Miracle-dz/UniFAR-FTUs huggingface dataset:
HaVAT: Automatic Cluster Structure Assessment in Unlabeled data

Clustering algorithms often rely on the input of the desired number of clusters, denoted as "k," to partition the data. However, a crucial question arises: do the data truly exhibit clusters, and if so, how many? This is known as clustering tendency assessment. Many variants of VAT have been proposed recently, and this family of algorithms called Visual Assessment of Clustering Tendency (VAT) has gained popularity among researchers in various domains. These algorithms aim to visually estimate the cluster structure and the number of clusters in the input dataset by reordering the pairwise dissimilarity matrix and creating a grayscale image called the reordered dissimilarity matrix image (RDI). Dark blocks in the RDI, representing pixels with low dissimilarity values, visually indicate potential clusters within the data. Although the VAT family of algorithms has proven valuable for estimating clusters in diverse datasets, manually interpreting the output, particularly with overlapping clusters or complex geometries, can be challenging. In this paper, we propose HaVAT, a novel method based on the Hough transform, to automate the interpretation of the RDI. Additionally, HaVAT can automatically determine the cluster hierarchy and obtain the optimal partition as part of the automated assessment based on a novel scoring mechanism. Our experiments on various datasets demonstrate HaVAT’s effectiveness and superiority over state-of-the-art methods in estimating cluster structure in terms of hierarchy and number of clusters.
Multiforecast-based Early Anomaly Detection for Spacecraft Health Monitoring

Early detection of impending anomalies is a strong desirable for spacecraft operation as it can allow preemptive action to safeguard the mission objectives. Methods abound for just-in-time anomaly detection but early detection is a much more sought after goal. In this paper, we present MEND, a simple-yet-powerful model for early anomaly detection for spacecraft health monitoring. In experiments, MEND was able to provide strong alerts for impending anomalies as much as 10-15 minutes before the onset of the anomaly which could give a system admin valuable time to perform curative action. It is notable that none of the other models considered, including state-of-the-art zero-shot time series prediction models, were able to achieve this. MEND is based on simple, explainable elements such as self-supervised operation-mode detection and self-disagreement-based anomaly detection which, as a side effect, offer insights that may aid root cause analysis and may be of independent interest. Code for MEND is available at https://github.com/purushottamkar/mend
Short-Term Fog Forecasting using Meteorological Observations at Airports in North India

Fog is a major phenomenon in North India during the winter months of November to February. It lowers visibility in these regions which adversely affects surface and air transportation, agriculture, and other day-to-day activities. Flights get delayed, canceled or diverted due to reduced visibility conditions. Therefore, accurate forecasting of fog (which is measured as visibility in terms of distance) at the airports up to 6 hours is important. We develop data science models that use historical meteorological observations (1974-2021) from METAR reports of ground stations at different airports of North India for short-term fog prediction. Models are designed for both binary classification (“fog” versus “no-fog”) as well as multi-class classification (that categorizes fog into 4 classes). In addition, we also design regression models that predict visibility. Results show that the best visibility regression model has a root-mean-squared-error (RMSE) of 0.20 km for 3-hour lead time prediction for Lucknow airport. Corresponding classification accuracies are 0.90 and 0.79 for the 2-class and 5-class problems respectively. Similar trends were observed for other airports and lead times. A web-based dissemination system has been deployed at https://fog.iitk. ac.in.
RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain

We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model which recommends properties based on recent interactions of users. For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario. Moreover, based on the conversion rate, we have designed a novel weighing scheme for different impressions done by users on the platform for the training of content and collaborative models. Finally, we show the efficiency of the proposed pipeline, RE-RecSys, on a real-world property and click stream dataset collected from leading real-estate platform in India. We show that the proposed pipeline is deployable in real-world scenario with an average latency of < 40 ms serving 1000 rpm.
Building Knowledge Graph for Products at Web Scale

A knowledge graph is the key to entity search as it can store the factual entity related information in a structured manner without the rigidity of a fixed schema. Both Google and Bing have web scale knowledge graphs and for a large fraction of web queries knowledge graph is invoked. E-commerce search is primarily an entity search. Therefore, building a Knowledge Graph is the key to improve the eCommerce search in many ways. However, building it at web scale is a highly challenging problem. It is an equally or even more challenging problem to build the knowledge graph for products. In this tutorial, we present state-of-the-art work to address some of the key challenges to build the knowledge graph as well as our methodology to build a product graph at web scale for Microsoft-Shopping, containing a few billion facts.
Deep Tractable Probabilistic Models

Probabilistic models deal with uncertainty in data-driven decision-making and modeling in a principled manner. Recent advances in GPU-accelerated computation have enabled probabilistic models to scale to large and complex data sets. However, as models grow in complexity, efficient exact inference (querying the model) becomes a challenge, hindering their feasibility in high-stakes domains like healthcare. This tutorial aims to introduce participants to Deep Tractable Probabilistic Models (DTPMs), a special class of probabilistic models that balance expressiveness and tractability. Participants will learn about the theoretical foundations, practical implementations, and real-world applications of DTPMs.
Does threat familiarity and knowledge shape rapid visual attention?

