ethicalabs/Research-Intent-Collab huggingface dataset:
Litigating gender                         discrimination cases before the ECOWAS Community Court of Justice and the                         African Court on Human and Peoples' Rights

The African regional courts have decided on hundreds of human rights claims, but very few cases on women’s human rights and gender discrimination have been litigated before the courts. A court may only adjudicate on matters brought before it. If persons who have faced gender discrimination do not bring cases, the court will be limited in its ability to decide on them. This chapter examines the few cases before the ECOWAS Community Court of Justice (CCJ) and the African Court on Human and Peoples’ Rights (African Court) initiated by women or brought on their behalf. The goal is to critically evaluate how both courts have interpreted the rights of women enshrined in human rights laws and the facts presented before them.
Funding grant proposals for scientific research: retrospective analysis of scores by members of grant review panel

To quantify randomness and cost when choosing health and medical research projects for funding.Retrospective analysis.Grant review panels of the National Health and Medical Research Council of Australia.Panel members' scores for grant proposals submitted in 2009.The proportion of grant proposals that were always, sometimes, and never funded after accounting for random variability arising from differences in panel members' scores, and the cost effectiveness of different size assessment panels.59% of 620 funded grants were sometimes not funded when random variability was taken into account. Only 9% (n = 255) of grant proposals were always funded, 61% (n = 1662) never funded, and 29% (n=788) sometimes funded. The extra cost per grant effectively funded from the most effective system was $A18,541 (£11,848; €13,482; $19,343).Allocating funding for scientific research in health and medicine is costly and somewhat random. There are many useful research questions to be addressed that could improve current processes.
Semantic Spaces of Clinical Text : Leveraging Distributional Semantics for Natural Language Processing of Electronic Health Records

De stora mängder kliniska data som genereras i patientjournalsystem är en underutnyttjad resurs med en enorm potential att förbättra hälso- och sjukvården. Då merparten av kliniska data är i form av ostrukturerad text, vilken är utmanande för datorer att analysera, finns det ett behov av sofistikerade metoder som kan behandla kliniskt språk. Metoder som inte kräver märkta exempel utan istället utnyttjar statistiska egenskaper i datamängden är särskilt värdefulla, med tanke på den begränsade tillgången till annoterade korpusar i den kliniska domänen. System för informationsextraktion och språkbehandling behöver innehålla viss kunskap om semantik. En metod går ut på att utnyttja de distributionella egenskaperna hos språk – mer specifikt, statistisk över hur termer samförekommer – för att modellera den relativa betydelsen av termer i ett högdimensionellt vektorrum. Metoden har använts med framgång i en rad uppgifter för behandling av allmänna språk; dess tillämpning i den kliniska domänen har dock endast utforskats i mindre utsträckning. Genom att tillämpa modeller för distributionell semantik på klinisk text kan semantiska rum konstrueras utan någon tillgång till märkta exempel. Semantiska rum av klinisk text kan sedan användas i en rad medicinskt relevanta tillämpningar. Tillämpningen av distributionell semantik i den kliniska domänen illustreras här i tre användningsområden: (1) synonymextraktion av medicinska termer, (2) tilldelning av diagnoskoder och (3) identifiering av läkemedelsbiverkningar. Det krävs dock att vissa begränsningar eller utmaningar adresseras för att möjliggöra en effektiv tillämpning av distributionell semantik på ett brett spektrum av uppgifter som behandlar språk – både allmänt och, i synnerhet, kliniskt – såsom hur man kan modellera betydelsen av flerordstermer och redogöra för funktionen av negation: ett enkelt sätt att modellera parafrasering och negation i ett distributionellt semantiskt ramverk presenteras och utvärderas. Idén om ensembler av semantisk rum introduceras också; dessa överträffer användningen av ett enda semantiskt rum för synonymextraktion. Den här metoden möjliggör en kombination av olika modeller för distributionell semantik, med olika parameterkonfigurationer samt inducerade från olika korpusar. Detta är inte minst viktigt i den kliniska domänen, då det gör det möjligt att komplettera potentiellt begränsade mängder kliniska data med data från andra, mer lättillgängliga källor. Arbetet påvisar också vikten av att konfigurera dimensionaliteten av semantiska rum, i synnerhet när vokabulären är omfattande, vilket är vanligt i den kliniska domänen.
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design

Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural architectural search is computationally expensive, while model retrieval often yields suboptimal static checkpoints. To resolve this dilemma, we model the performance gains induced by fine-grained architectural modifications as edit-effect evidence and build evidence graphs from prior tasks. By constructing a retrieval-augmented model refinement framework, our proposed M-DESIGN dynamically weaves historical evidence to discover near-optimal modification paths. M-DESIGN features an adaptive retrieval mechanism that quickly calibrates the evolving transferability of edit-effect evidence from different sources. To handle out-of-distribution shifts, we introduce predictive task planners that extrapolate gains from multi-hop evidence, thereby reducing reliance on an exhaustive repository. Based on our model knowledge base of 67,760 graph neural networks across 22 datasets, extensive experiments demonstrate that M-DESIGN consistently outperforms baselines, achieving the search-space best performance in 26 out of 33 cases under a strict budget.
CPRet: A Dataset, Benchmark, and Model for Retrieval in Competitive Programming

Competitive programming benchmarks are widely used in scenarios such as programming contests and large language model assessments. However, the growing presence of duplicate or highly similar problems raises concerns not only about competition fairness, but also about the validity of competitive programming as a benchmark for model evaluation. In this paper, we propose a new problem, similar question retrieval, to tackle this issue. Due to the lack of both data and models, solving this problem is challenging. To this end, we introduce CPRet, a retrieval-oriented benchmark suite for competitive programming, covering four retrieval tasks: two code-centric (i.e., Text-to-Code, Code-to-Code) and two newly proposed problem-centric tasks (i.e., Problem-to-Duplicate, Simplified-to-Full) built from a combination of automatically crawled problem-solution data and manually curated annotations. Our contribution includes both high-quality training data and temporally separated test sets for reliable evaluation. Besides, we further develop two task-specialized retrievers based on this dataset: CPRetriever-Code, trained with a novel Group-InfoNCE loss for problem-code alignment, and CPRetriever-Prob, fine-tuned for identifying problem-level similarity. Both models achieve strong results and are open-sourced for local use. Finally, we analyze LiveCodeBench and find that high-similarity problems inflate model pass rates and reduce differentiation, underscoring the need for similarity-aware evaluation in future benchmarks.   Github: https://github.com/coldchair/CPRet   Online Demo: https://www.cpret.online/
Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16

Abstract                   AlphaFold2 and AlphaFold3 have revolutionized protein structure prediction by enabling high-accuracy structure predictions for most single-chain proteins. However, obtaining high-quality predictions for difficult targets with shallow or noisy multiple sequence alignments and complicated multi-domain architectures remains challenging. We present MULTICOM4, an integrative structure prediction system that uses diverse MSA generation, large-scale model sampling, and an ensemble model quality assessment strategy to improve model generation and ranking of AlphaFold2 and AlphaFold3. In the 16th Critical Assessment of Techniques for Protein Structure Prediction, our predictors built on MULTICOM4 ranked among the top out of 120 predictors in tertiary structure prediction and outperformed a standard AlphaFold3 predictor. Our best predictor achieved an average TM-score of 0.902 for 84 CASP16 domains, with top-1 predictions reaching high accuracy (TM-score&gt;0.9) for 73.8% and correct folds (TM-score&gt;0.5) for 97.6% of domains. For best-of-top-5 predictions, all domains were correctly folded. The results show that MSA engineering using different sequence databases, alignment tools, and domain segmentation along with extensive model sampling is critical to generate accurate structural models. Combining complementary QA methods with model clustering further improves ranking reliability. These advances provide practical strategies for modeling difficult single-chain proteins in structural biology and drug discovery.
The role of e-participation and open data in evidence-based policy decision making in local government

This work evolved in the context of the project Policy Compass (http://policycompass.eu/), a project co-funded by the EC within FP7, Grant agreement no: 612133.
11 Tera-FLOP/s photonic convolutional accelerator and deep learning optical neural networks

Abstract         Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-FLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
EXPERIMENTAL VERIFICATION OF OPTIMAL START-UP POLICIES IN A CONTINUOUS STIRRED BIOREACTOR

