Miracle-dz/UniFAR-FTUs huggingface dataset:
Generational Variance Reduction in Monte Carlo Criticality Simulations as a Way of Mitigating Unwanted Correlations

Monte Carlo criticality simulations are widely used in nuclear safety demonstrations, as they offer an arbitrarily precise estimation of global and local tallies while making very few assumptions. However, since the inception of such numerical approaches, it is well known that bias might affect both the estimation of errors on these tallies and the tallies themselves. In particular, stochastic modeling approaches developed in the past decade have shed light on the prominent role played by spatial correlations through a phenomenon called neutron clustering. This effect is particularly of great significance when simulating loosely coupled systems (i.e., with a high dominance ratio). In order to tackle this problem, this paper proposes to recast the power iteration technique of Monte Carlo criticality codes into a variance reduction technique called Adaptative Multilevel Splitting. The central idea is that iterating over neutron generations can be seen as pushing a sub-population of neutrons towards a generational detector (instead of a spatial detector as variance reduction techniques usually do). While both approaches allow for neutron population control, the former blindly removes or splits neutrons. In contrast, the latter optimizes spatial, generational, and spectral attributes of neutrons when they are removed or split through an adjoint flux estimation, hence tempering both generational and spatial correlations. This is illustrated in the present article with a simple case of a bare slab reactor in the one speed theory on which the Adaptive Multilevel Splitting was applied and compared to variations of the Monte Carlo power iteration method used in neutron transport. Besides looking at the resulting efficiency of the methods, this work also aims at highlighting the main mechanisms of the Adaptive Multilevel Splitting in criticality calculations.
Constraining cosmological parameters from N-body simulations with variational Bayesian neural networks

Introduction: Methods based on deep learning have recently been applied to recover astrophysical parameters, thanks to the ability of these techniques to capture information from complex data. One of these schemes is the approximate Bayesian neural network (BNN), which has demonstrated to yield a posterior distribution into the parameter space that is extremely helpful for uncertainty quantification. However, modern neural networks tend to produce overly confident uncertainty estimates and introduce bias when applying BNNs to data. Method: In this work, we implement multiplicative normalizing flows (MNFs), a family of approximate posteriors for the parameters of BNNs with the purpose of enhancing the flexibility of the variational posterior distribution, to extract Ω m , h, and σ8 from the QUIJOTE simulations. We compared the latter method with the standard BNNs and the Flipout estimator. Results: We have found that the use of MNFs consistently outperforms the standard BNNs with a percent difference in the mean squared error of 21%, in addition to high-accuracy extraction of σ8 (r2 = 0.99), with precise and consistent uncertainty estimates. Discussions: These findings imply that MNFs provide a more realistic predictive distribution closer to the true posterior, mitigating the bias introduced by the variational approximation and allowing us to work with well-calibrated networks.
The NANOGrav 12.5 yr Data Set: Bayesian Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries

Pulsar timing array collaborations, such as the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), are seeking to detect nanohertz gravitational waves emitted by supermassive black hole binaries formed in the aftermath of galaxy mergers. We have searched for continuous waves from individual circular supermassive black hole binaries using NANOGrav’s recent 12.5 yr data set. We created new methods to accurately model the uncertainties on pulsar distances in our analysis, and we implemented new techniques to account for a common red-noise process in pulsar timing array data sets while searching for deterministic gravitational wave signals, including continuous waves. As we found no evidence for continuous waves in our data, we placed 95% upper limits on the strain amplitude of continuous waves emitted by these sources. At our most sensitive frequency of 7.65 nHz, we placed a sky-averaged limit of h 0 < (6.82 ± 0.35) × 10−15, and h 0 < (2.66 ± 0.15) × 10−15 in our most sensitive sky location. Finally, we placed a multimessenger limit of <(1.41±0.02)×109M⊙ on the chirp mass of the supermassive black hole binary candidate 3C 66B.
Sigma terms of the baryon octet in $N_\mathrm{f} = 2+1$ QCD with Wilson quarks

A lot of progress has been made in the direct determination of nucleon sigma terms. Using similar methods, we consider the sigma terms of the other octet baryons as well. These are determined on CLS gauge field ensembles employing the L\"uscher-Weisz gluon action and the Sheikholeslami-Wohlert fermion action with $N_\mathrm{f} = 2 + 1$. The ensembles have pion masses ranging from ${410}\,\mathrm{MeV}$ down to the physical value and lattice spacings covering a range between ${0.098}\,\mathrm{fm}$ and ${0.039}\,\mathrm{fm}$. We present some preliminary results for the pion and strange sigma terms and compare to indirect determinations. To do so, we discuss multi-state fits to tackle the well-known problem of excited state contamination comparing the ratio and summation methods also including priors.
Efficient Intra-Rack Resource Disaggregation for HPC Using Co-Packaged DWDM Photonics

