Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards reliable projections of global mean surface temperature

Quantifying the risk of global warming exceeding critical targets such as 2.0 K requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. These approaches give reliable projections of GMST when tested in a perfect model setting, and when applied to observations lead to well constrained projections with lower mean warming and narrower projection bands than previous estimates. Despite the reduced uncertainty, the lower warming leads to a greatly reduced probability of exceeding the 2.0 K warming target.
Some rigorous results on the phase transition of finitary random interlacements

In this paper, we show several rigorous results on the phase transition of Finitary Random Interlacements (FRI). For the high intensity regime, we show the existence of a critical ﬁber length, and ﬁnd its exact asymptotic as intensity goes to inﬁnity. At the same time, our result for the low intensity regime proves the global existence of a non-trivial phase transition with respect to the system intensity.
Air-Ground Collaborative Mobile Edge Computing: Architecture, Challenges, and Opportunities

By pushing computation, cache, and network control to the edge, mobile edge computing (MEC) is expected to play a leading role in fifth generation (5G) and future sixth generation (6G). Nevertheless, facing ubiquitous fast-growing computational demands, it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments (UEs). To address this issue, we propose an air-ground collaborative MEC (AGC-MEC) architecture in this article. The proposed AGC-MEC integrates all potentially available MEC servers within air and ground in the envisioned 6G, by a variety of collaborative ways to provide computation services at their best for UEs. Firstly, we introduce the AGC-MEC architecture and elaborate three typical use cases. Then, we discuss four main challenges in the AGC-MEC as well as their potential solutions. Next, we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy. Finally, we highlight several potential research directions of the AGC-MEC.
Improving type information inferred by decompilers with supervised machine learning

In software reverse engineering, decompilation is the process of recovering source code from binary files. Decompilers are used when it is necessary to understand or analyze software for which the source code is not available. Although existing decompilers commonly obtain source code with the same behavior as the binaries, that source code is usually hard to interpret and certainly differs from the original code written by the programmer. Massive codebases could be used to build supervised machine learning models aimed at improving existing decompilers. In this article, we build different classification models capable of inferring the high-level type returned by functions, with significantly higher accuracy than existing decompilers. We automatically instrument C source code to allow the association of binary patterns with their corresponding high-level constructs. A dataset is created with a collection of real open-source applications plus a huge number of synthetic programs. Our system is able to predict function return types with a 79.1% F1-measure, whereas the best decompiler obtains a 30% F1-measure. Moreover, we document the binary patterns used by our classifier to allow their addition in the implementation of existing decompilers.
Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19

COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic and safely reopen society. Essential analytics tools such as contact tracing, super-spreader event detection, and exposure mapping require collecting and analyzing sensitive user information. The increasing use of such powerful data-driven applications necessitates a secure, privacy-preserving infrastructure for computation on personal data. In this paper, we analyze two such computing infrastructures under development at the University of Illinois at Urbana-Champaign to track and mitigate the spread of COVID-19. First, we present Safer Illinois, a system for decentralized health analytics supporting two applications currently deployed with widespread adoption: digital contact tracing and COVID-19 status cards. Second, we introduce the RokWall architecture for privacy-preserving centralized data analytics on sensitive user data. We discuss the architecture of these systems, design choices, threat models considered, and the challenges we experienced in developing production-ready systems for sensitive data analysis.
Study of CeO2 and Its Native Defects by Density Functional Theory with Repulsive Potential

We investigated the native point defects in CeO2 by the density functional theory with repulsive potential (DFT+U) method and by use of a nonlinear core-corrected norm-conserving Ce pseudopotential. We find the neutral oxygen vacancy (VO0) in CeO2 to have a very low formation energy of only 0.39 eV in the O-poor limit. It is a deep donor with negative U behavior, stable only in its neutral and doubly positive states. The anion Frenkel defect is found to be the lowest energy disorder defect, with a formation energy of only 2.08 eV/defect site. These low formation energies arise from the improved transferability of our Ce pseudopotential for its +3 and +4 valence states. The negative U behavior of VO leads to excellent photocatalytic behavior, while the low formation energy of the anion Frenkel defect leads to a superior oxygen storage-and-release capability.
Modelling and optimal control of multi strain epidemics, with application to COVID-19

