making in the realm of technology development and policy.
Estimating Max-Stable Random Vectors with Discrete Spectral Measure using Model-Based Clustering

This study introduces a novel estimation method for the entries and structure of a matrix $A$ in the linear factor model $\textbf{X} = A\textbf{Z} + \textbf{E}$. This is applied to an observable vector $\textbf{X} \in \mathbb{R}^d$ with $\textbf{Z} \in \mathbb{R}^K$, a vector composed of independently regularly varying random variables, and light-tailed independent noise $\textbf{E} \in \mathbb{R}^d$. This leads to max-linear models treated in classical multivariate extreme value theory. The spectral measure of the limit distribution is subsequently discrete and completely characterized by the matrix $A$. Every max-stable random vector with discrete spectral measure can be written as a max-linear model. Each row of the matrix $A$ is both scaled and sparse. Additionally, the value of $K$ is not known a priori. The problem of identifying the matrix $A$ from its matrix of pairwise extremal correlation is addressed. In the presence of pure variables, which are elements of $\textbf{X}$ linked, through $A$, to a single latent factor, the matrix $A$ can be reconstructed from the extremal correlation matrix. Our proofs of identifiability are constructive and pave the way for our innovative estimation for determining the number of factors $K$ and the matrix $A$ from $n$ weakly dependent observations on $\textbf{X}$. We apply the suggested method to weekly maxima rainfall and wildfires to demonstrate its applicability.
Code-Switched Language Identification is Harder Than You Think

Code switching (CS) is a very common phenomenon in written and spoken communication, but is handled poorly by many NLP applications. Looking to the application of building CS corpora, we explore CS language identification for corpus building. We make the task more realistic by scaling it to more languages and considering models with simpler architectures for faster inference. We also reformulate the task as a sentence-level multi-label tagging problem to make it more tractable. Having defined the task, we investigate three reasonable architectures for this task and define metrics which better reflect desired performance. We present empirical evidence that no current approach is adequate, and finally provide recommendations for future work in this area.
Critic-Actor for Average Reward MDPs with Function Approximation: A Finite-Time Analysis

In recent years, there has been a lot of research work activity focused on carrying out asymptotic and non-asymptotic convergence analyses for two-timescale actor critic algorithms where the actor updates are performed on a timescale that is slower than that of the critic. In a recent work, the critic-actor algorithm has been presented for the infinite horizon discounted cost setting in the look-up table case where the timescales of the actor and the critic are reversed and asymptotic convergence analysis has been presented. In our work, we present the first critic-actor algorithm with function approximation and in the long-run average reward setting and present the first finite-time (non-asymptotic) analysis of such a scheme. We obtain optimal learning rates and prove that our algorithm achieves a sample complexity of $\mathcal{\tilde{O}}(\epsilon^{-2.08})$ for the mean squared error of the critic to be upper bounded by $\epsilon$ which is better than the one obtained for actor-critic in a similar setting. We also show the results of numerical experiments on three benchmark settings and observe that the critic-actor algorithm competes well with the actor-critic algorithm.
Shifts on trees versus classical shifts in chain recurrence

We construct continuous (and even invertible) linear operators acting on Banach (even Hilbert) spaces whose restrictions to their respective closed linear subspaces of chain recurrent vectors are not chain recurrent operators. This construction completely solves in the negative a problem posed by Nilson C. Bernardes Jr. and Alfred Peris on chain recurrence in Linear Dynamics. In particular: we show that the non-invertible case can be directly solved via relatively simple weighted backward shifts acting on certain unrooted directed trees; then we modify the non-invertible counterexample to address the invertible case, but falling outside the class of weighted shift operators; and we finally show that this behaviour cannot be achieved via classical (unilateral neither bilateral) weighted backward sifts (acting on $\mathbb{N}$ and $\mathbb{Z}$ respectively) by noticing that a classical shift is a chain recurrent operator whenever it admits a non-zero chain recurrent vector.
LIR: A Lightweight Baseline for Image Restoration

Recently, there have been significant advancements in Image Restoration based on CNN and transformer. However, the inherent characteristics of the Image Restoration task are often overlooked. Many works, instead, only focus on the basic block design and stack numerous such blocks to the model, leading to parameters redundant and computations unnecessary. Thus, the efficiency of the image restoration is hindered. In this paper, we propose a Lightweight Baseline for Image Restoration called LIR to efficiently reconstruct the image and remove degradations (blur, rain, noise, haze). First of all, LIR addresses the degradations existing in the local and global residual connections that are ignored by modern networks, through a simple structural design. Then, to achieve lightweight, a Lightweight Adaptive Attention (LAA) Block is introduced depending on the inherent characteristics of the Image Restoration, which is mainly composed of proposed Adaptive Filters and Attention Blocks. LAA is capable of adaptively sharpening contours, removing degradation, and capturing global information in various Image Restoration scenes in a computation-friendly manner. Extensive experiments demonstrate that our LIR achieves comparable performance to state-of-the-art models with fewer parameters and computations in certain tasks. In addition, it is worth noting that our LIR produces better visual results than state-of-the-art networks that are more in line with the human aesthetic.
Spatial-Sign based Maxsum Test for High Dimensional Location Parameters

