Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Implication for Probabilistic Graphical Models

The graphical structure of Probabilistic Graphical Models (PGMs) represents the conditional independence (CI) relations that hold in the modeled distribution. Every separator in the graph represents a conditional independence relation in the distribution, making them the vehicle through which new conditional independencies are inferred and verified. The notion of separation in graphs depends on whether the graph is directed (i.e., a Bayesian Network), or undirected (i.e., a Markov Network). The premise of all current systems-of-inference for deriving CIs in PGMs, is that the set of CIs used for the construction of the PGM hold exactly. In practice, algorithms for extracting the structure of PGMs from data discover approximate CIs that do not hold exactly in the distribution. In this paper, we ask how the error in this set propagates to the inferred CIs read off the graphical structure. More precisely, what guarantee can we provide on the inferred CI when the set of CIs that entailed it hold only approximately? It has recently been shown that in the general case, no such guarantee can be provided. In this work, we prove new negative and positive results concerning this problem. We prove that separators in undirected PGMs do not necessarily represent approximate CIs. That is, no guarantee can be provided for CIs inferred from the structure of undirected graphs. We prove that such a guarantee exists for the set of CIs inferred in directed graphical models, making the $d$-separation algorithm a sound and complete system for inferring approximate CIs. We also establish improved approximation guarantees for independence relations derived from marginal and saturated CIs.
From Static to Dynamic: A Continual Learning Framework for Large Language Models

The vast number of parameters in large language models (LLMs) endows them with remarkable capabilities, allowing them to excel in a variety of natural language processing tasks. However, this complexity also presents challenges, making LLMs difficult to train and inhibiting their ability to continuously assimilate new knowledge, which may lead to inaccuracies in their outputs. To mitigate these issues, this paper presents DynaMind, a novel continual learning framework designed for LLMs. DynaMind incorporates memory mechanisms to assimilate new knowledge and modular operators to enhance the model inference process with the newly assimilated knowledge, consequently improving the accuracies of LLMs' outputs. Benchmark experiments demonstrate DynaMind's effectiveness in overcoming these challenges. The code and demo of DynaMind are available on GitHub: https://github.com/Elfsong/DynaMind.
Examining the Influence of Job Satisfaction on Individual Innovation and Its Components: Considering the Moderating Role of Technostress

Background: Employee innovation is a crucial aspect of organizations in the current era. Therefore, studying the factors influencing individual innovation is vital and unavoidable. Undoubtedly, job satisfaction is a significant variable in management sciences. Nowadays, all organizations are interconnected with technology. Objective: This research explores the relationship between job satisfaction and individual innovation, including its components, and the moderating role of technostress. Research Method: This study, in terms of purpose, is applied, and in terms of data collection method, it is a descriptive survey. Data collection tools included the Technostress Inventory by Tarafdar and colleagues (2007), Janssen's Individual Innovation Questionnaire (2000), and the Job Satisfaction Survey (JSS) by Spector (1994). The validity and reliability of these questionnaires were confirmed. The sample size for this study was 215, and data analysis was performed using SPSS and SMART-PLS software. Findings: Job satisfaction has a significant and positive relationship with individual innovation, idea generation, idea promotion, and idea implementation. Technostress moderates the relationship between job satisfaction and individual innovation, as well as idea generation and idea promotion. However, technostress does not play a moderating role in the relationship between job satisfaction and idea implementation. Conclusion: Based on the obtained results, organizations should take necessary measures to increase job satisfaction and reduce technostress among their employees.
Bi-Encoders based Species Normalization - Pairwise Sentence Learning to Rank

Motivation: Biomedical named-entity normalization involves connecting biomedical entities with distinct database identifiers in order to facilitate data integration across various fields of biology. Existing systems for biomedical named entity normalization heavily rely on dictionaries, manually created rules, and high-quality representative features such as lexical or morphological characteristics. However, recent research has investigated the use of neural network-based models to reduce dependence on dictionaries, manually crafted rules, and features. Despite these advancements, the performance of these models is still limited due to the lack of sufficiently large training datasets. These models have a tendency to overfit small training corpora and exhibit poor generalization when faced with previously unseen entities, necessitating the redesign of rules and features. Contribution: We present a novel deep learning approach for named entity normalization, treating it as a pair-wise learning to rank problem. Our method utilizes the widely-used information retrieval algorithm Best Matching 25 to generate candidate concepts, followed by the application of bi-directional encoder representation from the encoder (BERT) to re-rank the candidate list. Notably, our approach eliminates the need for feature-engineering or rule creation. We conduct experiments on species entity types and evaluate our method against state-of-the-art techniques using LINNAEUS and S800 biomedical corpora. Our proposed approach surpasses existing methods in linking entities to the NCBI taxonomy. To the best of our knowledge, there is no existing neural network-based approach for species normalization in the literature.
Smooth Number Message Authentication Code in the IoT Landscape