Reinfection and multiple viral strains are among the latest challenges in the current COVID-19 pandemic. In contrast, epidemic models often consider a single strain and perennial immunity. To bridge this gap, we present a new epidemic model that simultaneously considers multiple viral strains and reinfection due to waning immunity. The model is general, applies to any viral disease and includes an optimal control formulation to seek a trade-off between the societal and economic costs of mitigation. We validate the model, with and without mitigation, in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil. The model can derive optimal mitigation strategies for any number of viral strains, whilst also evaluating the effect of distinct mitigation costs on the infection levels. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, and therefore demand more restrictive measures in the future.
Evidence that Ultra-high-energy Gamma Rays Are a Universal Feature near Powerful Pulsars

The highest-energy known gamma-ray sources are all located within 0. °5 of extremely powerful pulsars. This raises the question of whether ultra-high-energy (UHE; >56 TeV) gamma-ray emission is a universal feature expected near pulsars with a high spin-down power. Using four years of data from the High Altitude Water Cherenkov Gamma-Ray Observatory, we present a joint-likelihood analysis of 10 extremely powerful pulsars to search for subthreshold UHE gamma-ray emission correlated with these locations. We report a significant detection (>3σ), indicating that UHE gamma-ray emission is a generic feature of powerful pulsars. We discuss the emission mechanisms of the gamma rays and the implications of this result. The individual environment, such as the magnetic field and particle density in the surrounding area, appears to play a role in the amount of emission.
Porcupine: a synthesizing compiler for vectorized homomorphic encryption

Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly on encrypted data. Despite its promise, HE has seen limited use due to performance overheads and compilation challenges. Recent work has made significant advances to address the performance overheads but automatic compilation of efficient HE kernels remains relatively unexplored. This paper presents Porcupine, an optimizing compiler that generates vectorized HE code using program synthesis. HE poses three major compilation challenges: it only supports a limited set of SIMD-like operators, it uses long-vector operands, and decryption can fail if ciphertext noise growth is not managed properly. Porcupine captures the underlying HE operator behavior so that it can automatically reason about the complex trade-offs imposed by these challenges to generate optimized, verified HE kernels. To improve synthesis time, we propose a series of optimizations including a sketch design tailored to HE to narrow the program search space. We evaluate Porcupine using a set of kernels and show speedups of up to 52% (25% geometric mean) compared to heuristic-driven hand-optimized kernels. Analysis of Porcupine’s synthesized code reveals that optimal solutions are not always intuitive, underscoring the utility of automated reasoning in this domain.
Word Alignment by Fine-tuning Embeddings on Parallel Corpora

Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs. The great majority of past work on word alignment has worked by performing unsupervised learning on parallel text. Recently, however, other work has demonstrated that pre-trained contextualized word embeddings derived from multilingually trained language models (LMs) prove an attractive alternative, achieving competitive results on the word alignment task even in the absence of explicit training on parallel data. In this paper, we examine methods to marry the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel text with objectives designed to improve alignment quality, and proposing methods to effectively extract alignments from these fine-tuned models. We perform experiments on five language pairs and demonstrate that our model can consistently outperform previous state-of-the-art models of all varieties. In addition, we demonstrate that we are able to train multilingual word aligners that can obtain robust performance on different language pairs.
Infinitely Many Twin Prime Polynomials of Odd Degree

Abstract While the twin prime conjecture is still famously open, it holds true in the setting of finite fields: There are infinitely many pairs of monic irreducible polynomials over that differ by a fixed constant, for each . Elementary, constructive proofs were given for different cases by Hall and Pollack. In the same spirit, we discuss the construction of a further infinite family of twin prime tuples of odd degree, and its relations to the existence of certain Wieferich primes and to arithmetic properties of the combinatorial Bell numbers.
Small Molecules to Destabilize the ACE2-RBD Complex: A Molecular Dynamics Study for Potential COVID-19 Therapeutics

The ongoing COVID-19 pandemic has infected millions of people, claimed hundreds of thousands of lives, and made a worldwide health emergency. Understanding the SARS-CoV-2 mechanism of infection is crucial in the development of potential therapeutics and vaccines. The infection process is triggered by direct binding of the SARS-CoV-2 receptor-binding domain (RBD) to the host cell receptor, Angiotensin-converting enzyme 2 (ACE2). Many efforts have been made to design or repurpose therapeutics to deactivate RBD or ACE2 and prevent the initial binding. In addition to direct inhibition strategies, small chemical compounds might be able to interfere and destabilize the meta-stable, pre-fusion complex of ACE2-RBD. This approach can be employed to prevent the further progress of virus infection at its early stages. In this study, Molecular docking is employed to analyze the binding of two chemical compounds, SSAA09E2 and Nilotinib, with the druggable pocket of the ACE2-RBD complex. The structural changes as a result of the interference with the ACE2-RBD complex are analyzed by molecular dynamics simulations. Results show that both Nilotinib and SSAA09E2 can induce significant conformational changes in the ACE2-RBD complex, intervene with the hydrogen bonds, and influence the flexibility of proteins. Moreover, essential dynamics analysis suggests that the presence of small molecules can trigger large-scale conformational changes that may destabilize the ACE2-RBD complex.
Multidrug-resistant Opportunistic and Pathogenic Bacteria Contaminate Algerian Banknotes Currency

