Miracle-dz/UniFAR-FTUs huggingface dataset:
Teleporting Windows to Improve Virtual Reality Cockpits

Virtual reality allows a user to be surrounded by multiple floating virtual screens or windows in a kind of cockpit. Such arrangements increase the user’s ability to monitor several windows and quickly switch between windows with rapid eye motions. However, users sitting in a physical chair may suffer from neck fatigue if they fixate an off-center window for long periods of time. We propose an interaction technique for quickly teleporting any window to the center of the workspace, allowing the user to fixate that window with their neck in a neutral position, and later dismiss the window which snaps back to its original position. We present an experimental comparison of (1) a Cockpit whose windows are fixed, (2) a Cockpit with Teleportation, allowing any window to be temporarily moved to the center, and (3) a condition similar to a single Desktop screen where only one window is visible at a time, and a key combination similar to Alt+Tab switches between windows. Teleportation resulted in less neck fatigue than Cockpit, as subjectively reported by users, and was also preferred by a majority of users. We subsequently present a prototype that sketches how to extend teleportation to multiple simultaneous windows.
Instability of preferences due to Covid-19 Crisis and emotions: a natural experiment from urban Burkina Faso

The salience of the first Covid-19 crisis over a well-identified period represents an unexpected and abrupt change in the environment. This study uses the onset of the Covid-19 crisis to empirically examine whether risk and time preferences change in response to this exogenous shock. We use an original panel dataset conducted in January 2020 (before any event) and June 2020 (after the removal of strong economic measures) among women working in the informal sector in Ouagadougou, Burkina Faso. We use individual fixed effects on a balanced panel of 853 women to isolate the specific causal effect of the Covid-19 crisis on variation in attitudes toward risk and time over these six months and rule out alternative explanations for differences in preferences. We demonstrate strong preference instability: risk aversion changed over the period in both the gain (13%) and loss (-47%) domains, while impatience increased by 9%. We also show that risk aversion (in both domains) is non-sensitive to actual impacts, but appears to be driven by economic fears and concerns related to the Covid-19 crisis. We also find that greater exposure to the media reinforces preference instability: the more informed the respondent is, the more their risk and time preferences vary. The same phenomenon is observed when their source of information comes from the government or from a social network (Facebook and WhatsApp).
Surrogate‐Model Assisted Plausibility‐Check, Calibration, and Posterior‐Distribution Evaluation of Subsurface‐Flow Models

Modern physics‐based subsurface‐flow models often require many parameters and computationally costly simulations. This prohibits traditional ensemble‐based conditioning. We expedite the calibration of such models by using surrogate/proxy models based on Gaussian Process Regression (GPR). In an iterative procedure, we use the proxy models to (a) estimate the direction of steepest descent, (b) propose only parameter combinations for full‐model runs that are likely to lead to plausible results, and (c) preselect proposed parameter combinations by their predicted performance. This method yields an ensemble of full‐model runs covering the full plausible parameter space, but at higher resolution close to the optimum. This is the basis for Markov‐Chain Monte Carlo (MCMC) simulations using GPR to estimate the posterior parameter distribution. We tested several variants of the scheme on a 3‐D variably‐saturated steady‐state subsurface‐flow model and compared it to a Neural Posterior Estimation (NPE) scheme, which requires samples of the prior distribution only. While the estimated posterior distributions of the two approaches were similar, the GPR‐based MCMC approach reproduced the data better than samples from the NPE‐based posterior distributions.
Understanding Physiological and Behavioral Characteristics Separately for High-Performance Video-Based Hand Gesture Authentication

