Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials

We test the BUQEYE model of correlated effective field theory (EFT) truncation errors on Reinert, Krebs, and Epelbaum's semi-local momentum-space implementation of the chiral EFT ($\chi$EFT) expansion of the nucleon-nucleon (NN) potential. This Bayesian model hypothesizes that dimensionless coefficient functions extracted from the order-by-order corrections to NN observables can be treated as draws from a Gaussian process (GP). We combine a variety of graphical and statistical diagnostics to assess when predicted observables have a $\chi$EFT convergence pattern consistent with the hypothesized GP statistical model. Our conclusions are: First, the BUQEYE model is generally applicable to the potential investigated here, which enables statistically principled estimates of the impact of higher EFT orders on observables. Second, parameters defining the extracted coefficients such as the expansion parameter $Q$ must be well chosen for the coefficients to exhibit a regular convergence pattern -- a property we exploit to obtain posterior distributions for such quantities. Third, the assumption of GP stationarity across lab energy and scattering angle is not generally met; this necessitates adjustments in future work. We provide a workflow and interpretive guide for our analysis framework, and show what can be inferred about probability distributions for $Q$, the EFT breakdown scale $\Lambda_b$, the scale associated with soft physics in the $\chi$EFT potential $m_{\rm eff}$, and the GP hyperparameters. All our results can be reproduced using a publicly available Jupyter notebook, which can be straightforwardly modified to analyze other $\chi$EFT NN potentials.
Inferring Non-Failure Conditions for Declarative Programs

Unintended failures during a computation are painful but frequent during software development. Failures due to external reasons (e.g., missing files, no permissions) can be caught by exception handlers. Programming failures, such as calling a partially defined operation with unintended arguments, are often not caught due to the assumption that the software is correct. This paper presents an approach to verify such assumptions. For this purpose, non-failure conditions for operations are inferred and then checked in all uses of partially defined operations. In the positive case, the absence of such failures is ensured. In the negative case, the programmer could adapt the program to handle possibly failing situations and check the program again. Our method is fully automatic and can be applied to larger declarative programs. The results of an implementation for functional logic Curry programs are presented.
Situating Data Sets: Making Public Data Actionable for Housing Justice

Activists, governmentsm and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps. Our interpretation is grounded in D'Ignazio and Klein's Data Feminism, emphasizing standpoint theory. Through our analysis and discussion, we highlight how shifting positionalities from data intermediaries to data accomplices affects the design of data sets and maps. We provide HCI scholars with three design implications when situating data for grassroots organizers: becoming a domain beginner, striving for data actionability, and evaluating our design artifacts by the social relations they sustain rather than just their technical efficacy.
Causal and Stable Relativistic Hydrodynamic Fluctuations

When two nuclei collide close to the speed of light, a fluid state known as the quark-gluon plasma is formed. Attempts to understand the dynamics of this fluid have generated significant research into dissipative relativistic fluid dynamics. The fluctuation-dissipation theorem implies that any dissipative dynamical system will also experience thermal fluctuations; however, such fluctuations are not typically included in the modeling of the quark-gluon plasma. This work discusses a new method of determining whether a hydrodynamic framework is consistent with thermal fluctuations. We develop a new method for calculating the noise correlator of relativistic hydrodynamic systems and apply it to Israel-Stewart theory in a general hydrodynamic frame.
Highly efficient in vivo mutagenesis method based on the endonuclease and exonuclease activity of Bacillus alcalophilus RecJ (BaRecJ)

With the continual advancement of technologies such as microbial cultivation, DNA sequencing, bioinformatics, and genetic engineering, methods for microbial breeding have become increasingly diverse. We identified a RecJ enzyme (BaRecJ) with both endonuclease and exonuclease activities from B. alcalophilus. Differing from traditional physical-chemical mutagenesis approaches and the methods based on perturbation factor. this research established a novel mutagenesis method utilizing the endonuclease and exonuclease activities of BaRecJ. Mutagenesis of E. coli was conducted using the BaRecJ method, followed by screening for rifampicin-resistant mutants, rpoB sequencing results demonstrated a broader, more uniform spectrum of mutations and a higher frequency of substitution mutations with this mutagenesis approach. Furthermore, this mutagenesis method was applied to S cerevisiae, resulting in mutants with enhanced tolerance to acetic acid and ethanol, exhibiting improved fermentation performance and flocculation abilities Genomic resequencing analysis summarized genes possibly associated with the tolerance of mutants. Therefore, this approach not only holds immense potential in microbial mutagenesis breeding and adaptive evolution but also, when coupled with genomic resequencing, allows for the rapid identification of genetic loci associated with specific traits.
Lytic transglycosylase repertoire diversity enables daughter cell separation and antibiotic resistance in Escherichia coli under acidic stress