Abstract Currency is one of the most exchanged items in human communities as it is used daily in exchange for goods and services. It is handled by persons with different hygiene standards and can transit in different environments. Hence, money can constitute a reservoir for different types of human pathogens. This study aimed to evaluate the potential of Algerian banknotes to shelter opportunistic pathogenic and multiresistant bacteria. To that end, 200 circulating notes of four different denominations were collected from various places and analyzed for their bacterial loads and contents. Besides, predominant strains were identified and characterized by biochemical and molecular methods, and their resistance profiles against 34 antibiotics were determined. Our results indicated that 100% of the studied banknotes were contaminated with bacteria. The total bacterial concentrations were relatively high, and different bacterial groups were grown, showing important diversity. In total, 48 predominant strains were identified as belonging to 17 genera. Staphylococcus and Micrococcus were the most prevalent genera, followed by Bacillus, Pseudomonas, and Acinetobacter. Antibiotic susceptibility testing showed that all the isolates harbored resistance to at least two molecules, and worrying resistance levels were observed. These findings prove that Algerian currency harbors opportunistic multiresistant bacteria and could potentially act as a vehicle for the spread of bacterial diseases and as a reservoir for antibiotic resistance genes among the community. Therefore, no cash payment systems should be developed and generalized to minimize cash handling and subsequent potential health risks.
Phosphorothioate substitutions in RNA structure studied by molecular dynamics simulations, QM/MM calculations and NMR experiments

Phosphorothioates (PTs) are important chemical modifications of the RNA backbone where a single non-bridging oxygen of the phosphate is replaced with a sulphur atom. PT can stabilize RNAs by protecting them from hydrolysis and is commonly used as tool to explore their function. It is, however, unclear what basic physical effects PT has on RNA stability and electronic structure. Here, we present Molecular Dynamics (MD) simulations, quantum mechanical (QM) calculations, and NMR spectroscopy measurements, exploring the effects of PT modifications in the structural context of the Neomycin-sensing riboswitch (NSR). The NSR is the smallest biologically functional riboswitch with a well-defined structure stabilized by a U-turn motif. Three of the signature interactions of the U-turn; an H-bond, an anion-π interaction and a potassium binding site; are formed by RNA phosphates, making the NSR an ideal model for studying how PT affects RNA dynamics, stability, and interaction with solvent. By comparing with high-level QM calculations, we reveal the distinct physical properties of the individual interactions facilitated by the PT. The sulphur force-field parameters commonly employed in the literature do not reflect these distinctions, leading to unsatisfactory description of PT in simulations of the NSR. We show that it is not possible to accurately describe the PT interactions using one universal set of van der Waals sulphur parameters and provide suggestions for improving the force-field performance.
Genome-Wide Analysis of the COBRA-Like Gene Family Supports Gene Expansion through Whole-Genome Duplication in Soybean (Glycine max)

The COBRA-like (COBL) gene family has been associated with the regulation of cell wall expansion and cellulose deposition. COBL mutants result in reduced levels and disorganized deposition of cellulose causing defects in the cell wall and inhibiting plant development. In this study, we report the identification of 24 COBL genes (GmCOBL) in the soybean genome. Phylogenetic analysis revealed that the COBL proteins are divided into two groups, which differ by about 170 amino acids in the N-terminal region. The GmCOBL genes were heterogeneously distributed in 14 of the 20 soybean chromosomes. This study showed that segmental duplication has contributed significantly to the expansion of the COBL family in soybean during all Glycine-specific whole-genome duplication events. The expression profile revealed that the expression of the paralogous genes is highly variable between organs and tissues of the plant. Only 20% of the paralogous gene pairs showed similar expression patterns. The high expression levels of some GmCOBLs suggest they are likely essential for regulating cell expansion during the whole soybean life cycle. Our comprehensive overview of the COBL gene family in soybean provides useful information for further understanding the evolution and diversification of COBL genes in soybean.
Extreme Exposure to Filtered Far‐UVC: A Case Study

