ethicalabs/Research-Intent-Collab huggingface dataset:
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets

The analysis of tabular datasets is highly prevalent both in scientific research and real-world applications of Machine Learning (ML). Unlike many other ML tasks, Deep Learning (DL) models often do not outperform traditional methods in this area. Previous comparative benchmarks have shown that DL performance is frequently equivalent or even inferior to models such as Gradient Boosting Machines (GBMs). In this study, we introduce a comprehensive benchmark aimed at better characterizing the types of datasets where DL models excel. Although several important benchmarks for tabular datasets already exist, our contribution lies in the variety and depth of our comparison: we evaluate 111 datasets with 20 different models, including both regression and classification tasks. These datasets vary in scale and include both those with and without categorical variables. Importantly, our benchmark contains a sufficient number of datasets where DL models perform best, allowing for a thorough analysis of the conditions under which DL models excel. Building on the results of this benchmark, we train a model that predicts scenarios where DL models outperform alternative methods with 86.1% accuracy (AUC 0.78). We present insights derived from this characterization and compare these findings to previous benchmarks.
IS RIGHT TO PRIVACY IN PERIL DUE TO SURGE OF CCTV SURVEILLANCE?

The present study analyzed the issue of right to privacy vis-à-vis CCTV surveillance. The usage of CCTV surveillance has grown in recent years. Governments and individuals are using CCTV surveillance for reducing the risk of crimes and to get proper information of crime. Technological enhancement should be used for making life better and safe. It has been found that mishandling of CCTV surveillance results in violation of the right to life with dignity. It has also raised the concern about liberty and freedom of individuals. The study put prominence on the proper usage of CCTV surveillances.  It is essential that the right of privacy should not be violated and the usage will give fruitful results.  There are always two sides of the coin. CCTV surveillance has its own pluses and drawbacks and the same is narrated in the present study. Whenever it is done without proper precautions then it may enter into the private life of a person without taking their consent. Photographs and clippings of private moments are divulged and it is the annexation of the privacy of people. It is uncomfortable. It is very difficult to keep balance between uprightness and evil in the era of technological advancement. The study put emphasis on the germane usage of CCTV surveillances rather than cluttered. The right to privacy is associated with dignity, freedom and liberty. It shall not be hampered by technological expansion.
Central Bank Digital Currencies for Cross-border Payments

Over the years, the demand for seamless and inexpensive cross-border payments has grown in parallel with growth in international e-commerce, remittances and tourism. Yet, cross-border payments have not kept pace with the intensive modernization that has characterized domestic payment services worldwide. An alternative avenue to modernize delivery of cross-border payment services is being increasingly explored in the context of central banks issuing their own digital currency. A central bank digital currency (CBDC) could well incorporate options and features specifically designed to execute cross-border payments, with a view to reducing the inefficiencies and rents discussed above by shortening the payments value chain. This report discusses the use of CBDCs for cross-border payments. The report reviews the models that have been developed for this purpose to date and discusses critical legal issues that arise in the context of cross-border use of CBDC. This report is organized as follows. Section II specifically discusses the models developed jointly by the Bank of Canada, Bank of England, and Monetary Authority of Singapore; Section III evaluates how cross-border CBDCs address challenges of the existing correspondent banking arrangement; Section IV discusses the legal issues involved in cross-border use of CBDCs, and Section V concludes the report with some general remarks.
Impact of timing and setting of palliative care referral on quality of end‐of‐life care in cancer patients