The diversity of workload requirements and increasing hardware heterogeneity in emerging high performance computing (HPC) systems motivate resource disaggregation. Resource disaggregation allows compute and memory resources to be allocated individually as required to each workload. However, it is unclear how to efficiently realize this capability and cost-effectively meet the stringent bandwidth and latency requirements of HPC applications. To that end, we describe how modern photonics can be co-designed with modern HPC racks to implement flexible intra-rack resource disaggregation and fully meet the bit error rate (BER) and high escape bandwidth of all chip types in modern HPC racks. Our photonic-based disaggregated rack provides an average application speedup of 11% (46% maximum) for 25 CPU and 61% for 24 GPU benchmarks compared to a similar system that instead uses modern electronic switches for disaggregation. Using observed resource usage from a production system, we estimate that an iso-performance intra-rack disaggregated HPC system using photonics would require 4× fewer memory modules and 2× fewer NICs than a non-disaggregated baseline.
MALTA-Cz: a radiation hard full-size monolithic CMOS sensor with small electrodes on high-resistivity Czochralski substrate

Depleted Monolithic Active Pixel Sensor (DMAPS) sensors developed in the Tower Semiconductor 180 nm CMOS imaging process have been designed in the context of the ATLAS ITk upgrade Phase-II at the HL-LHC and for future collider experiments. The “MALTA-Czochralski (MALTA-Cz)” full size DMAPS sensor has been developed with the goal to demonstrate a radiation hard, thin CMOS sensor with high granularity, high hit-rate capability, fast response time and superior radiation tolerance. The design targets radiation hardness of > 1015 (1 MeV) neq/cm2 and 100 Mrad TID. The sensor shall operate as tracking sensor with a spatial resolution of ≈ 10 μm and be able to cope with hit rates in excess of 100 MHz/cm2 at the LHC bunch crossing frequency of 40 MHz. The 512 × 512 pixel sensor uses small collection electrodes (3.5 μm) to minimize capacitance. The small pixel size (36.4 × 36.4 μm2) provides high spatial resolution. Its asynchronous readout architecture is designed for high hit-rates and fast time response in triggered and trigger-less detector applications. The readout architecture is designed to stream all hit data to the multi-channel output which allows an off-sensor trigger formation and the use of hit-time information for event tagging. The sensor manufacturing has been optimised through process adaptation and special implant designs to allow the manufacturing of small electrode DMAPS on thick high-resistivity p-type Czochralski substrate. The special processing ensures excellent charge collection and charge particle detection efficiency even after a high level of radiation. Furthermore the special implant design and use of a Czochralski substrate improves the sensor's time resolution. This paper presents a summary of sensor design optimisation through process and implant choices and TCAD simulation to model the signal response. Beam and laboratory test results on unirradiated and irradiated sensors have shown excellent detection efficiency after a dose of 2 × 1015 1 MeV neq/cm2. The time resolution of the sensor is measured to be σ = 2 ns.
There is No Big Brother or Small Brother:Knowledge Infusion in Language Models for Link Prediction and Question Answering

The integration of knowledge graphs with deep learning is thriving in improving the performance of various natural language processing (NLP) tasks. In this paper, we focus on knowledge-infused link prediction and question answering using language models, T5, and BLOOM across three domains:Aviation, Movie, and Web. In this context, we infuse knowledge in large and small language models and study their performance, and find the performance to be similar. For the link prediction task on the Aviation Knowledge Graph, we obtain a 0.2 hits@1 score using T5-small, T5-base, T5-large, and BLOOM. Using template-based scripts, we create a set of 1 million synthetic factoid QA pairs in the aviation domain from National Transportation Safety Board (NTSB) reports. On our curated QA pairs, the three models of T5 achieve a 0.7 hits@1 score. We validate our findings with the paired student t test and Cohen’s kappa scores. For link prediction on Aviation Knowledge Graph using T5-small and T5-large, we obtain a Cohen’s kappa score of 0.76, showing substantial agreement between the models. Thus, we infer that small language models perform similar to large language models with the infusion of knowledge.
Lax Eigenvalues in the Zero-Dispersion Limit for the Benjamin-Ono Equation on the Torus

We consider the zero-dispersion limit for the Benjamin-Ono equation on the torus for bell shaped initial data. Using the approximation by truncated Fourier series, we transform the eigenvalue equation for the Lax operator into a problem in the complex plane. Then, we use the steepest descent method to get asymptotic expansions of the Lax eigenvalues. As a consequence, we determine the weak limit of solutions as the dispersion parameter goes to zero, as long as the initial data is an even bell shaped potential.
Investigating Conversational Search Behavior For Domain Exploration

Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.
SantaCoder: don't reach for the stars!