This paper presents the Smooth Number Message Authentication Code (SNMAC) for the context of lightweight IoT devices. The proposal is based on the use of smooth numbers in the field of cryptography, and investigates how one can use them to improve the security and performance of various algorithms or security constructs. The literature findings suggest that current IoT solutions are viable and promising, yet they should explore the potential usage of smooth numbers. The methodology involves several processes, including the design, implementation, and results evaluation. After introducing the algorithm, provides a detailed account of the experimental performance analysis of the SNMAC solution, showcasing its efficiency in real-world scenarios. Furthermore, the paper also explores the security aspects of the proposed SNMAC algorithm, offering valuable insights into its robustness and applicability for ensuring secure communication within IoT environments.
SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras

India is home to a multitude of languages of which 22 languages are recognised by the Indian Constitution as official. Building speech based applications for the Indian population is a difficult problem owing to limited data and the number of languages and accents to accommodate. To encourage the language technology community to build speech based applications in Indian languages, we are open sourcing SPRING-INX data which has about 2000 hours of legally sourced and manually transcribed speech data for ASR system building in Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi and Tamil. This endeavor is by SPRING Lab , Indian Institute of Technology Madras and is a part of National Language Translation Mission (NLTM), funded by the Indian Ministry of Electronics and Information Technology (MeitY), Government of India. We describe the data collection and data cleaning process along with the data statistics in this paper.
Valley polarization and photocurrent generation in transition metal dichalcogenide alloy 
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Monolayer transition metal dichalcogenides (TMDCs) constitute the core group of materials in the emerging semiconductor technology of valleytronics. While the coupled spin-valley physics of pristine TMDC materials and their heterstructures has been extensively investigated, less attention was given to TMDC alloys, which could be useful in optoelectronic applications due to the tunability of their band gaps. We report here our experimental investigations of the spin-valley physics of the monolayer and bilayer TMDC alloy, MoS$_{2x}$Se$_{2(1-x)}$, in terms of valley polarization and the generation as well as electrical control of a photocurrent utilising the circular photogalvanic effect. Piezoelectric force microscopy provides evidence for an internal electric field perpendicular to the alloy layer, thus breaking the out-of-plane mirror symmetry. The experimental observation is supported by first principles calculations based on the density functional theory. A comparison of the photocurrent device, based on the alloy material, is made with similar devices involving other TMDC materials.
Survey of Vector Database Management Systems

There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, that demand vast stores of unstructured data coupled with reliable, secure, fast, and scalable query processing capability. A variety of new data management techniques now exist for addressing these needs, however there is no comprehensive survey to thoroughly review these techniques and systems. We start by identifying five main obstacles to vector data management, namely vagueness of semantic similarity, large size of vectors, high cost of similarity comparison, lack of natural partitioning that can be used for indexing, and difficulty of efficiently answering hybrid queries that require both attributes and vectors. Overcoming these obstacles has led to new approaches to query processing, storage and indexing, and query optimization and execution. For query processing, a variety of similarity scores and query types are now well understood; for storage and indexing, techniques include vector compression, namely quantization, and partitioning based on randomization, learning partitioning, and navigable partitioning; for query optimization and execution, we describe new operators for hybrid queries, as well as techniques for plan enumeration, plan selection, and hardware accelerated execution. These techniques lead to a variety of VDBMSs across a spectrum of design and runtime characteristics, including native systems specialized for vectors and extended systems that incorporate vector capabilities into existing systems. We then discuss benchmarks, and finally we outline research challenges and point the direction for future work.
PulmoBell: Home-based Pulmonary Rehabilitation Assistive Technology for People with COPD