Video-based hand gesture authentication is a challenging fine-grained spatiotemporal analysis task, which requires models with the ability to extract stable physiological and behavioral features from dynamic gestures. Inspired by biological studies, we present a physiological–behavioral characteristic understanding network (PB-Net) for hand gesture authentication. According to the properties of physiological and behavioral characteristics, we design two branches, the physiological branch (P-Branch) and the behavioral branch (B-Branch), as well as corresponding data-tailoring strategies for the PB-Net. The data-tailoring strategies can produce two customized videos for the two branches, which can facilitate the analyses of physiological and behavioral characteristics. Besides, the data-tailoring strategies can remove significant redundant information and thus can improve running efficiency. The P-Branch and B-Branch do not interfere with each other and focus on the distillation of physiological and behavioral features separately. Considering that the important degree of the physiological and behavioral features could be different and changeable, we devise an adaptive physiological–behavioral feature fusion (APBF) module to automatically assign appropriate weights for the two features and merge them together to obtain a more satisfactory identity feature. Finally, the rationality, validity, and superiority of the PB-Net are fully demonstrated by extensive ablation experiments and sufficient comparisons with 23 excellent video understanding networks on the SCUT-DHGA dataset. The code is available at https://github.com/SCUT-BIP-Lab/PB-Net.
A discriminative lexicon approach to word comprehension, production, and processing: Maltese plurals

Abstract:Comprehending and producing words is a natural process for human speakers. In linguistic theory, investigating this process formally and computationally is often done by focusing on forms only. By moving beyond the world of forms, we show in this study that the discriminative lexicon (DL) model—operating with word comprehension as a mapping of form onto meaning, and word production as a mapping of meaning onto form—generates accurate predictions about what meanings listeners understand and what forms speakers produce. Furthermore, we show that measures derived from the computational model are predictive for human reaction times. Although mathematically very simple, the linear mappings between form and meaning posited by our model are powerful enough to capture the complexity and productivity of a Semitic language with a complex hybrid morphological system.
BAC-NOMA for Secondary Transmission

Introducing backscatter (BAC) devices into a legacy non-orthogonal multiple access (NOMA) network greatly improves spectrum efficiency, which provides a promising solution for the combination of internet of things (IoT) and wireless networks. Deep learning (DL) as an emerging optimization tool gradually attracts people’s interest in wireless communication area. In this letter, a BAC-NOMA network is investigated, where a sum-rate maximization problem is formulated and the closed-form solution of backscattering coefficient is derived. The original problem is transformed and solved by a semi-definite relaxation (SDR) based algorithm and a learning based algorithm. The simulation results show that both algorithms have their own advantages and disadvantages and should be chosen wisely according to actual situations.
Fecal Coliform Concentrations in the Upper Cohansey River Watershed Predicted by Air Temperature, Discharge, and Land Use

The Upper Cohansey River Watershed in southwestern New Jersey has a history of being affected by fecal coliform bacteria (FC). A study was undertaken to investigate the environmental factors associated with FC concentration. For 44% of samples taken throughout the watershed in 2012–2013, FC concentration exceeded the benchmark value. FC levels were related to air temperature, river discharge, and land use in stream buffers. Human sources of FC had been eliminated following research results published in 2009. Results of the study reported in this article suggest the need to further investigate wildlife sources of FC and to implement additional mitigation actions.
A Copy-Move Image Forgery Detection Using Modified SURF Features and A-KAZE Detector