Abstract   In this work dynamic optimal start-up policies were computed and validate in an experimental lab scale continuous bioreactor. Using the theoretical optimal profile a reduction in the start-up time was achieved when compared to simple step-like changes in the manipulated variables. Therefore, the use of optimal start-up policies led to raw material savings. To test the optimal start-up policies two operating points were selected. Experiments were run at these conditions to fit kinetic rate constants leading to a reliable model representation of the addressed biosystem. Our aim was to demonstrate that dynamic optimization is a valuable tool when approaching the dynamic operation of bioreactors assuming that a good model representation of the system is available.
Russia, humanitarian intervention and the Responsibility to Protect: the case of Syria

Western analysis perceives Russian approaches to issues of humanitarian intervention and the Responsibility to Protect (R2P) as running counter to western-inspired international norms. This debate has surfaced with some vigour over Russia's policy in the Syria conflict where, in order to protect its strategic interests in Syria, an obstructionist Moscow has been accused of ignoring humanitarian considerations and allowing time for the Assad regime to crush the opposition by vetoing a resolution threatening to impose sanctions. While Russian approaches are undoubtedly explained by a desire to maximize its growing political influence and trade advantages to serve its legitimate foreign policy interests, and while Moscow's attitudes to intervention and R2P exhibit important differences from those of the major western liberal democracies, its arguments are in fact framed within a largely rational argument rooted in ‘traditional’ state-centred international law. This article first highlights key arguments in the scholarly literature on intervention and R2P before going on to examine the evolution of Russian views on these issues. The analysis then focuses on the extent to which Moscow's arguments impact on international legal debates on the Libya and Syria conflicts. The article then seeks to explore how Russian approaches to intervention/R2P reflect fundamental trends in its foreign policy thinking and its quest for legitimacy in a negotiated international order. Finally, it attempts to raise some important questions regarding Russia's role in the future direction of the intervention/R2P debates.
Quantum Error Mitigation for near-future Quantum Computation

Kvantedatamaskiner har potensiale til å kunne løse spesifikke beregningsproblemer eksponentielt raskere enn noen klassisk datamaskin er i stand til. I nærliggende fremtid er derimot kvantedatamaskiner begrensede når det kommer til antallet tilgjengelige qubiter, og i lengden av mulige kvanteberegninger grunner mye støy. På sikt vil kvantefeilkorreksjon kunne korrigere feil til vilkårlig presisjon over lange kvanteberegninger, men de krever et stort antall ekstra qubiter. Vi fokuserer derfor i stedet på dempe effekten av kvantefeil, så kalt kvantefeilmitigering. I denne oppgaven studerer vi ulike teknikker for å dempe kvantefeil i anvendelser av kvantedatamaskiner i nær fremtid. Målet vårt er å undersøke og sammenligne ulike slike teknikker, og vi foreslår en variant av nullstøysekstrapoleringsteknikken men en ny metode for støyforsterkning på kvantekrets-nivå. Vi anvendte nullstøysekstrapoleringsteknikken på en representativ testkrets og oppnådde en relativ feil på 3.3%, betydelig bedre enn den opprinnelige feilen i kretsen på 30%, i det beste testede tilfellet. Vår metode gav også bedre resultater enn en eksisterende støyforsterkningsmetode for flere ulike støymodeller. Vi så derimot at denne teknikken ga en betydelig økning i beregningskostnad, spesielt når vi inkluderer mange støyforsterkningsledd. For kun 2 og 3 ledd fant vi fortsatt en betydelig forbedring i den relative feilen, til henholdsvis 14% og 8.3%, men i disse tilfellene økte beregningskostnaden med under én størrelsesorden. Videre utforsket vi potensialet i å kombinere to ulike teknikker for støydemping. Vi kombinerte vår variant av nullstøysekstrapoleringen men en teknikk for støydemping ved feildeteksjon. Dette anvendte vi på en algoritme for simuleringer av H2-molekylet på en simulator med en enkel støymodell. Den kombinerte metoden gav bedre resultater enn både nullstøysekstrapolerings- og feildeteksjons-teknikkene gav for seg selv. Med en feilrate på 0.1% oppnådde den kombinerte metoden en feil under den kjemiske nøyaktigheten. Teknikken med feildeteksjon oppnådde ikke dette for noen av de testede tilfellene, men teknikken kom med en betydelig lavere beregningskostnad enn nullstøysekstrapolering, men den krever noen ekstra qubiter. Å kombinere de to støymitigeringsteknikkene viser dermed potensiale til å kunne oppnå en større grad av støydemping
A MEASUREMENT OF THE DAMPING TAIL OF THE COSMIC MICROWAVE BACKGROUND POWER SPECTRUM WITH THE SOUTH POLE TELESCOPE