The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.
Combinatorial Pure Exploration of Causal Bandits

The combinatorial pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we choose a subset of variables to intervene or do no intervention, and observe the random outcomes of all random variables, with the goal that using as few rounds as possible, we can output an intervention that gives the best (or almost best) expected outcome on the reward variable $Y$ with probability at least $1-\delta$, where $\delta$ is a given confidence level. We provide the first gap-dependent and fully adaptive pure exploration algorithms on two types of causal models -- the binary generalized linear model (BGLM) and general graphs. For BGLM, our algorithm is the first to be designed specifically for this setting and achieves polynomial sample complexity, while all existing algorithms for general graphs have either sample complexity exponential to the graph size or some unreasonable assumptions. For general graphs, our algorithm provides a significant improvement on sample complexity, and it nearly matches the lower bound we prove. Our algorithms achieve such improvement by a novel integration of prior causal bandit algorithms and prior adaptive pure exploration algorithms, the former of which utilize the rich observational feedback in causal bandits but are not adaptive to reward gaps, while the latter of which have the issue in reverse.
Semistable degenerations and period spaces for polarized K3 surfaces

Modular compactifications of moduli spaces for polarized K3 surfaces are constructed using the tools of logarithmic geometry in the sense of Fontaine and Illusie. The relationship between these new moduli spaces and the classical minimal and toroidal compactifications of period spaces are discussed, and it is explained how the techniques of this paper yield models for the latter spaces over number fields. The paper also contains a discussion of Picard functors for log schemes and a logarithmic version of Artin’s method for proving representability by an algebraic stack.
The phosphate-induced small RNA EsrL promotes E. coli virulence, biofilm formation, and intestinal colonization

Escherichia coli are part of the normal intestinal microbiome, but some enterohemorrhagic E. coli (EHEC) and enteropathogenic E. coli (EPEC) strains can cause potentially life-threatening gastroenteritis. Virulence factors underlying the ability of EHEC and EPEC to cause disease include those encoded in the locus of the enterocyte effacement (LEE) pathogenicity island. Here, we demonstrated that EsrL, a small RNA present in many E. coli strains, promoted pathogenicity, adhesion, and biofilm formation in EHEC and EPEC. PhoB, the response regulator of the two-component system that controls cellular responses to phosphate, directly repressed esrL expression under low-phosphate conditions. A phosphate-rich environment, similar to that of the human intestine, relieved PhoB-mediated repression of esrL. EsrL interacted with and stabilized the LEE-encoded regulator (ler) transcript, which encodes a transcription factor for LEE genes, leading to increased bacterial adhesion to cultured cells and colonization of the rabbit colon. EsrL also bound to and stabilized the fimC transcript, which encodes a chaperone that is required for the assembly of type 1 pili, resulting in enhanced cell adhesion in pathogenic E. coli and enhanced biofilm formation in pathogenic and nonpathogenic E. coli. Our findings demonstrate that EsrL stimulates the expression of virulence genes in both EHEC and EPEC under phosphate-rich conditions, thus promoting the pathogenicity of EHEC and EPEC in the nutrient-rich gut environment. Description A small RNA enhances E. coli pathogenicity in phosphate-rich conditions such as the gut. Phosphate-stimulated virulence To cause disease, enterohemorrhagic E. coli (EHEC) and enteropathogenic E. coli (EPEC) must first adhere to intestinal cells (enterocytes). Adhesion requires the expression of genes in the LEE pathogenicity island driven by the master regulator Ler. Jia et al. identified a small RNA, EsrL, that promoted EHEC and EPEC adhesion to cultured human cells and EHEC colonization of the rabbit colon. Under high-phosphate conditions similar to those in the gut, EsrL expression was derepressed and EsrL bound to and stabilized the ler transcript. Stabilization of another transcript by EsrL ultimately enhanced the capacity of EPEC and nonpathogenic E. coli to form biofilms, which protects the bacteria from antibiotics and host defenses. Stabilization of both transcripts likely contributes to E. coli virulence and biofilm formation in the high-phosphate environment of the gut. –AMV
Poroelastic modelling reveals the cooperation between two mechanisms for albuminuria