: Copy-move forgery detection (CMFD) is a well-known digital image forgery technique that is used in detecting forgery regions of images. The copy-move forgery in images occurs when a specific section of an image is attached to a new component of the same image to replicate the forged image parts as an original. The forgery appears to be realistic because even after being forged in the objective region, the image acquires all the basic qualities of the original image. Some post-processing functions, such as scaling or rotation invariant, noise handling, hybrid image manipulation, etc., limit the capability of CMFD models. To overcome these limitations, the features are extracted from the Hybrid feature extraction using modified speeded up robust feature (M-SURF) with accelerated KAZE (A-KAZE) feature description and detection algorithm. The main advantages of the SURF descriptor are: the SURF features are resistant to several post-processing attacks and use Laplacian of gaussian to differentiate background and foreground features clearly. The A-KAZE descriptor is robust in detecting scale-invariant of forged regions and poorly localized key points of objects. A-KAZE constructs the invariant scaling levels using nonlinear diffusion filtering. It smooths the image while preserving the edges and decreasing speckle noise. As a result, it captures the precise location of features during the feature extraction process. Following the feature extraction, the matched features are compared, and removed false matches by applying random sample consensus (RANSAC) technique. The performance evaluation of the proposed Hybrid MSURF with A-KAZE CMFD method is validated on MICC-F220, MICC-F2000, CoMoFoD datasets and surpasses the existing CMFD methods. The proposed CMFD achieved better results compared to existing methods such as when CMFD based on DHE-SURF features along with mDBSCAN clustering and CMFD based on SURF, BRISK features along with DBSCAN clustering measured in terms of Precision of 99.93%, Accuracy of 99.46%, F1-score of 98.21%, true positive rate (TPR) of 99.93%, and false positive rate (FPR) of 4.67%.
Tournament Fixing Parameterized by Feedback Vertex Set Number Is FPT

A knockout (or single-elimination) tournament is a format of a competition that is very popular in practice (particularly in sports, elections and decision making), and which has been extensively and intensively studied from a theoretical point of view for more than a decade. Particular attention has been devoted to the Tournament Fixing problem, where, roughly speaking, the objective is to determine whether we can conduct the knockout tournament in a way that makes our favorite player win. Here, part of the input is a tournament graph D that encodes the winner of each possible match. A sequence of papers has studied the parameterized complexity of Tournament Fixing with respect to the feedback arc set number (fas) of D Given that this parameter yielded tractability, it has been asked explicitly and repeatedly whether Tournament Fixing is FPT also with respect to the feedback vertex set number (fvs) of D. We answer this question positively. In fact, although fvs can be arbitrarily smaller than fas, we attain the same dependency on the parameter in the time complexity. So, additionally, our work subsumes the best known algorithm for Tournament Fixing with respect to as.
In Vitro Antiprotozoal Activity of Hibiscus sabdariffa Extract against a Ciliate Causing High Mortalities in Turbot Aquaculture

Simple Summary The in vitro antiprotozoal activity of Hibiscus sabdariffa ethanolic extract was evaluated on the ciliate parasite Philasterides dicentrarchi, which causes severe losses in turbot aquaculture. Our results showed that H. sabdariffa extract had successfully inhibited the parasite population growth rate (IC50 = 1.57 mg mL−1), and caused significant changes in the activity of antioxidant enzymes (LOEC = 0.22 mg mL−1), especially glutathione peroxidase, total glutathione, and catalase. The activity of proteases (virulence factors) was also inhibited (IC50 = 0.76 mg mL−1), and the gene expression of catepsin 90 and leishmanolysin proteases was downregulated. Organic acids and phenolic phytochemicals in hibiscus extract are potentially responsible for the antiprotozoal bioactivity herein determined. Therefore, H. sabdariffa extract can be a promising disease-control alternative against the ciliate proliferation, cellular defense mechanisms, and pathogenicity. Abstract Philasterides dicentrarchi is an histophagous parasite that infects flatfish, namely turbot (Scophthalmus maximus), and cause significant losses in aquaculture units. The available measures for P. dicentrarchi control have limited efficiency, and some cause harm to fish. Hence, sustainable and natural control strategies are urgently needed. This study evaluated the in vitro bioactivity of the ethanol extract of Hibiscus sabdariffa calyces on P. dicentrarchi population growth rate (PGR), oxidative stress biomarkers (glutathione-S-transferases (GST), glutathione reductase (GR), glutathione peroxidase (GPx), total glutathione (TG) and catalase (CAT), neurotoxicity (acetylcholinesterase, AChE), activity and gene expression of proteases as major virulence factors. H. sabdariffa extract inhibited parasite PGR (IC50 = 1.57 mg mL−1), and caused significant changes in the activity of antioxidant enzymes (LOEC = 0.22 mg mL−1), especially GPx, TG, and CAT. The activity of proteases was also severely inhibited (IC50 = 0.76 mg mL−1), and gene expression of catepsin 90 and leishmanolysin proteases was downregulated. Organic acids and phenolic phytochemicals in hibiscus extract are potentially responsible for the antiprotozoal bioactivity herein determined. Therefore, H. sabdariffa extract can be a promising disease-control alternative against the ciliate proliferation, cellular defense mechanisms and pathogenicity. Still, its applicability in aquaculture settings, and potential effects on farmed fish, should be further elucidated.
Using Planning to Improve Semantic Parsing of Instructional Texts

