ethicalabs/Research-Intent-Collab huggingface dataset:
Interface electrochemistry in conversion materials for Li-ion batteries

This work presents a new methodology based on a multi-interface superlattice approach to investigate interface electrochemistry in conversion reactions, through first-principles DFT or DFT + U calculations. This method is applied to the CoO electrode for which several experimental data are available, and describes the main interface effects by means of three interdependent descriptors, namely the chemical (interface bonding), the mechanical (stress) and the electrical/electrochemical (electric bias) descriptors. Thanks to this decomposition, the most probable interfaces occurring during the CoO conversion have been determined, and a probable electrode morphology has been proposed. Furthermore, the addition of an external redox potential to the multi-interface superlattice has revealed asymmetric electrochemical interface phenomena in charge and discharge, shedding some light into the voltage hysteresis observed for the CoO conversion/reconversion.
Indirect detection, direct detection, and collider detection cross-sections for a 70 GeV dark matter WIMP

4 pages, proceedings of ICHEP 2022
Can artificial neural networks supplant the polygene risk score for risk prediction of complex disorders given very large sample sizes?

Genome-wide association studies (GWAS) provide a means of examining the common genetic variation underlying a range of traits and disorders. In addition, it is hoped that GWAS may provide a means of differentiating affected from unaffected individuals. This has potential applications in the area of risk prediction. Current attempts to address this problem focus on using the polygene risk score (PRS) to predict case-control status on the basis of GWAS data. However this approach has so far had limited success for complex traits such as schizophrenia (SZ). This is essentially a classification problem. Artificial neural networks (ANNs) have been shown in recent years to be highly effective in such applications. Here we apply an ANN to the problem of distinguishing SZ patients from unaffected controls. We compare the effectiveness of the ANN with the PRS in classifying individuals by case-control status based only on genetic data from a GWAS. We use the schizophrenia dataset from the Psychiatric Genomics Consortium (PGC) for this study. Our analysis indicates that the ANN is more sensitive to sample size than the PRS. As larger and larger sample sizes become available, we suggest that ANNs are a promising alternative to the PRS for classification and risk prediction for complex genetic disorders.
A Hybrid Log-Driven and Event-Time Streaming Pipeline: Integrating Kafka Streams with Apache Flink for Real-Time Financial Transaction Processing

Growing demand for real-time, fault-tolerant transaction processing in financial and enterprise environments has accelerated the adoption of distributed stream-processing frameworks such as Kafka Streams and Apache Flink, which together enable continuous ingestion, validation, enrichment, and routing of transactional data with strict correctness guarantees. As organizations confront increasing transaction volumes, complex compliance requirements, and the need for instantaneous insights, traditional batch-oriented or monolithic systems are unable to provide the low-latency responsiveness and horizontal scalability demanded by modern financial workloads. In this context, Kafka Streams offers a lightweight, embedded processing model tightly coupled with Kafka’s durable log, enabling deterministic state management, idempotent updates, and replay-based recovery without external cluster infrastructure. Complementing this, Apache Flink provides a mature event-time processing engine with sophisticated windowing, watermark propagation, and fault-tolerant state handling capable of capturing nuanced temporal patterns and detecting anomalies in continuously evolving datasets. By synthesizing these strengths into a unified architecture, and grounding the design in foundational research on log-centric systems, event-time semantics, and early continuous computation frameworks published prior to September 2017, this article demonstrates how the combined approach supports real-time fraud detection, payment authorization, algorithmic trading pipelines, AML monitoring, and audit-ready ledger transformations. Ultimately, the integrated model delivers strong exactly-once consistency, operational resilience, temporal accuracy, and analytical flexibility qualities that are essential for building next-generation financial platforms under stringent regulatory, latency, and correctness constraints.
On Diversified Preferences of Large Language Model Alignment

EMNLP 2024
The Three-Dimensional Virtual Surgical Simulation and Surgical Assistance for Optimizing Robotic Partial Nephrectomy

Robot-assisted partial nephrectomy (RAPN) has been accepted as the standard treatment recommended for relatively small renal mass or even the T2 renal carcinoma in experienced hospitals as Nephron Sparing Surgery. To obtain better RAPN surgical outcomes, the understanding of surgical anatomies such as the position of intra-renal structure and the positional relationship of each structure should be detailed in a three-dimensional (3D) manner. The 3D virtual surgical simulation for partial nephrectomy based on the image segmentation method with high-resolution CT can provide the 3D anatomical details of the renal tumor focusing on their relationships with the arterial and venous branches as well as with the intrarenal portion of the urinary collecting system. This imaging application is also used as image guidance during the surgery, and it indicated that it provides the improvement of clinical outcomes such as the duration of hospitalization, transfusion, and major postoperative complications as well as conversion to radical nephrectomy or open partial nephrectomy. In this chapter, we describe the basics of the 3D imaging assistance methods for partial nephrectomy and the benefit of 3D virtual surgical simulation in optimizing the outcome of the RAPN.
Methodology, results, and publication of oncology clinical trials: Insights from all the world’s randomized controlled trials (RCTs) 2014-2017.