ABSTRACT The threat superiority effect is the finding that threatening stimuli capture attention more rapidly than neutral stimuli. Despite its well-established presence, it is unknown whether the response is impacted by our experience with or knowledge of threats. To explore this, we quantified individual differences in familiarity and knowledge for threatening and non-threatening stimuli, and asked if these differences modulated attention on a visual search task. We found that participants were faster at detecting stimuli that they were less familiar with and had less knowledge of, but only for neutral items and not threats. These results suggest that experience and semantic knowledge can shape rapid visual attention, but that the threat superiority effect may be immune to these influences.
Predicting functionally important breast cancer SNPs using pleiotropy, conservation, and protein structure

Motivation With over 24,000 SNPs associated with breast cancer in ClinVar, there is a need to prioritize the subset most likely to be causally linked to diagnostic, prognostic, and other clinical outcomes of disease. Building off currently known breast cancer oncogenes and SNPs, we identify the subset of SNPs with pleiotropic effects, with the goal of identifying mutations functionally relevant to disease progression. We further use sequence and structure analysis to prioritize missense mutations most likely to impact protein function. Results From the known breast cancer SNPs, we identified co-associated mutations located at evolutionarily conserved positions and contributing significant protein stability as potential focal points for disease biomarkers, protein function studies, and therapeutic intervention. To identify regions likely integral to protein function, we plotted genomic intervals where multiple disease density peaks overlap. Of the breast cancer SNPs, 1,714 were co-associated in-frame mutations, of which 930 occurred at conserved residue positions (Shannon Entropy <1.0) and 833 were also missense mutations. Building structure-based models of the 277 SNPs with available protein structure resulted in identification of 133 SNPs that are calculated to affect protein thermostability by >100-fold (>3 kcal/mol). The workflow we built can be applied to other diseases to help identify functional mutations. Availability Python code for the integrated analysis workflow available at http://github.com/mcarpenter-brandeis/brc-pleiotropy and detailed data tables available in Supplemental Information. Contact acheng@brandeis.edu, meredith.a. carpenter@gmail.coms
Deciphering the molecular mechanisms underlying anti-pathogenic potential of a polyherbal formulation Enteropan® against multi-drug resistant Pseudomonas aeruginosa

Anti-pathogenic potential of a polyherbal formulation Enteropan® was investigated against a multidrug resistant strain of the bacterium Pseudomonas aeruginosa. Enteropan-pre-exposed P. aeruginosa displayed reduced (∼70%↓) virulence towards the model host Caenorhabditis elegans. Enteropan affected various traits like biofilm formation, protein synthesis and secretion, quorum-modulated pigment production, antibiotic susceptibility, nitrogen metabolism etc. in this pathogen. P. aeruginosa could not develop complete resistance to the virulence-attenuating activity of Enteropan even after repeated exposure to this polyherbal formulation. Whole transcriptome analysis showed 17% of P. aeruginosa genome to get differentially expressed under influence of Enteropan. Major mechanisms through which Enteropan exerted its anti-virulence activity were found to be generation of nitrosative stress, oxidative stress, envelop stress, quorum modulation, disturbance of protein homeostasis and metal homeostasis. Network analysis of the differently expressed genes resulted in identification of 10 proteins with high network centrality as potential targets from among the down-regulated genes. Differential expression of genes coding for five (rpoA, tig, rpsB, rpsL, and rpsJ) of these targets was validated through RT-PCR too, and they can further be pursued as potential targets by various drug discovery programmes.
Identification of a chromatin-bound ERRα interactome network in mouse liver