BACKGROUNDLimited data are available on how the timing and setting of palliative care (PC) referral can affect end‐of‐life care. In this retrospective cohort study, the authors examined how the timing and setting of PC referral were associated with the quality of end‐of‐life care.METHODSAll adult patients residing in the Houston area who died of advanced cancer between September 1, 2009 and February 28, 2010 and had a PC consultation were included. Data were retrieved on PC referral and quality of end‐of‐life care indicators.RESULTSAmong 366 decedents, 120 (33%) had an early PC referral (&gt;3 months before death), and 169 (46%) were first seen as outpatients. Earlier PC referral was associated with fewer emergency room visits (39% vs 68%; P &lt; .001), fewer hospitalizations (48% vs 81%; P &lt; .003), and fewer hospital deaths (17% vs 31%; P = .004) in the last 30 days of life. Similarly, outpatient PC referral was associated with fewer emergency room visits (48% vs 68%; P &lt; .001), fewer hospital admissions (52% vs 86%; P &lt; .001), fewer hospital deaths (18% vs 34%; P = .001), and fewer intensive care unit admissions (4% vs 14%; P = .001). In multivariate analysis, outpatient PC referral (odds ratio [OR], 0.42; 95% confidence interval [CI], 0.28‐0.66; P &lt; .001) was independently associated with less aggressive end‐of‐life care. Men (OR, 1.63; 95% CI, 1.06‐2.50; P = .03) and hematologic malignancies (OR, 2.57; 95% CI, 1.18‐5.59; P = .02) were associated with more aggressive end‐of‐life care.CONCLUSIONSPatients who were referred to outpatient PC had improved end‐of‐life care compared with those who received inpatient PC. The current findings support the need to increase the availability of PC clinics and to streamline the process of early referral. Cancer 2014;120:1743–1749. © 2014 American Cancer Society.
Observed solar modulation of galactic cosmic ray intensity in the outer heliosphere, 1997–2001

Instruments on Pioneer 10, Voyager 1 and Voyager 2 measure galactic cosmic ray intensity (&gt;80 MeV/nucleon) as the spacecraft move along their solar system escape trajectories. As of 1 June 2001, their respective heliocentric radial distances were 78 AU (in approximately the antapex direction from the Sun), 81 AU and 64 AU (the latter two in approximately the apex direction). The principal finding of this paper is that all three spacecraft were still under the delayed influence of increasing solar activity during cycle 23 and were, therefore, inside the modulation boundary of the heliosphere. Also the combined evidence favors essential antapex‐apex symmetry in the large scale modulation medium. The contents of this paper are generally concordant with studies by other groups of authors but are unique in the inclusion of the recent four years of antapex data from Pioneer 10.
Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures

The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, and these predicted structures are expected to benefit the model performance by increasing the number of training samples. In this work, we constructed a new data set that acted as a benchmark and implemented a state-of-the-art structure-based approach for determining whether the performance of the function prediction model can be improved by putting additional AlphaFold-predicted structures into the training set and further compared the performance differences between two models separately trained with real structures only and AlphaFold-predicted structures only. Experimental results indicated that structure-based protein function prediction models could benefit from virtual training data consisting of AlphaFold-predicted structures. First, model performances were improved in all three categories of Gene Ontology terms (GO terms) after adding predicted structures as training samples. Second, the model trained only on AlphaFold-predicted virtual samples achieved comparable performances to the model based on experimentally solved real structures, suggesting that predicted structures were almost equally effective in predicting protein functionality.
Error-Mitigation Strategies for Quantum Computing in High-Accuracy Prediction of Molecular Interactions in Process Engineering

Quantum computing is emerging as a transformative paradigm for molecular-level modeling in process engineering, offering the potential to perform large-scale, high-accuracy simulations that surpass the computational limits of classical methods. However, the inherent noise and decoherence in quantum systems present major obstacles to reliable molecular interaction predictions. This research conducts a qualitative investigation of error-mitigation strategies designed to improve the accuracy and stability of quantum simulations for modeling molecular interactions relevant to chemical and process engineering. By reviewing scientific literature and analyzing case studies across quantum mechanics, computational chemistry, and industrial process modeling, the study identifies the principal challenges associated with quantum noise, gate fidelity, and algorithmic scalability. A conceptual framework was developed to evaluate how quantum error correction (QEC) techniques, zero-noise extrapolation, and hybrid quantum–classical algorithms enhance predictive precision in molecular simulations. The findings suggest that combining these methods not only mitigates computational noise but also improves convergence efficiency in modeling reaction pathways and intermolecular potentials. This research synthesizes interdisciplinary insights to propose a roadmap for integrating quantum-enhanced molecular simulations into sustainable process engineering. It underscores how advances in error mitigation will be essential for achieving trustworthy, energy-efficient, and scalable industrial applications of quantum computation in the coming decade.
3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