2019  Background: Clinical cancer research is now a global effort. Most published overviews of oncology trials are restricted to a specific disease site or cohort of high-profile journals. Here we describe authorship, trial characteristics, design, and results of all oncology RCTs published globally during 2014-2017. Methods: A structured literature search was designed using PUBMED to identify all RCTs evaluating anti-cancer therapies published during 2014-2017. Data were captured regarding authorship, participants, study characteristics, design, and results. Among superiority RCTs that met the primary endpoint (i.e. statistically “positive”), we calculated the ESMO-MCBS to identify trials with substantial clinical benefit (MCBS scores 4/5 or A/B). Outcomes were compared with Chi Square or Fisher’s Exact tests. Results: The study cohort included 694 RCTs. The most common cancers evaluated were breast (17%, 121/694), lung (15%, 104/694) and colorectal (8%, 58/694). Treatment intent was curative, adjuvant/neoadjuvant, and palliative in 10% (68/694), 25% (176/694), and 65% (448/694) of trials respectively. Median sample size was 443 (IQR 246-718). Seventy percent (488/694) of RCTs were supported by industry; 87% (601/694) of experimental arms tested systemic therapy. Ninety-two percent (636/694) of RCTs were led by investigators in 28 high-income countries; the most common countries leading these trials were US (27%, 174/636), France (10%, 64/636), Germany (10%, 62/636), Japan (9%, 59/636), and UK (9%, 57/636). The most common primary endpoints were PFS (32%, 220/694), OS (31%, 215/694), and DFS (11%, 79/694); Forty-six percent of all trials (318/694) met their primary endpoint. Among superiority trials with “positive” results, 33% met ESMO-MCBS threshold for substantial clinical benefit. The median impact factor (IF) of journals which published the overall study cohort of trials was 21 (IQR 7-27); trials meeting their primary endpoint were published in higher profile journals (median IF 25 vs 18, p &lt; 0.001). Conclusions: At the global level, oncology clinical trials are dominated by high-income countries and study diseases which do not necessarily reflect the global burden of cancer. The vast majority of trials are funded by industry and only one third of “positive” trials meet ESMO-MCBS threshold for substantial clinical benefit.
Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits

Variational Quantum Circuits (VQC) are promising models for quantum machine learning, but standard monolithic architectures face an expressivity--trainability dilemma: small circuits can be under-parameterized, while larger circuits are difficult to simulate and optimize. We propose Multi-Layer Fully-Connected Variational Quantum Circuits (FC-VQC), a modular framework that decomposes high-dimensional inputs into fixed-size local VQC blocks connected by deterministic block-mixing rules. This design keeps each quantum computation local while allowing the number of trainable quantum parameters to scale linearly with input dimension. We evaluate FC-VQC across tabular regression, tabular classification, and spatio-temporal BSDE/PDE approximation. Across the evaluated tasks, FC-VQC improves over monolithic VQC baselines and achieves competitive or improved performance relative to structure-matched deep neural network (DNN) baselines, while using substantially fewer trainable parameters.
Diffusion models for probabilistic precipitation generation from atmospheric variables

Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially of extreme events like floods and droughts. In existing ESMs, precipitation is not resolved explicitly, but represented by parameterizations. These typically rely on resolving approximated but computationally expensive column-based physics, not accounting for interactions between locations. They struggle to capture fine-scale precipitation processes and introduce significant biases. We present a novel approach, based on generative machine learning, which integrates a conditional diffusion model with a UNet architecture to generate accurate, high-resolution (0.25{\deg}) global daily precipitation fields from a small set of prognostic atmospheric variables. Unlike traditional parameterizations, our framework efficiently produces ensemble predictions, capturing uncertainties in precipitation, and does not require fine-tuning by hand. We train our model on the ERA5 reanalysis and present a method that allows us to apply it to arbitrary ESM data, enabling fast generation of probabilistic forecasts and climate scenarios. By leveraging interactions between global prognostic variables, our approach provides an alternative parameterization scheme that mitigates biases present in the ESM precipitation while maintaining consistency with its large-scale (annual) trends. This work demonstrates that complex precipitation patterns can be learned directly from large-scale atmospheric variables, offering a computationally efficient alternative to conventional schemes.
Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review