We develop a symbolic planning-based decoder to improve the few-shot semantic parsing of instructional texts. The system takes long-form instructional texts as input and produces sequences of actions in a formal language that enable execution of the instructions. This task poses unique challenges since input texts may contain long context dependencies and ambiguous and domain-specific language. Valid semantic parses also require sequences of steps that constitute an executable plan. We build on recent progress in semantic parsing by leveraging large language models to learn parsers from small amounts of training data. During decoding, our method employs planning methods and domain information to rank and correct candidate parses. To validate our method, we evaluate on four domains: two household instruction-following domains and two cooking recipe interpretation domains. We present results for few-shot semantic parsing using leave-one-out cross-validation. We show that utilizing planning domain information improves the quality of generated plans. Through ablations we also explore the effects of our decoder design choices.
T OWARDS ANTIGENIC PEPTIDE DISCOVERY WITH BETTER MHC - I BINDING PREDICTION AND IMPROVED BENCHMARK METHODOLOGY

The Major Histocompatibility Complex (MHC) is a crucial component of the cellular immune system in vertebrates, responsible for, among others, presenting peptides derived from intracellular proteins. The MHC-I presentation is vital in the immune response and holds great promise in vaccine development and cancer immunotherapy. In this study, we analyze the limitations of existing methods and benchmarks for MHC-I presentation. We introduce a new benchmark to measure crucial generalization properties and models’ reliability on unseen MHC molecules and peptides. Finally, we present HLABERT , a pre-trained language model which significantly surpasses prior methods on our benchmark and also sets new state-of-the-art on the old benchmarks.
Designing More “Human-like” Algorithms: A computational complexity perspective

We encounter and solve a variety of algorithmic tasks in our daily lives without intentionally formalizing the underlying algorithm, such as sorting through a pile of laundry, remembering long numerical sequences (e.g. social security numbers), and creating schedules under a given set of constraints. In fact, such examples often inspired well-known problems in theoretical computer science – for example, the “traveling salesman problem” (an NP-hard problem) phrase was first mentioned in a 1832 German handbook describing a 47-city tour through Germany for a salesman that does not visit any city more than once (Applegate et al., 2006). However, while we may often wish to solve such tasks optimally, humans are not always capable of analyzing and discovering the optimal solution to an algorithmic task. Instead, we tend to develop heuristics or rely on instructions from family and friends to guide our strategies, and are also faced with limitations (perceptual, memory, etc.) that either alter the overall task complexity or lead to suboptimal behavior. A diverse set of works have attempted to discuss the links between human strategies when solving a given task and its computational complexity. Such studies explore a variety of modalities, ranging from perceptual tasks (e.g. designing compact frequency-based word clouds) to decisionmaking (e.g. choosing a path to travel on) to social behavior (e.g. forming friendships and alliances), and often seek to answer one of the following questions:
Enlightened Imagery: Multi-modal Image Captioning with Transformer-Based unified architecture