Generative models have been successfully used to synthesize completely novel images, text, music and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning methods applied to molecular and drug discovery have yet to produce stable and novel 3-D crystal structures across multiple material classes. To that end, we herein present an autoencoder-based generative deep-representation learning pipeline for geometrically optimized 3-D crystal structures that simultaneously predicts the values of eight target properties. The system is highly general, as demonstrated through creation of novel materials from three separate material classes: binary alloys, ternary perovskites and Heusler compounds. Comparison of these generated structures to those optimized via electronic-structure calculations shows that our generated materials are valid and geometrically optimized.
Antimicrobial Stewardship: Smart Approach to Combat Antibiotic Resistance

Abstract: Antimicrobial resistance (AMR) is a major public health concern worldwide. It raises morbidity and mortality and is connected with substantial/significant economic costs. Effective prevention and treatment of an increasing number of diseases caused by pathogens that can no longer be treated with conventional drugs are threatened by antimicrobial re-sistance. AMS programme helps to encourage the appropriate use of antimicrobials. The rise and spread of antibiotic resistance and strategies to combat it are the main topics of this review. Antimicrobial stewardship programme is a healthcare strategy that optimizes the use of anti-biotics and promotes the appropriate use of antimicrobials through the adoption of evidence-based interventions. AMS programme helps to encourage the right use of antimicrobials. This coordinated approach enhances patient outcomes, reduces microbial resistance, and slows the spread of diseases brought on by multidrug-resistant organisms. The growth of antibiotic re-sistance and the strategies for battling it have been the main topics of this review.
A systematic review of digital technology and innovation and its potential to address anti-corruption, transparency, and accountability in the pharmaceutical supply chain

The urgent need to acquire medical supplies amidst the COVID-19 pandemic has led to bypassing of controls that govern the global pharmaceutical supply chain, increasing the risk of corruption. Hence, promoting anti-corruption, transparency, and accountability (ACTA) in supply chain and procurement has never been more important. The adoption of digital tools, if designed and implemented appropriately, can reduce the risks of corruption.Following PRISMA guidelines, we conducted an interdisciplinary systematic review of health/medicine, humanities/social sciences, engineering, and computer science literature, with the aims of identifying technologies used for pharmaceutical supply chain and procurement optimization and reviewing whether they address ACTA mechanisms to strengthen pharmaceutical governance. Our review identified four distinct categories of digital solutions: e-procurement and open contracting; track-and-trace technology; anti-counterfeiting technology; and blockchain technology.Findings demonstrate an increase in research of technologies to improve pharmaceutical supply chain and procurement functions; however, most technologies are not being leveraged to directly address ACTA or global health outcomes. Some blockchain and RFID technologies incorporated ACTA mechanisms and mentioned specific policy/governance frameworks, but more purposeful linkage is needed. Findings point to the need for targeted policy development and governance to activate these innovative technologies to improve global health .
An Experimental Method to Estimate Running Time of Evolutionary Algorithms for Continuous Optimization

Running time analysis is a fundamental problem of critical importance in evolutionary computation. However, the analysis results have rarely been applied to advanced evolutionary algorithms (EAs) in practice, let alone their variants for continuous optimization. In this paper, an experimental method is proposed for analyzing the running time of EAs that are widely used for solving continuous optimization problems. Based on Glivenko–Cantelli theorem, the proposed method simulates the distribution of gain, which is introduced by average gain model to characterize progress during the optimization process. Data fitting techniques are subsequently adopted to obtain a desired function for further analyses. To verify the validity of the proposed method, experiments were conducted to estimate the upper bounds on expected first hitting time of various evolutionary strategies, such as (1,    $\lambda $   ) evolution strategy, standard evolution strategy, covariance matrix adaptation evolution strategy, and its improved variants. The results suggest that all estimated upper bounds are correct. Backed up by the proposed method, state-of-the-art EAs for continuous optimization will have identical results about the running time as simplified schemes, which will bridge the gap between theoretical foundation and applications of evolutionary computation.
Photonic neural networks with silicon integrated optical circuits