Objective Estrogen-related-receptor α (ERRα) plays a critical role in the transcriptional regulation of cellular bioenergetics and metabolism, and perturbations in its activity have been associated with metabolic diseases. While several coactivators and corepressors of ERRα have been identified to date, a knowledge gap remains in understanding the extent to which ERRα cooperates with coregulators in the control of gene expression. Herein, we mapped the primary chromatin-bound ERRα interactome in mouse liver. Methods RIME (Rapid Immuno-precipitation Mass spectrometry of Endogenous proteins) analysis using mouse liver samples from two circadian time points was used to catalog ERRα-interacting proteins on chromatin. The genomic crosstalk between ERRα and its identified cofactors in the transcriptional control of precise gene programs was explored through cross-examination of genome-wide binding profiles from chromatin immunoprecipitation-sequencing (ChIP-seq) studies. The dynamic interplay between ERRα and its newly uncovered cofactor Host cell factor C1 (HCFC1) was further investigated by loss-of-function studies in hepatocytes. Results Characterization of the hepatic ERRα chromatin interactome led to the identification of 48 transcriptional interactors of which 42 were previously unknown including HCFC1. Interrogation of available ChIP-seq binding profiles highlighted oxidative phosphorylation (OXPHOS) under the control of a complex regulatory network between ERRα and multiple cofactors. While ERRα and HCFC1 were found to bind to a large set of common genes, only a small fraction showed their co-localization, found predominately near the transcriptional start sites of genes particularly enriched for components of the mitochondrial respiratory chain. Knockdown studies demonstrated inverse regulatory actions of ERRα and HCFC1 on OXPHOS gene expression ultimately dictating the impact of their loss-of-function on mitochondrial respiration. Conclusions Our work unveils a repertoire of previously unknown transcriptional partners of ERRα comprised of chromatin modifiers and transcription factors thus advancing our knowledge of how ERRα regulates metabolic transcriptional programs.
Image Segmentation Using Markov Random Field Model Learning Feature and Parallel Hybrid Algorithm

In this paper, a new notion of image segmentation using Markov random field (MRF) model learning feature is addressed. The segmentation problem is formulated as pixel labeling problem in a supervised framework. MRF model is employed to model the class labels. This model learns a given training image derived from a class of images. The model having learnt is validated for other images of the same class. The learning problem is formulated using conditional pseudo likelihood (CPL) approach and the parameters are estimated using homotopy continuation method. This learning attribute is exploited to obtain the MAP estimates of the class labels using proposed hybrid tabu search (HTS) and parallel hybrid tabu search (PHTS) algorithms with a view to reduce the computational burden. The performance of these algorithms is compared with that of the simulated annealing (SA) algorithm. This learning feature avoids the parameter estimation of each and every individual image of a class of images.
A Quantitative Machine Learning Approach to Evaluating Letters of Recommendation

Letters of Recommendation (LOR) are key components of the undergraduate and graduate admissions process. A fair and objective evaluation of these LORs is difficult due to diverse applicant-recommender relationships, a lack of standardized criteria, and limited resources for reviewing the LORs. In this paper, we describe three criteria, relevance, specificity, and positivity, for characterizing the quality of an LOR. Approximately 4,000 LORs written in support of students applying to either a Master’s in Computer Science or a Master’s in Data Science degree are manually rated using these criteria along with rating guidelines developed for this study. Predictive models utilizing natural language processing and machine learning are trained to predict these ratings directly from the LOR text. The work described in this paper can aid in objective and automatic assessment of LORs, or help the admissions committee selectively review the LORs when resources are limited. This work can be extended to support the admissions process for other graduate and undergraduate programs.
Recognizing Similar Crises through the Application of Ontology-based Knowledge Mining

Recognizing and learning from similar crisis situations is crucial for the development of effective response strategies. This study addresses the challenge of identifying similarities within a wide range of crisis-related information. To overcome this challenge, we employed an ontology-based crisis situation knowledge base enriched with crisis-related information. Additionally, we implemented a semantic similarity measure to assess the degree of similarity between crisis situations. Our investigation specifically focuses on recognizing similar crises through the application of ontology-based knowledge mining. Through our experiments, we demonstrate the accuracy and efficiency of our approach to recognizing similar crises. These findings highlight the potential of ontology-based knowledge mining for enhancing crisis recognition processes and improving overall crisis management strategies.
UnityAccessibilityToolkit (UA11Y): Developer Tool and Roadmap to Make Games More Accessible for People with Vision Impairments

An increasing number of game productions rely on accessibility features. Large productions like The Last of Us demonstrate what is possible in this area, but many games still lack essential accessibility features. Smaller productions often lack the knowledge and resources to implement different accessibility features. In the first part of this paper, we investigate how people with vision impairments play video games and compare current accessibility features. Based on our findings, we present a roadmap and guidelines for improving game accessibility for individuals with a color vision deficiency, low vision, or any form of vision impairment. In the third part, we introduce the UnityAccessibilityToolkit, a toolkit for game developers to quickly and easily integrate accessibility elements for players with vision impairments into a game. We evaluated the toolkit with nine developers who were challenged to make a simple match-3 game more accessible. Our results demonstrate that the Unity toolkit is easy and fast to use, and that important accessibility features can be implemented quickly.
Prevalence of Stunting and Associated Factors among Children Under Five Years in Sana’a City, Yemen