We present a measurement of the angular power spectrum of the cosmic microwave background (CMB) using data from the South Pole Telescope (SPT). The data consist of 790 square degrees of sky observed at 150 GHz during 2008 and 2009. Here we present the power spectrum over the multipole range 650 &lt; ell &lt; 3000, where it is dominated by primary CMB anisotropy. We combine this power spectrum with the power spectra from the seven-year Wilkinson Microwave Anisotropy Probe (WMAP) data release to constrain cosmological models. We find that the SPT and WMAP data are consistent with each other and, when combined, are well fit by a spatially flat, LCDM cosmological model. The SPT+WMAP constraint on the spectral index of scalar fluctuations is ns = 0.9663 +/- 0.0112. We detect, at ~5-sigma significance, the effect of gravitational lensing on the CMB power spectrum, and find its amplitude to be consistent with the LCDM cosmological model. We explore a number of extensions beyond the LCDM model. Each extension is tested independently, although there are degeneracies between some of the extension parameters. We constrain the tensor-to-scalar ratio to be r &lt; 0.21 (95% CL) and constrain the running of the scalar spectral index to be dns/dlnk = -0.024 +/- 0.013. We strongly detect the effects of primordial helium and neutrinos on the CMB; a model without helium is rejected at 7.7-sigma, while a model without neutrinos is rejected at 7.5-sigma. The primordial helium abundance is measured to be Yp = 0.296 +/- 0.030, and the effective number of relativistic species is measured to be Neff = 3.85 +/- 0.62. The constraints on these models are strengthened when the CMB data are combined with measurements of the Hubble constant and the baryon acoustic oscillation feature. Notable improvements include ns = 0.9668 +/- 0.0093, r &lt; 0.17 (95% CL), and Neff = 3.86 +/- 0.42. The SPT+WMAP data show...
Self-determination: from decolonization to deterritorialization

Unresolved claims to self-determination are among the biggest challenges in global politics today. A large number of groups in all parts of the world, from indigenous peoples to religious, linguistic and ethnic minorities, seek independence or greater participation in the determination of their futures. However, several problems associated with the conceptualization of self-determination are limiting opportunities for the peaceful resolution of such claims. The international community lacks a coherent legal framework for extending the right of self-determination to all peoples, particularly to groups outside the decolonization context. More seriously, the issue of self-determination remains linked to a deeply entrenched concept of state sovereignty which revolves around an artificial link between nations, states and territorial integrity. Given that the boundaries of identity and community are fluid and constantly shifting, this territorial model of sovereignty more often precipitates rather than accommodates claims to self-determination. We thus argue for the need to deterritorialize self-determination, which would place greater emphasis on human rights and democratic participation. It would also open up more possibilities to deal with self-determination claims in the context of alternative political arrangements, such as autonomy, federalism, multiculturalism or overlapping sovereignties.
An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database

BACKGROUND: Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer. OBJECTIVE: To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformatics METHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses. RESULTS: We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer. CONCLUSIONS: The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.
Liberal international criminal law and legal memory: deconstructing the production of witness memories at the International Criminal Tribunal for Rwanda

International criminal tribunals and courts, such as the International Criminal Tribunal for Rwanda (ICTR), are commonly understood within legal transitional justice scholarship as the primary response to mass human rights violations, not only in addressing impunity, but also in uncovering the truth of what happened and why. This conceptually orientated article aims to deconstruct legal witnessing and memory production at the ICTR in order to critique claims in legal scholarship that international criminal institutions are able to produce a collective memory of mass rights violations. Specifically, the article proposes an original conceptual framework using insights from critical theory, Giorgio Agamben (witness) and Paul Ricoeur (memory), which it is argued extends our understanding of the scope, and limitations, of liberal Western criminal institutions� (in)ability to make sense of past atrocities.
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.