Image captioning represents a highly captivating yet challenging multimodal task that bridges two of the most extensively researched areas in artificial intelligence: vision and language. The In recent years, there has been a surge of interest in the field of image captioning. The primary objective of this project is to construct an image captioning model capable of generating accurate and natural language captions, effectively describing various situations across a wide array of samples. The motivation behind this work lies in the potential applications of such models in providing information about image content, which would otherwise be inaccessible to individuals with visual impairments. For example, this technology could be employed in the development of mobile applications that enable users with visual impairments to capture images using their smartphones and subsequently receive spoken descriptions of the image content. This, in turn, emphasizes the importance of advancing image captioning capabilities to create more effective and accessible solutions for the visually impaired.I used CNN-LSTM architecture as the baseline for this project. The novel architecture proposed in this paper is a unified architecture that employ transformers as the encoder and decoder. This enables the model to extract rich visual representation using ViT (Vision transformer) and combine it with the state of the art BERT model for the natural language processing. This unified model managed to achieve a 70% accuracy on flickr8k test set with a BLEU score of 20.
Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation

In recent years, knowledge distillation has become a cornerstone of efficiently deployed machine learning, with labs and industries using knowledge distillation to train models that are inexpensive and resource-optimized. Trojan attacks have contemporaneously gained significant prominence, revealing fundamental vulnerabilities in deep learning models. Given the widespread use of knowledge distillation, in this work we seek to exploit the unlabelled data knowledge distillation process to embed Trojans in a student model without introducing conspicuous behavior in the teacher. We ultimately devise a Trojan attack that effectively reduces student accuracy, does not alter teacher performance, and is efficiently constructible in practice.
Myrf Represses Nodal Signaling Via Furin During Left-Right Patterning And Gastrulation

Congenital heart disease (CHD) is the most common major birth defect in children, affecting nearly 3% of children and is the leading cause of infant mortality [1]. Heterotaxy (Htx) is a disorder of left-right (LR) patterning, in which organs, including the heart, are mispatterned relative to the LR axis. Htx is associated with severe forms of CHD, but its genetic causes remain largely undefined [2]. Exome sequencing studies of large patient cohorts have recently revealed hundreds of CHD/Htx candidate disease genes, which now need to be validated in experimental systems for causality in order to better understand the causes and biological mechanisms of CHD/Htx. Recently, the membrane-bound transcription factor MYRF has emerged as a candidate CHD/Htx gene in 14 patients with 13 unique variants. This is intriguing, as MYRF has an established role in the generation and maintenance of myelin in the central nervous system, but no known functional role in LR patterning or cardiac development. Here, we show that myrf is essential for LR patterning and gastrulation in Xenopus. Our data suggests both the LR patterning and delayed gastrulation phenotype are due to an upregulation of Nodal signaling indicating that myrf normally suppresses nodal. Searching for candidate genes downstream of MYRF, we identified the proprotein convertase FURIN as a potential target. Our results demonstrate that myrf suppresses furin transcription, which affects Nodal signaling via furin’s role in nodal ligand processing. Together, we provide a plausible mechanism for LR patterning defects and congenital heart disease in patients with MYRF variants. In doing so, we hope to better elucidate the molecular mechanisms of cardiac development and LR patterning and understand how these variants lead to disease in order to eventually provide genetic counseling, prognosis and therapeutics for patients with this rare disease.
BERT With Multitask Fine-Tuning and Loss Construction

Bidirectional Encoder Representations from Transformers, or BERT, is a transformer-based model that generates contextual word representations, that can then be adapted for use on various natural language processing (NLP) tasks. In this study, our goal is to implement a minimalist BERT model to perform sentence-level tasks simultaneously, including sentiment analysis, paraphrase detection, and semantic textual similarity (STS). To approach the problem, we conduct several experiments using BERT embeddings in combination with various techniques leveraged from concepts including similarity task fine-tuning, multitask loss construction, and round robin sampling procedures. Through our experimentation, we found that using a weighted multitask loss function, in conjunction with combined similarity task fine-tuning, improved BERT’s performance across the three tasks. We achieve test performance of 0.526 accuracy for sentiment classification, 0.861 accuracy for paraphrase detection, and a 0.775 Pearson correlation coefficient for STS.