Far‐UVC devices are being commercially sold as “safe for humans” for the inactivation of SARS‐CoV‐2, without supporting human safety data. We felt there was a need for rapid proof‐of‐concept human self‐exposure, to inform future controlled research and promote informed discussion. A Fitzpatrick Skin Type II individual exposed their inner forearms to large radiant exposures from a filtered Krypton‐Chloride (KrCl) far‐UVC system (SafeZoneUVC, Ushio Inc., Tokyo, Japan) with peak emission at 222 nm. No visible skin changes were observed at 1500 mJ cm−2; whereas, skin yellowing that appeared immediately and resolved within 24 h occurred with a 6000 mJ cm−2 exposure. No erythema was observed at any time point with exposures up to 18 000 mJ cm−2. These results combined with Monte Carlo Radiative Transfer computer modeling suggest that filtering longer ultraviolet wavelengths is critical for the human skin safety of far‐UVC devices. This work also contributes to growing arguments for the exploration of exposure limit expansion, which would subsequently enable faster inactivation of viruses.
An Atoh1 CRE Knock-In Mouse Labels Motor Neurons Involved in Fine Motor Control

Abstract Motor neurons (MNs) innervating the digit muscles of the intrinsic hand (IH) and intrinsic foot (IF) control fine motor movements. The ability to reproducibly label specifically IH and IF MNs in mice would be a beneficial tool for studies focused on fine motor control. To this end, we find that a CRE knock-in mouse line of Atoh1, a developmentally expressed basic helix-loop-helix (bHLH) transcription factor, reliably expresses CRE-dependent reporter genes in ∼60% of the IH and IF MNs. We determine that CRE-dependent expression in IH and IF MNs is ectopic because an Atoh1 mouse line driving FLPo recombinase does not label these MNs although other Atoh1-lineage neurons in the intermediate spinal cord are reliably identified. Furthermore, the CRE-dependent reporter expression is enriched in the IH and IF MN pools with much sparser labeling of other limb-innervating MN pools such as the tibialis anterior (TA), gastrocnemius (GS), quadricep (Q), and adductor (Ad). Lastly, we find that ectopic reporter expression begins postnatally and labels a mixture of α and γ-MNs. Altogether, the Atoh1 CRE knock-in mouse strain might be a useful tool to explore the function and connectivity of MNs involved in fine motor control when combined with other genetic or viral strategies that can restrict labeling specifically to the IH and IF MNs. Accordingly, we provide an example of sparse labeling of IH and IF MNs using an intersectional genetic approach.
Source decomposition of the frontocentral auditory steady‐state gamma band response in schizophrenia patients and healthy subjects

Gamma‐band auditory steady‐state response (ASSR) is a neurophysiologic index that is increasingly used as a translational biomarker in the development of treatments of neuropsychiatric disorders. While gamma‐band ASSR is generated by distributed networks of highly interactive temporal and frontal cortical sources, the majority of human gamma‐band ASSR studies using electroencephalography (EEG) highlight activity from only a single frontocentral scalp site, Fz, where responses tend to be largest and reductions in schizophrenia patients are most evident. However, no previous study has characterized the relative source contributions to Fz, which is a necessary step to improve the concordance of preclinical and clinical EEG studies.
Adaptations to explosive resistance training with partial range of motion are not inferior to full range of motion

We tested whether explosive resistance training with partial range of motion (ROM) would be as effective as full ROM training using a noninferiority trial design. Fifteen subjects with strength training experience took part in an explosive—concentric only—leg press training program, three times per week for 10 weeks. One leg was randomly assigned to exercise with partial ROM (ie, 9º) and the other leg to full ROM. Before and after training, we assessed leg press performance, isokinetic concentric and isometric knee extension torque, and vastus lateralis muscle architecture. Overall, both training modalities increased maximal strength and rate of force development. Training with partial ROM yielded noninferior results compared to full ROM for leg press peak power (+69 ± 47% vs. +61 ± 64%), isokinetic strength (4‐6 ± 6%‐12% vs. 1‐6 ± 6%‐10% at 30, 60, and 180˚s‐1), and explosive torque after 100 (47 ± 24 vs. 35 ± 22) and 150 ms (57 ± 22% vs. 42 ± 25%). The comparison was inconclusive for other functional parameters (ie, isokinetic peak torque (300˚s−1), joint angle at isokinetic peak torque, explosive torque after 50 ms, and electrically evoked torque) and for muscle fascicle length and thickness, although noninferiority was established for pennation angle. However, partial ROM was not found statistically inferior to full ROM for any measured variable. Under the present conditions, the effects of explosive heavy resistance training were independent of joint ROM. Instead, these data suggest that the distinct timing of muscle work in explosive contractions confers more influence to the starting joint angle than ROM on adaptations to this type of training.