It remains a significant public health problem in many low and middle-income countries, particularly in Yemen, related to nutritional deficiencies during the most critical periods of growth and development in early childhood. The purpose of this study was to determine the associated factors for stunting among Yemeni children under the age of five living in Sana’a city. This study was conducted using a cross-sectional study design among 434 mothers and their children aged 6-59 months. Data were collected through face-to-face interview using the structured questionnaire that consists of 28 items. Anthropometric indicator, height-for-age was determined using the current World Health Organization (WHO) growth standards. An analysis of the data was conducted using a multivariable logistic regression model. Statistical significance was determined by the Adjusted Odds Ratio (AOR) and 95% Confidence Interval (CI) from the multivariable analysis. The prevalence of stunting among children under the age of five was 46.1%. According to the multivariable analysis, stunting is statistically associated with children aged 13-24 months (AOR 2.04), 25-36 months (AOR 2.27), 37-48 months (AOR 2.38), secondary education of the father (AOR 0.31), household size of five members (AOR 0.66), and flushed or poured toilets (AOR 1.91). There are a number of family-specific factors that contribute to stunted childhood development. By addressing these factors, we can ensure that children have the best possible chance to thrive and reach their full potential. Thus, it is essential to prioritise family-specific factors when addressing childhood stunting to ensure a brighter future for children.
More is Better: The Effects of Multiple Repetitions on Implicit Memory Across Long Durations

Implicit memory was measured using word fragment completion up to 18 months after being exposed to words multiple times. In all cases participants showed significant priming for multiply presented words over singly presented ones. In shorter delay conditions (up to 6 weeks), implicit memory was tested using word reading speed and fragment completion. Both measures indicated larger effects following multiple prior exposures than following a single prior exposure. This effect was greater for low- than for high-frequency words. These results are consistent with the view that implicit memory is part of the same system that produces explicit memory. They do not support claims of dissociations in the effects of repetitions on implicit and explicit memory that had been used to argue for separate memory systems. In recent years, the distinction between implicit and explicit memories has taken a significant place among the diverse dichotomies used to partition memory into comprehensible pieces. The term “implicit memory” was first introduced by Graf and Schacter (1985) to refer to improved performance due to previous experiences that does not require conscious recollection of those experiences (Schacter, 1987), whereas the contrasting term, “explicit memory,” refers to improved performance due to previous experience that does require conscious recollection. The implicit/explicit distinction gains its primary interest to the degree that the two phenomena can be independently manipulated. For example, whereas amnesics generally demonstrate devastating loss of explicit memory, implicit memory tests show improvements due to past exposure indicating that, indeed, some memorial processes are spared (e.g., Graf & Schacter, 1985; Shimamura, 1986). Prior to the
Editing and Analysing Historical Astronomical Diagrams with Artificial Intelligence

The EIDA project explores the historical use of astronomical diagrams across Asia, Africa, and Europe. We aim to develop automatic image analysis tools to analyze and edit these diagrams without human annotation, gaining a refined understanding of their role in shaping and transmitting astronomy. In this paper, we present a baseline method to detects lines and circles in historical diagrams, based on text removal, edge detection and RANSAC. We compare this strong baseline to a deep learning approach based on LETR. This work contributes to historical diagram vectorization, enabling novel methods of comparison and clustering, and offering fresh insights into the vast corpus of astronomical diagrams.
Image Life Trails Based On Contrast Reduction Models For Face Counter-Spoong

Natural face images are both content and context-rich, in the sense that they carry signiﬁcant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spooﬁng by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Diﬀerences taken across this image sequence help in bringing out the self-shadows already present in the original image. On a client speciﬁc mode, when the subjects and faces are registered, secondary statistics which capture the prominence of self-shadow information, indicate that planar print-images tend to have highly suppressed self-shadows when compared with natural face images. An elaborate tuning procedure, based on a reduced set of training images was developed to ﬁrst identify the optimal parameter set and then adapt the feature-vectors so that the error-rates were minimized for a speciﬁc dataset. Overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.267% and the error rates for other datasets such OULU-NPU and CASIA-SURF were 0.17% and 0.73% respectively.