In this study, we explore a robust testing procedure for the high-dimensional location parameters testing problem. Initially, we introduce a spatial-sign based max-type test statistic, which exhibits excellent performance for sparse alternatives. Subsequently, we demonstrate the asymptotic independence between this max-type test statistic and the spatial-sign based sum-type test statistic (Feng and Sun, 2016). Building on this, we propose a spatial-sign based max-sum type testing procedure, which shows remarkable performance under varying signal sparsity. Our simulation studies underscore the superior performance of the procedures we propose.
Federated Unlearning: a Perspective of Stability and Fairness

This paper explores the multifaceted consequences of federated unlearning (FU) with data heterogeneity. We introduce key metrics for FU assessment, concentrating on verification, global stability, and local fairness, and investigate the inherent trade-offs. Furthermore, we formulate the unlearning process with data heterogeneity through an optimization framework. Our key contribution lies in a comprehensive theoretical analysis of the trade-offs in FU and provides insights into data heterogeneity's impacts on FU. Leveraging these insights, we propose FU mechanisms to manage the trade-offs, guiding further development for FU mechanisms. We empirically validate that our FU mechanisms effectively balance trade-offs, confirming insights derived from our theoretical analysis.
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data

Self-supervised learning (SSL) has been incorporated into many state-of-the-art models in various domains, where SSL defines pretext tasks based on unlabeled datasets to learn contextualized and robust representations. Recently, SSL has been a new trend in exploring the representation learning capability in the realm of tabular data, which is more challenging due to not having explicit relations for learning descriptive representations. This survey aims to systematically review and summarize the recent progress and challenges of SSL for non-sequential tabular data (SSL4NS-TD). We first present a formal definition of NS-TD and clarify its correlation to related studies. Then, these approaches are categorized into three groups -- predictive learning, contrastive learning, and hybrid learning, with their motivations and strengths of representative methods within each direction. On top of this, application issues of SSL4NS-TD are presented, including automatic data engineering, cross-table transferability, and domain knowledge integration. In addition, we elaborate on existing benchmarks and datasets for NS-TD applications to discuss the performance of existing tabular models. Finally, we discuss the challenges of SSL4NS-TD and provide potential directions for future research. We expect our work to be useful in terms of encouraging more research on lowering the barrier to entry SSL for the tabular domain and improving the foundations for implicit tabular data.
Nonconventional screening of Coulomb interaction in two-dimensional semiconductors and metals: A comprehensive cRPA study of MX2 (M=Mo, W, Nb, Ta; X=S, Se, Te)

Experimental observations of large exciton binding energies and non-hydrogenic Rydberg series in 2D semiconducting TMDs, along with deviations in plasmon dispersion in 2D metallic TMDs, suggest the presence of a nonconventional screening of the Coulomb interaction. The experimentally observed Mott insulating state in the charge density wave (CDW) reconstructed lattice of TMDs containing 4d and 5d elements further confirms the presence of strong Coulomb interactions in these systems. In this study, we use first-principles electronic structure calculations and constrained random-phase approximation to calculate the Coulomb interaction parameters (partially screened U and fully screened W) between localized $d$ electrons in 2D TMDs. We specifically explore materials represented by the formula MX2 (M=Nb, Ta, Mo, W, and X=S, Se, Te) and consider three different phases (1H, 1T, and 1T'). Our results show that the short-range interactions are strongly screened in all three phases, whereas the long-range interactions remain significant even in metallic systems. This nonconventional screening provides a compelling explanation for the deviations observed in the usual hydrogenic Rydberg series and conventional plasmon dispersion in 2D semiconducting and metallic TMDs, respectively. Our calculations yield on-site Coulomb interaction parameters U within the ranges of 0.8-2.5 eV, 0.8-1.9 eV, and 0.9-2.4 eV for the 1H, 1T, and 1T' structures, respectively. Furthermore, our findings indicate a substantially high ratio of on-site effective Coulomb interaction to bandwidth (U_eff/W_b>>1) in CDW TMDs, providing robust evidence for the experimentally observed strongly correlated Mott phase.
TransFR: Transferable Federated Recommendation with Pre-trained Language Models

Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent architecture for privacy-preserving recommendations. In conventional FRs, a dominant paradigm is to utilize discrete identities to represent users/clients and items, which are subsequently mapped to domain-specific embeddings to participate in model training. Despite considerable performance, we reveal three inherent limitations that can not be ignored in federated settings, i.e., non-transferability across domains, unavailability in cold-start settings, and potential privacy violations during federated training. To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data. Specifically, it first learns domain-agnostic representations of items by exploiting pre-trained models with public textual corpora. To tailor for federated recommendation, we further introduce an efficient federated fine-tuning and a local training mechanism. This facilitates personalized local heads for each client by utilizing their private behavior data. By incorporating pre-training and fine-tuning within FRs, it greatly improves the adaptation efficiency transferring to a new domain and the generalization capacity to address cold-start issues. Through extensive experiments on several datasets, we demonstrate that our TransFR model surpasses several state-of-the-art FRs in terms of accuracy, transferability, and privacy.
Supervised Algorithmic Fairness in Distribution Shifts: A Survey

Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains. In real-world applications, machine learning models are often trained on a specific dataset but deployed in environments where the data distribution may shift over time due to various factors. This shift can lead to unfair predictions, disproportionately affecting certain groups characterized by sensitive attributes, such as race and gender. In this survey, we provide a summary of various types of distribution shifts and comprehensively investigate existing methods based on these shifts, highlighting six commonly used approaches in the literature. Additionally, this survey lists publicly available datasets and evaluation metrics for empirical studies. We further explore the interconnection with related research fields, discuss the significant challenges, and identify potential directions for future studies.
Impact of snowfall on terahertz channel performance: measurement and modeling insights

In the evolving domain of wireless communication, the investigation on terahertz (THz) frequency spectrum, spanning 0.1 to 10 THz, has become a critical focus for advancing ultra-high-speed data transmission technologies. The effective deployment of THz wireless communication techniques mandates a complete study of channel performance under various atmospheric conditions, such as rain, fog, cloud, haze, and notably, snow. These environmental elements significantly impact the design of the protocol stack, ranging from physical-layer signal processing to application design and strategic network planning. An in-depth understanding of channel propagation and fading characteristics in real-world environments, especially over ultra-wide bandwidths, is crucial. This work presents a comprehensive measurement-based and theoretical investigation of line-of-sight (LoS) THz channel performance in snowy conditions. It methodically examines both the empirical and predicted aspects of channel power and bit-error-ratio (BER). The effects of snowfall rate, carrier frequency, ambient temperature, and relative humidity on channel performance are analyzed and discussed. Our findings demonstrate that snowy conditions not only amplify power loss but also induce rapid fluctuations in the power levels of the THz channel. Notably, our results reveal an absence of significant multipath effects in these scenarios. This insight highlights the need for further research into the dynamics of snowflake movement and their interaction with THz transmission paths.
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation

Generating natural language text from graph-structured data is essential for conversational information seeking. Semantic triples derived from knowledge graphs can serve as a valuable source for grounding responses from conversational agents by providing a factual basis for the information they communicate. This is especially relevant in the context of large language models, which offer great potential for conversational interaction but are prone to hallucinating, omitting, or producing conflicting information. In this study, we conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples. We compare four large language models of varying sizes with different prompting techniques. Through a series of benchmark experiments on the WebNLG dataset, we analyze the models’ performance and identify the most common issues in the generated predictions. Our findings show that the capabilities of large language models in triple verbalization can be significantly improved through few-shot prompting, post-processing, and efficient fine-tuning techniques, particularly for smaller models that exhibit lower zero-shot performance.
Deep learning path-like collective variable for enhanced sampling molecular dynamics

Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. Existing variables that describe the progression along a reactive pathway offer an elegant solution but face a number of limitations. In this paper, we address these challenges by introducing a new path-like collective variable called the `Deep-locally-non-linear-embedding', which is inspired by principles of the locally linear embedding technique and is trained on a reactive trajectory. The variable mimics the ideal reaction coordinate by automatically generating a non-linear combination of features through a differentiable generalized autoencoder that combines a neural network with a continuous k-nearest-neighbor selection. Among the key advantages of this method is its capability to automatically choose the metric for searching neighbors and to learn the path from state A to state B without the need to handpick landmarks a priori. We demonstrate the effectiveness of DeepLNE by showing that the progression along the path variable closely approximates the ideal reaction coordinate in toy models such as the M\"uller-Brown-potential and alanine dipeptide. We then use it in molecular dynamics simulations of an RNA tetraloop, where we highlight its capability to accelerate transitions and converge the free energy of folding.
Pseudoredundancy for the Bit-Flipping Algorithm

The analysis of the decoding failure rate of the bit-flipping algorithm has received increasing attention. For a binary linear code we consider the minimum number of rows in a parity-check matrix such that the bit-flipping algorithm is able to correct errors up to the minimum distance without any decoding failures. We initiate a study of this bit-flipping redundancy, which is akin to the stopping set, trapping set or pseudocodeword redundancy of binary linear codes, and focus in particular on codes based on finite geometries.