Albuminuria occurs when albumin leaks abnormally into the urine. Its mechanism remains unclear. A gel-compression hypothesis attributes the glomerular barrier to compression of the glomerular basement membrane (GBM) as a gel layer. Loss of podocyte foot processes would allow the gel layer to expand circumferentially, enlarge its pores and leak albumin into the urine. To test this hypothesis, we develop a poroelastic model of the GBM. It predicts GBM compression in healthy glomerulus and GBM expansion in the diseased state, essentially confirming the hypothesis. However, by itself, the gel compression and expansion mechanism fails to account for two features of albuminuria: the reduction in filtration flux and the thickening of the GBM. A second mechanism, the constriction of flow area at the slit diaphragm downstream of the GBM, must be included. The cooperation between the two mechanisms produces the amount of increase in GBM porosity expected in vivo in a mutant mouse model, and also captures the two in vivo features of reduced filtration flux and increased GBM thickness. Finally, the model supports the idea that in the healthy glomerulus, gel compression may help maintain a roughly constant filtration flux under varying filtration pressure.
Two negative ion groups in volume H− sources

The two-laser photodetachment technique has been used to measure the dynamics of the recovery of negative ion density in the center of the multicusp H− ion source Camembert III after laser irradiation. Several series of experiments in pure hydrogen and hydrogen–argon mixtures for the pressures ranging from 0.85 to 3 mTorr were performed. The curve of the dynamics of H− recovery serves to determine the temperature of negative ions. The single-temperature fits of the experimental curves were not satisfactory, so several changes were introduced into the process of treatment of the experimental data. As a result two negative ion populations with different temperatures were identified. These populations are formed as a result of the existence of two H− production regions with different plasma potentials. The low temperature population (0.03–0.25 eV) corresponds to the ions formed in the central source region. The higher temperature population corresponds to ions formed near the walls or filaments (driver regio.
Drivers and facilitators of the illegal killing of elephants across 64 African sites

Ivory poaching continues to threaten African elephants. We (1) used criminology theory and literature evidence to generate hypotheses about factors that may drive, facilitate or motivate poaching, (2) identified datasets representing these factors, and (3) tested those factors with strong hypotheses and sufficient data quality for empirical associations with poaching. We advance on previous analyses of correlates of elephant poaching by using additional poaching data and leveraging new datasets for previously untested explanatory variables. Using data on 10 286 illegally killed elephants detected at 64 sites in 30 African countries (2002–2020), we found strong evidence to support the hypotheses that the illegal killing of elephants is associated with poor national governance, low law enforcement capacity, low household wealth and health, and global elephant ivory prices. Forest elephant populations suffered higher rates of illegal killing than savannah elephants. We found only weak evidence that armed conflicts may increase the illegal killing of elephants, and no evidence for effects of site accessibility, vegetation density, elephant population density, precipitation or site area. Results suggest that addressing wider systemic challenges of human development, corruption and consumer demand would help reduce poaching, corroborating broader work highlighting these more ultimate drivers of the global illegal wildlife trade.
JET ROTATION INVESTIGATED IN THE NEAR-ULTRAVIOLET WITH THE HUBBLE SPACE TELESCOPE IMAGING SPECTROGRAPH

We present results of the second phase of our near-ultraviolet investigation into protostellar jet rotation using the Hubble Space Telescope Imaging Spectrograph. We obtain long-slit spectra at the base of five T Tauri jets to determine if there is a difference in radial velocity between the jet borders which may be interpreted as a rotation signature. These observations are extremely challenging and push the limits of current instrumentation, but have the potential to provide long-awaited observational support for the magnetocentrifugal mechanism of jet launching in which jets remove angular momentum from protostellar systems. We successfully detect all five jet targets (from RW Aur, HN Tau, DP Tau, and CW Tau) in several near-ultraviolet emission lines, including the strong Mg ii doublet. However, only RW Aur's bipolar jet presents a sufficiently high signal-to-noise ratio to allow for analysis. The approaching jet lobe shows a difference of 10 km s−1 in a direction which agrees with the disk rotation sense, but is opposite to previously published optical measurements for the receding jet. The near-ultraviolet difference is not found six months later, nor is it found in the fainter receding jet. Overall, in the case of RW Aur, differences are not consistent with a simple jet rotation interpretation. Indeed, given the renowned complexity and variability of this system, it now seems likely that any rotation signature is confused by other influences, with the inevitable conclusion that RW Aur is not suited to a jet rotation study.