Αυτή η διατριβή παρουσιάζει μια συνεκτική εξερεύνηση για την υλοποιήση φωτονικών νευρωνικών δικτύων σε κλίμακα μικροτσίπ. Περιλαμβάνει τους θεμελιώδεις ορισμούς και τις σημαντικότερες προκλήσεις για την πραγματοποίησή τους, καθώς και τις καινοτόμες αρχιτεκτονικές και προτότυπα που έχουν δειχθεί έως τώρα στη βιβλιογραφία. Το ερευνητικό ταξίδι του διδακτορικού μου ξεκινά με μια εκτενή εξέταση των θεωρητικών θεμελίων των φωτονικών νευρωνικών δικτύων, τα οποία αποτέλεσαν τις βάσεις για την ερευνητικές μου καινοτομίες στον τομέα αυτό. Κεντρικό σημείο αυτής της διατριβής είναι η εισαγωγή μιας καινοτόμου αρχιτεκτονικής τύπου Xbar για γραμμική επεξεργασία πληροφοριών, η οποία εκμεταλλευόμενη τεχνικές συστήματος διαμοιρασμού χώρου, είναι σχεδιασμένη να λειτουργεί ως ένας οικουμενικός γραμμικός επεξεργαστής πληροφοριών ενσωματωμένος σε κλίμακες τσιπ, όπου η πληροφορία κωδικοποιείται και επεξεργάζεται μέσω του φωτός. Αυτή η αρχιτεκτονική καινοτομία επιτρέπει την υλοποίηση πράξεων γραμμικής άλγεβρας, και πιο συγκεκριμένα πολλαπλασιασμό πινάκων μεγάλων διαστάσεων, σε υψηλές ταχύτητες, με μεγάλη ακρίβεια, κάτω από την ομπρέλα της υψηλής ενεργειακής αποδοτικότητας. Οι θεωρητικές αρχές που υποστηρίζουν αυτήν την καινοτόμο αρχιτεκτονική αναλύονται λεπτομερώς, αναδεικνύοντας το δυναμικό της για επανάσταση στην επεξεργασία γραμμικής επεξεργασίας σε τσιπ με τη χρήση της οπτικής τεχνολογίας. Επί της θεωρητικής αυτής βάσης, παρουσιάζεται η υλοποίηση γραμμικών υπολογιστικών πράξεων πάνω σε ένα πρωτότυπο 4×4 Xbar ολοκληρωμένο σε πλατφόρμα πυριτίου χρησιμοποιώντας τη φωτονική τεχνολογία. Αυτή η υλοποίηση επιβεβαιώνει τις θεωρητικές έννοιες και παρουσιάζει τα πρακτικά πλεονεκτήματά της στην γραμμική επεξεργασία πληροφοριών. Επιπροσθέτως, παρουσιάζεται η ενσωμάτωσή της ως γραμμικός επεξεργαστής σε μοντέλα νευρωνικών δικτύων για την υλοποίηση εφαρμογών, όπου επιτυγχάνονται γραμμικοί υπολογισμοί σε ταχύτητες ρεκόρ, έως και 25 φορές υψηλότερες από τις ταχύτητες των σύγχρονων ηλεκτρονικών επεξεργαστών. Γεφυρώνοντας το χάσμα μεταξύ υλοποιήσεων φωτονικών επεξεργαστών γραμμικών και μη γραμμικών πράξεων, παρουσιάζεται η υλοποίηση μιας συνάρτησης ενεργοποίησης τύπου ReLU σε πλατφόρμα που ενσωματώνει υλικά III-V με την ώριμη τεχνολογία πυριτίου. Αυτή η υλοποίηση θέτει τις βάσεις για την επίτευξη της ενσωμάτωσης συστημάτων επεξεργασίας γραμμικών και μη γραμμικών πράξεων σε κλίμακες τσιπ, για την πραγματοποίηση ενός πλήρους συστήματος νευρωνικών φωτονικών δικτύων. Συνοψίζοντας, αυτή η διατριβή παρουσιάζει μια ολιστική προσέγγιση για την προαγωγή των φωτονικών νευρωνικών δικτύων σε κλίμακα τσιπ, από τη θεωρητική εξερεύνηση έως την πρακτική εφαρμογή. Οι καινοτόμες αρχιτεκτονικές και οι υλποιήσεις που παρουσιάζονται σε αυτό το έργο ανοίγουν ενδιαφέρουσες προοπτικές για την ενσωμάτωση της φωτονικής τεχνολογίας σε μια ευρεία γκάμα υπολογιστικών εφαρμογών για μοντέλα νευρωνικών δικτύων.
Review of approximations for the exchange-correlation energy in density-functional theory