Peptidoglycan (PG) is an indispensable architectural element that imparts physical toughness and rigidity to the bacterial envelope. It is also a dynamic structure that undergoes continuous turnover or autolysis. Escherichia coli possesses redundant autolytic enzymes responsible for PG turnover; however, the rationale behind the existence of numerous autolytic enzymes remains incompletely understood. In this study, we elucidated the physiological roles of MltE and MltC, members of the lytic transglycosylase (LTG) family that catalyze the cleavage of glycosidic bonds between disaccharide subunits within PG strands. MltE and MltC are acidic LTGs that exhibit increased enzymatic activity and protein levels under acidic pH conditions, respectively. Deletion of these two LTGs results in a pronounced growth defect and elevated membrane permeability at acidic pH. Furthermore, these two LTGs are crucial for resistance against various antibiotics, particularly vancomycin. Intriguingly, inactivation of these LTGs induces a chaining morphology, indicative of daughter cell separation defects, only under acidic pH conditions. Simultaneous deletion of PG amidases, the known contributors to daughter cell separation, exacerbates the chaining phenotype at acidic pH. This suggests that the two LTGs may participate in the cleavage of glycan strands between daughter cells that cannot be resolved by PG amidases under acidic pH conditions. Collectively, our findings highlight the role of LTG repertoire diversity in facilitating bacterial survival and antibiotic resistance under stressful conditions. IMPORTANCE Orchestration of peptidoglycan (PG) synthesis and degradation plays a critical role in bacterial growth and development of antibiotic resistance. Bacteria use a diverse array of enzymes involved in PG synthesis and degradation to navigate the challenging conditions of the periplasm. Although Escherichia coli harbors 12 lytic transglycosylases (LTGs) responsible for cleaving glycosidic bonds between disaccharide subunits within PG strands, the physiological significance of their redundancy remains unclear. In this study, we demonstrated the indispensability of two LTGs, MltE and MltC, for cell growth, daughter cell separation, and antibiotic resistance under acidic stress conditions. Our findings highlight the potential significance of MltE and MltC as primary targets for the development of potentiators to augment the antimicrobial efficacy of antibiotics in acidic pH environments. One sentence summary Acid stress-specific lytic transglycosylases
mgPGPT: Metagenomic analysis of plant growth-promoting traits

In a recent publication, we introduce the PGPT ontology and PGPT-db of bacterial plant growth-promotion traits and associated database of protein sequences, and provide several tools for bacterial genome analysis on the PLaBAse server. Here, we extend the scope of the PGPT ontology to perform PGPT analysis of metagenomic datasets. First, we introduce mgPGPT-db, an extended database of 39, 582, 183 protein sequences obtained computationally by including proteins from AnnoTree. With this, we have integrated the PGPT ontology into our metagenome analysis tool MEGAN and provide mapping files to identify PGPT-related genes using the results of a DIAMOND alignment of reads against either the new mgPGPT-db database, the NCBI-nr protein database, or the AnnoTree protein database. We demonstrate and compare these different approaches in detail on an example data set and evince the improvement compared to the PGPT-db. We also compare the inferred PGPT content of several samples taken from different environments and reveal plant specific PGPT clustering. IMPORTANCE A deeper understanding of plant growth-promoting traits of bacteria is important to enlight and enhance the native plant-beneficial bacterial functional diversity regarding the environmental stress adaption or the ability to suppress even food-borne pathogens by strain inoculation dedicated to agriculture and other plant production systems. The work presented here extends recent work beyond the analysis of individual to allow the assessment of the PGPT potential of metagenomes obtained from environmental samples.
Mutations compensating for cell polarity defects change the gene-fitness relationship on a genome-wide scale