Thymic epithelial tumors (TETs) are rare, but they are the most common tumors in the anterior mediastinum. The use of artificial intelligence (AI) in the medical field is rapidly advancing. Especially in a rare disease such as TET, AI can help to improve existing care and foster new innovations. However, there are many different AI models with various implications. This systematic review aims to give an overview of recent studies on AI applications for the diagnosis and prognosis of TETs.In this systematic review, six electronic databases were searched for research papers with the keywords "thymoma", "thymic epithelial tumors", "thymic carcinoma", "artificial intelligence", "machine learning" and "deep learning". The screening was performed by two reviewers and conflicts were resolved by a third reviewer.After removal of duplicates, 582 articles were included. Of these, 65 articles were eligible for inclusion after screening. AI models are used mainly for distinguishing between different mediastinal tumor types (n=21), risk stratification (n=26), surgical planning (n=7) and for assisting in subtyping (n=4). For prognostic purposes, AI is used for predicting clinical outcome as well as the chance of metastasis or prognosis (n=7). Models using combined clinical and radiomics data performed better than models with a single type of data. Some of the AI models outperformed physicians or successfully supported physicians in their workflow.Many AI models have been studied in the context of the diagnosis and prognosis of TETs. Even though results are promising, external validation is often lacking, and sample sizes are small. Therefore, most models are not yet ready for clinical implementation. Further research on AI models with larger datasets and external validation is necessary, with careful consideration of the risk of data leakage.
Magnitude homology of graphs and discrete Morse theory on Asao-Izumihara complexes

Recently, Asao and Izumihara introduced CW-complexes whose homology groups are isomorphic to direct summands of the graph magnitude homology group. In this paper, we study the homotopy type of the CW-complexes in connection with the diagonality of magnitude homology groups. We prove that the Asao-Izumihara complex is homotopy equivalent to a wedge of spheres for pawful graphs introduced by Y. Gu. The result can be considered as a homotopy type version of Gu's result. We also formulate a slight generalization of the notion of pawful graphs and find new non-pawful diagonal graphs of diameter $2$.
Propositional computability logic I

In the same sense as classical logic is a formal theory of truth, the recently initiated approach called computability logic is a formal theory of computability. It understands (interactive) computational problems as games played by a machine against the environment, their computability as existence of a machine that always wins the game, logical operators as operations on computational problems, and validity of a logical formula as being a scheme of "always computable" problems. The present contribution gives a detailed exposition of a soundness and completeness proof for an axiomatization of one of the most basic fragments of computability logic. The logical vocabulary of this fragment contains operators for the so called parallel and choice operations, and its atoms represent elementary problems, i.e. predicates in the standard sense. This article is self-contained as it explains all relevant concepts. While not technically necessary, however, familiarity with the foundational paper "Introduction to computability logic" [Annals of Pure and Applied Logic 123 (2003), pp.1-99] would greatly help the reader in understanding the philosophy, underlying motivations, potential and utility of computability logic, -- the context that determines the value of the present results. Online introduction to the subject is available at http://www.cis.upenn.edu/~giorgi/cl.html and http://www.csc.villanova.edu/~japaridz/CL/gsoll.html .
Hidden orbital polarization in diamond, silicon, germanium, gallium arsenide and layered materials

It was previously believed that the Bloch electronic states of non-magnetic materials with inversion symmetry cannot have finite spin polarizations. However, since the seminal work by Zhang et al. [Nat. Phys. 10, 387-393 (2014)] on local spin polarizations of Bloch states in non-magnetic, centrosymmetric materials, the scope of spintronics has been significantly broadened. Here, we show, using a framework that is universally applicable independent of whether hidden spin polarizations are small (e.g., diamond, Si, Ge, and GaAs) or large (e.g., MoS2 and WSe2), that the corresponding quantity arising from orbital - instead of spin - degrees of freedom, the hidden orbital polarization, is (i) much more abundant in nature since it exists even without spin-orbit coupling and (ii) more fundamental since the interband matrix elements of the site-dependent orbital angular momentum operator determines the hidden spin polarization. We predict that the hidden spin polarization of transition metal dichalcogenides is reduced significantly upon compression. We suggest experimental signatures of hidden orbital polarization from photoemission spectroscopies and demonstrate that the current-induced hidden orbital polarization may play a far more important role than its spin counterpart in antiferromagnetic information technology by calculating the current-driven antiferromagnetism in compressed silicon.
Exact site frequency spectra of neutrally evolving tumors: a transition between power laws reveals a signature of cell viability