In this chapter, we provide a review of ground-state Kohn-Sham density-functional theory of electronic systems and some of its extensions, we present exact expressions and constraints for the exchange and correlation density functionals, and we discuss the main families of approximations for the exchange-correlation energy: semilocal approximations, single-determinant hybrid approximations, multideterminant hybrid approximations, dispersion-corrected approximations, as well as orbital-dependent exchange-correlation density functionals. The chapter aims at providing both a consistent bird's-eye view of the field and a detailed description of some of the most used approximations. It is intended to be readable by chemists/physicists and applied mathematicians.
The influence of diagnosed mental health conditions and symptoms of depression and/or anxiety on suicide ideation, plan, and attempt among college students: Findings from the Healthy Minds Study, 2018–2019

Approximately 11% of 18-25 year-olds report thoughts of suicide. Additionally, suicide is the second leading cause of death in college student populations. We sought to evaluate the relationship between diagnosed mental health conditions and current symptoms of depression and/or anxiety and suicidality in the past year.Healthy Minds Study (HMS) 2018-2019 data from 38,757 college students were analyzed. The PHQ-9, GAD-7, and prior mental health condition diagnoses were used to create a suicidality severity index and we determined how these associations varied by race/ethnicity, gender, and sexual orientation. We also assessed non-suicidal self-injury (NSSI) outcomes in the past year.Students with both a mental health condition diagnosis and current moderate/severe symptoms of depression and/or anxiety had a higher prevalence of NSSI, 10 times the odds (95% CI 9.4-11.5) of suicide ideation, 28 times the odds (95% CI 23.8-33.1) of suicide ideation, with planning or attempt, and 47 times the odds (95% CI 31.1-71.4) of suicide ideation, with planning and attempt, compared to students with none/minimal depression and/or anxiety symptoms and no mental health condition diagnosis.We could not clinically confirm depression or anxiety diagnoses nor infer causality of associations in this cross-sectional study. Future longitudinal studies are needed to establish temporality.Mental health condition diagnoses and moderate/severe symptoms of depression and/or anxiety were strongly associated with suicidality among college students. These findings identify potential opportunities to further understand and address the mental health needs of college students.
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundation models with just three lines of code. Supporting various modalities including image, text, and tabular data, both independently and in combination, the library offers a comprehensive suite of functionalities spanning classification, regression, object detection, semantic matching, and image segmentation. Experiments across diverse datasets and tasks showcases AutoMM's superior performance in basic classification and regression tasks compared to existing AutoML tools, while also demonstrating competitive results in advanced tasks, aligning with specialized toolboxes designed for such purposes.
Peer Influence in the Diffusion of iPhone 3G over a Large Social Network.

In this paper, we study the effect of peer influence in the diffusion of the iPhone 3G across a number of communities sampled from a large dataset provided by a major European Mobile carrier in one country. We identify tight communities of users in which peer influence may play a role and use instrumental variables to control for potential correlation between unobserved subscriber heterogeneity and friends' adoption. We provide evidence that the propensity of a subscriber to adopt increases with the percentage of friends who have already adopted. During a period of 11 months, we estimate that 14 percent of iPhone 3Gs sold by this carrier were due to peer influence. This result is obtained after controlling for social clustering, gender, previous adoption of mobile Internet data plans, ownership of technologically advanced handsets, and heterogeneity in the regions where subscribers move during the day and spend most of their evenings. This result remains qualitatively unchanged when we control for changes over time in the structure of the social network. We provide results from several policy experiments showing that, with this level of effect of peer influence, the carrier would have hardly benefitted from using traditional marketing strategies to seed the iPhone 3G to benefit from viral marketing.
Large-scale land acquisitions and minorities/indigenous peoples' rights under ethnic federalism in Ethiopia. A Case Study of Gambella Regional State

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