Chronic Obstructive Pulmonary Disease (COPD) can be fatal and is challenging to live with due to its severe symptoms. Pulmonary rehabilitation (PR) is one of the managements means to maintain COPD in a stable status. However, implementation of PR in the UK has been challenging due to the environmental and personal barriers faced by patients, which hinder their uptake, adherence, and completion of the programmes. Moreover, increased exercise capacity following PR does not always translate into physical activity (PA) and unfortunately, can lead back to exercise capacity seen prior to PR. Current alternative solutions using telerehabilitation methods have limitations on addressing these accessibility problems, and no clear conclusion can be drawn on the efficacy of telerehabilitation in enhancing the sustainability of PR outcomes via promoting PA in patients' everyday life. In this work, the authors propose a novel design of sensor-based assistive product with the aim of facilitating PR and promoting PA maintenance in a home-based setting. Prototypes of different levels of fidelity are presented, followed by an evaluation plan for future research directions.
Computational Approaches for Modeling Power Consumption on an Underwater Flapping Fin Propulsion System

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations. To optimize for different gait performance metrics, we develop a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, that is able to evaluate different fin designs and kinematics, and allow for comparison with other bio-inspired platforms. We create and train computational models using experimental data, and use these models to predict thrust and power under different fin operating states, providing efficiency profiles. We then use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. These comparisons provide a better understanding of how fin materials affect our thrust generation and propulsive efficiency, allowing us to inform control systems and weight for efficiency on an inverse gait-selector model.
Inferring Properties in Computation Tree Logic

We consider the problem of automatically inferring specifications in the branching-time logic, Computation Tree Logic (CTL), from a given system. Designing functional and usable specifications has always been one of the biggest challenges of formal methods. While in recent years, works have focused on automatically designing specifications in linear-time logics such as Linear Temporal Logic (LTL) and Signal Temporal Logic (STL), little attention has been given to branching-time logics despite its popularity in formal methods. We intend to infer concise (thus, interpretable) CTL formulas from a given finite state model of the system in consideration. However, inferring specification only from the given model (and, in general, from only positive examples) is an ill-posed problem. As a result, we infer a CTL formula that, along with being concise, is also language-minimal, meaning that it is rather specific to the given model. We design a counter-example guided algorithm to infer a concise and language-minimal CTL formula via the generation of undesirable models. In the process, we also develop, for the first time, a passive learning algorithm to infer CTL formulas from a set of desirable and undesirable Kripke structures. The passive learning algorithm involves encoding a popular CTL model-checking procedure in the Boolean Satisfiability problem.
Terahertz Induced Protein Interactions in a Random Medium

Folding of proteins into their correct native structure is key to their function. Simultaneously, the intricate interplay between cell movement and protein conformation highlights the complex nature of cellular processes. In this work, we demonstrate the impact of Terahertz (THz) signaling on controlling protein conformational changes in a random medium. Our system of interest consists of a communication link that involves a nanoantenna transmitter, a protein receiver, and a channel composed of moving red blood cells. Due to the system dynamics, we investigate the influence of both the fast and slow channel variations on protein folding. Specifically, we analyze the system’s selectivity to asses the effectiveness of the induced THz interaction in targeting a specific group of proteins under fading conditions. By optimizing the selectivity metric with respect to the nanoantenna power and frequency, it is possible to enhance the controllability of protein interactions. Our probabilistic analysis provides a new perspective regarding electromagnetically triggered protein molecules, their micro-environment and their interaction with surrounding particles. It helps elucidate how external conditions impact the protein folding kinetics and pathways. This results in not only understanding the mechanisms underlying THz-induced protein interactions but also engineering these still-emerging tools.
What is in Your App? Uncovering Privacy Risks of Female Health Applications

FemTech or Female Technology, is an expanding field dedicated to providing affordable and accessible healthcare solutions for women, prominently through Female Health Applications that monitor health and reproductive data. With the leading app exceeding 1 billion downloads, these applications are gaining widespread popularity. However, amidst contemporary challenges to women's reproductive rights and privacy, there is a noticeable lack of comprehensive studies on the security and privacy aspects of these applications. This exploratory study delves into the privacy risks associated with seven popular applications. Our initial quantitative static analysis reveals varied and potentially risky permissions and numerous third-party trackers. Additionally, a preliminary examination of privacy policies indicates non-compliance with fundamental data privacy principles. These early findings highlight a critical gap in establishing robust privacy and security safeguards for FemTech apps, especially significant in a climate where women's reproductive rights face escalating threats.
An Experimental Study of Model-based Control for Planar Handed Shearing Auxetics Robots