The site frequency spectrum (SFS) is a popular summary statistic of genomic data. While the SFS of a constant-sized population undergoing neutral mutations has been extensively studied in population genetics, the rapidly growing amount of cancer genomic data has attracted interest in the spectrum of an exponentially growing population. Recent theoretical results have generally dealt with special or limiting cases, such as considering only cells with an infinite line of descent, assuming deterministic tumor growth, or taking large-time or large-population limits. In this work, we derive exact expressions for the expected SFS of a cell population that evolves according to a stochastic branching process, first for cells with an infinite line of descent and then for the total population, evaluated either at a fixed time (fixed-time spectrum) or at the stochastic time at which the population reaches a certain size (fixed-size spectrum). We find that while the rate of mutation scales the SFS of the total population linearly, the rates of cell birth and cell death change the shape of the spectrum at the small-frequency end, inducing a transition between a $1/j^2$ power-law spectrum and a $1/j$ spectrum as cell viability decreases. We show that this insight can in principle be used to estimate the ratio between the rate of cell death and cell birth, as well as the mutation rate, using the site frequency spectrum alone. Although the discussion is framed in terms of tumor dynamics, our results apply to any exponentially growing population of individuals undergoing neutral mutations.
Synthesis of Spherical 4R Mechanism for Path Generation using Differential Evolution

The problem of path generation for the spherical 4R mechanism is solved using the Differential Evolution algorithm (DE). Formulas for the spherical geodesics are employed in order to obtain the parametric equation for the generated trajectory. Direct optimization of the objective function gives the solution to the path generation task without prescribed timing. Therefore, there is no need to separate this task into two stages to make the optimization. Moreover, the order defect problem can be solved without difficulty by means of manipulations of the individuals in the DE algorithm. Two examples of optimum synthesis showing the simplicity and effectiveness of this approach are included.
The Cosmic Microwave Background Bipolar Power Spectrum: Basic Formalism and Applications

39 pages, 12 figures
A parallel-vector algorithm for rapid structural analysis on high-performance computers

A fast, accurate Choleski method for the solution of symmetric systems of linear equations is presented. This direct method is based on a variable-band storage scheme and takes advantage of column heights to reduce the number of operations in the Choleski factorization. The method employs parallel computation in the outermost \(DO\)-loop and vector computation via the `loop unrolling' technique in the innermost \(DO\)-loop. The close relationship between Choleski and Gauss elimination methods is examined.
Multiflavor Mott insulators in quantum materials and ultracold atoms

Mott insulators with large and active (or multiflavor) local Hilbert spaces widely occur in quantum materials and ultracold atomic systems, and are dubbed "multiflavor Mott insulators". For these multiflavored Mott insulating materials, the spin-only description with the quadratic spin interactions is often insufficient to capture the major physical processes. In the situation with active orbitals, the Kugel-Khomskii superexchange model was then proposed. We briefly review this historical model and discuss the modern developments beyond the original spin-orbital context. These include and are not restricted to the $4d$/$5d$ transition metal compounds with the spin-orbit-entangled $J=3/2$ quadruplets, the rare-earth magnets with two weakly-separated crystal field doublets, breathing magnets and/or the cluster and molecular magnets, et al. We explain the microscopic origin of the emergent Kugel-Khomskii physics in each realization with some emphasis on the $J=3/2$ quadruplets, and refer the candidate multiflavor Mott insulators as "$J=3/2$ Mott insulators". For the ultracold atoms, we review the multiflavor Mott insulator realization with the ultracold alkaline and alkaline-earth atoms on the optical lattices. Despite a large local Hilbert space from the atomic hyperfine spin states, the system could naturally realize a large symmetry group such as the Sp($N$) and SU($N$) symmetries. These ultracold atomic systems lie in the large-$N$ regime of these symmetry groups and are characterized by strong quantum fluctuations. The Kugel-Khomskii physics and the exotic quantum ground states with the "baryon-like" physics can appear in various limits. We conclude with our vision and outlook on this subject.
An evaluation of the effects of the occupational safety and health inspectors’ supervision in workplaces

The aim of this study was to investigate the effects of the administrational supervision of occupational safety and health (OSH) inspectors in the workplaces that they inspect. In the questionnaire survey, the respondents were OSH managers and workers' OSH representatives in the chemical companies' plants. For workers, the aggregated variable 'General OSH enforcement operations' was positively related to the aggregated variable 'Effects of the current OSH inspection'. In contrast, for the OSH managers, the corresponding data did not reveal any correlation. The professional competence in carrying out the OSH inspections was positively related to the different variables of the aggregated variable 'Effects of the current OSH inspection (Outputs)'. In contrast, for the workers' OSH representatives, the analysis failed to confirm this relationship. The present findings are that OSH managers and workers' OSH representatives desired that the OSH inspector should follow up more effectively that OSH management systems are being implemented in practice. They should provide more advice to workplaces in order to help them to exceed the minimum level laid down by law. Workers' OSH representatives expressed clearly that the OSH inspector should more often impose binding obligations. The professional skills of the OSH inspectors need to correspond better to the needs of today's working life and to be able to react to system changes in the workplaces. The OSH enforcement approach should become more uniform and be harmonized between the individual OSH inspectors and the OSH agencies. The development of the quality of OSH inspections and the professional competence of inspectors are important aspects from the point of the effectiveness.