Parallel robots based on Handed Shearing Auxetics (HSAs) can implement complex motions using standard electric motors while maintaining the complete softness of the structure, thanks to specifically designed architected metamaterials. However, their control is especially challenging due to varying and coupled stiffness, shearing, non-affine terms in the actuation model, and underactuation. In this paper, we present a model-based control strategy for planar HSA robots enabling regulation in task space. We formulate equations of motion, show that they admit a collocated form, and design a P-satI-D feedback controller with compensation for elastic and gravitational forces. We experimentally identify and verify the proposed control strategy in closed loop.
An Inexact Frank-Wolfe Algorithm for Composite Convex Optimization Involving a Self-Concordant Function

In this paper, we consider Frank-Wolfe-based algorithms for composite convex optimization problems with objective involving a logarithmically-homogeneous, self-concordant functions. Recent Frank-Wolfe-based methods for this class of problems assume an oracle that returns exact solutions of a linearized subproblem. We relax this assumption and propose a variant of the Frank-Wolfe method with inexact oracle for this class of problems. We show that our inexact variant enjoys similar convergence guarantees to the exact case, while allowing considerably more flexibility in approximately solving the linearized subproblem. In particular, our approach can be applied if the subproblem can be solved prespecified additive error or to prespecified relative error (even though the optimal value of the subproblem may not be uniformly bounded). Furthermore, our approach can also handle the situation where the subproblem is solved via a randomized algorithm that fails with positive probability. Our inexact oracle model is motivated by certain large-scale semidefinite programs where the subproblem reduces to computing an extreme eigenvalue-eigenvector pair, and we demonstrate the practical performance of our algorithm with numerical experiments on problems of this form.
Redundancy-Adaptive Multimodal Learning for Imperfect Data

Multimodal models trained on complete modality data often exhibit a substantial decrease in performance when faced with imperfect data containing corruptions or missing modalities. To address this robustness challenge, prior methods have explored various approaches from aspects of augmentation, consistency or uncertainty, but these approaches come with associated drawbacks related to data complexity, representation, and learning, potentially diminishing their overall effectiveness. In response to these challenges, this study introduces a novel approach known as the Redundancy-Adaptive Multimodal Learning (RAML). RAML efficiently harnesses information redundancy across multiple modalities to combat the issues posed by imperfect data while remaining compatible with the complete modality. Specifically, RAML achieves redundancy-lossless information extraction through separate unimodal discriminative tasks and enforces a proper norm constraint on each unimodal feature representation. Furthermore, RAML explicitly enhances multimodal fusion by leveraging fine-grained redundancy among unimodal features to learn correspondences between corrupted and untainted information. Extensive experiments on various benchmark datasets under diverse conditions have consistently demonstrated that RAML outperforms state-of-the-art methods by a significant margin.
The Properties Of Mechanical Instability On Lumbar Spine At The Adjacent Segment After Rigid Fixation

Posterior spinal interbodyfusion (PLIF) and rigid fixation have been frequently undertaken for lumbar arhrodesis. The effect of rigid fixation pedicle screw instrumentation on biomechanical behaviors of adjacent segment instability has not been well documented. Seventeen fresh-frozen lamb lumbar spines (Sacrum-T12) were used for current study. The biomechanical tests were performed by using the axial compression testing machine. The axial compression was applied to all amputee legs with the loading speed of 5 mm/min (max. 400 Newton). A specially designed fixture used to increase moment up to 8400 Nmm generated through the axial movement of the actuator was applied to each specimen to achieve the flexion and extension motions, axial neutral compression, right and left bending, respectively were applied to specimens. All specimens were tested intact and after implantation with posterior pedicle screws and rod fixation. During testing, intervertebral displacement at decompression levels L5-S1 was recorded continuously by extensometer. While preparing test specimen, suitable gauge marks (diamond mark for high accuracy) were selected for the test specimen. Gauge marks were applied to test specimen with pins due to sliding surface of test specimen. Gauge marks were attached to L5 and S1 to measure the (superior-inferior) and (anterior-posterior) displacement. At implanted rod spines of adjacent segment (S1-L5) group, neutral vertical (superior-inferior) displacements were showed lower data than transverse (anterior-posterior) displacements. When we checked the statistical data, there was a numerical difference and there was significant change between the intact spine and adjacent segment of axial compression and extension position. Also we compare data of the transverse (anterior-posterior) displacements at the lumbar spine after rigid fixation of adjacent segment; adjacent segment was greater than intact spine. Rigid fixation of lumbar spine alters range of motion (ROM) of adjacent segment level. Therefore abnormal stress and hyper mobility on adjacent segment causes spinal instability, which can develop facet joint degeneration and low back pain.