Functional defects resulting from deleterious mutations can often be restored during evolution by compensatory mutations elsewhere in the genome. Importantly, this process can generate the molecular diversity seen in networks regulating the same biological function. How the capacity for compensatory evolution is embedded in the structure of biological networks is still unresolved. Here, we study a case in Saccharomyces cerevisiae where a genetic perturbation in a module related to polarity establishment is compensated by making additional gene deletions. A transposon mutagenesis screen revealed that compensatory evolution altered the gene-fitness relation of approximately 13% of the annotated genes. Interestingly, a nearly equal number of genes become more and less tolerant to disruptions, indicating substantial functional rewiring of genes. Grouping the affected genes by their biological function, we find that they are involved in a diverse set of processes, of which many have no clear link to cell polarity. Our results show that mutations compensating for defects in one functional module can have global consequences that influence the evolutionary direction of many other cellular processes.
Structural basis of Ebola virus nucleocapsid assembly and functions

The Ebola virus, a member of the Filoviridae family, causes severe hemorrhagic fever in humans. Filamentous virions contain a helical nucleocapsid responsible for genome transcription, replication, and packaging into progeny virions. The nucleocapsid consists of a helical nucleoprotein (NP)–viral genomic RNA complex forming the core structure, to which VP24 and VP35 bind externally. Two NPs, each paired with a VP24 molecule, constitute a repeating unit. However, the detailed nucleocapsid structure remains unclear. Here, we determined the nucleocapsid-like structure within virus-like particles at 4.6 Å resolution using single-particle cryo-electron microscopy. Mutational analysis identified specific interactions between the two NPs and two VP24s and demonstrated that each of the two VP24s in different orientations distinctively regulates nucleocapsid assembly, viral RNA synthesis, and intracellular transport of the nucleocapsid. Our findings highlight the sophisticated mechanisms underlying the assembly and functional regulation of the nucleocapsid and provide insights into antiviral development.
Emotional Content and Semantic Structure of Dialogues Predict Interpersonal Neural Synchrony in the Prefrontal Cortex: a Hyperscanning Study with Functional Near-Infrared Spectroscopy

A fundamental characteristic of social exchanges is the synchronization of indi-viduals’ behaviors, physiological responses, and neural activity. However, the influence of how individuals communicate in terms of emotional content and expressed associative knowledge on interpersonal synchrony has been scarcely investigated so far. This study addresses this research gap by bridging recent advances in cognitive neuroscience data, affective computing, and cognitive data science frameworks. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, prefrontal neural data were collected during social interactions involving 84 participants (i.e., 42 dyads) aged 18-35 years. Wavelet transform coherence was used to assess interpersonal neural synchrony between partici-pants. We used manual transcription of dialogues and automated methods to codify transcriptions as emotional levels and syntactic/semantic networks. Our quantitative findings reveal higher than random expectations levels of interper-sonal neural synchrony in the superior frontal gyrus (p = 0.020) and the bilateral middle frontal gyri (p < 0.001; p = 0.002). Stepwise models based on dialogues’ emotional content only significantly predicted interpersonal neural synchrony in the medial ( = 14.13%) and left prefrontal cortex ( = 8.25%). Con-versely, models relying on semantic features were more effective for the right prefrontal cortex ( = 18.30%). Generally, emotional content emerged as the most accurate predictor of synchrony. However, we found an interplay between emotions and associative knowledge during role reversal (i.e., a clinical technique involving perspective-taking) providing quantitative support to empathy in social interactions emphasizing both affective and cognitive computations. Our study identifies a mind-brain duality in emotions and associative knowledge reflecting neural synchrony levels, opening new ways for investigating human interactions.
A Feature Selection Method for Tissue-Specific Alternative Polyadenylation Sites Data in Rice

The identification of tissue-specific alternative polyadenylation (tsAPA) sites contributes to the research on gene expression regulation and transcriptome diversity in rice. However, identifying the tsAPA sites in plants is difficult, because of the dispersion, variability, complexity of their features and the lack of related research. A hybrid feature selection algorithm called SRBT, based on the SVM-RFE and Boruta, was presented to identify the tsAPA sites in rice. In the experiment, the tsAPA sites data were adopted to reduce dimension with SRBT algorithm and then classified by the support vector machine (SVM). The results show that the proposed method can effectively extract important features and obtain a higher average prediction accuracy of 81%, compared with SVM-RFE, Boruta, GAFS, T-test and ReliefF. The SRBT works well in identifying the tsAPA sites, which offers an effective method for further analysis of the tsAPA in gene expression and transcription during the growth of rice.
TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning

: Multi-agent reinforcement learning (MARL) has emerged as a popular technique in diverse domains due to its ability to automate system controller de-sign and facilitate continuous intelligence learning. For instance, traffic flow is often trained with MARL to enable intelligent simulations for autonomous driving. However, The existing MARL algorithm only characterizes the relative degree of each agent’s contribution to the team, and cannot express the contribution that the team needs from the agent. Especially in the field of autonomous driving, the team changes over time, and the agent needs to act directly according to the needs of the team. To address these limitations, we propose an innovative method inspired by realistic traffic coordinators called the Traffic Coordinator Network (TraCo). Our approach leverages a combination of cross-attention and counterfactual advantage function, allowing us to extract distinctive characteristics of domain agents and accurately quantify the contribution that a team needs from an agent. Through experiments conducted on four traffic tasks, we demonstrate that our method out-performs existing approaches, yielding superior performance. Furthermore, our approach enables the emergence of rich and diverse social behaviors among vehicles within the traffic flow.
Loop closure with a low power millimeter wave radar sensor using an autoencoder

In this paper, we will consider place recognition, more commonly know as loop closure, with a low resolution single-chip millimeter wave (mmWave) radar in indoor environments. It is an essential part in simultaneous localization and mapping (SLAM) systems to avoid drift. By using a novel method to create descriptors or latent codes with an autoen-coder in combination with exploiting the temporal similarity between our latent codes, we are able to successfully extract loop closures with a radar-only system without requiring ground truth. Our proposed method is validated in an industrial IoT lab on an Unmanned Aerial Vehicle (UAV) and on a cargo bike in a parking building.
Class-Balanced and Reinforced Active Learning on Graphs

Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost. However, most existing algorithms for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. GNNs trained with class-imbalanced labeled data are susceptible to bias toward majority classes, and the lower performance of minority classes may lead to a decline in overall performance. To tackle this issue, we propose a novel class-balanced and reinforced active learning framework for GNNs, namely, GCBR. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GCBR designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. The reinforcement learning algorithm Advantage Actor-Critic (A2C) is employed to learn an optimal policy stably and efficiently. We further upgrade GCBR to GCBR++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines.
An Inductive Bias for Emergent Communication in a Continuous Setting

We study emergent communication in a multi-agent reinforcement learning setting, where the agents solve cooperative tasks and have access to a communication channel. The communication channel may consist of either discrete symbols or continuous variables. We introduce an inductive bias to aid with the emergence of good communication protocols for continuous messages, and we look at the effect this type of inductive bias has for continuous and discrete messages in itself or when used in combination with reinforcement learning. We demonstrate that this type of inductive bias has a beneficial effect on the communication protocols learnt in two toy environments, Negotiation and Sequence Guess.
SCALE: Causal Learning and Discovery of Robot Manipulation Skills using Simulation

: We propose SCALE, an approach for discovering and learning a diverse set of interpretable robot skills from a limited dataset. Rather than learning a single skill which may fail to capture all the modes in the data, we first identify the different modes via causal reasoning and learn a separate skill for each of them. Our main insight is to associate each mode with a unique set of causally relevant context variables that are discovered by performing causal interventions in simulation. This enables data partitioning based on the causal processes that generated the data, and then compressed skills that ignore the irrelevant variables can be trained. We model each robot skill as a Regional Compressed Option, which extends the options framework by associating a causal process and its relevant variables with the option. Modeled as the skill Data Generating Region, each causal process is local in nature and hence valid over only a subset of the context space. We demonstrate our approach for two representative manipulation tasks: block stacking and peg-in-hole insertion under uncertainty. Our experiments show that our approach yields diverse skills that are compact, robust to domain shifts, and suitable for sim-to-real transfer.
A Minimalist Approach for Domain Adaptation with Optimal Transport

We reveal an intriguing connection between adversarial attacks and cycle monotone maps, also known as optimal transport maps. Based on this finding, we developed a novel method named source fiction for semi-supervised optimal transport-based domain adaptation. We conduct experiments on various datasets and show that our method can notably improve the performance of the optimal transport solvers in domain adaptation
A Deep Learning Bootcamp for Engineering & Management Students

Students from engineering & management with a focus on vocational activities such as machining or accounting tend to lack the necessary computer science foundations to build, appreciate, and evaluate machine learning solutions. However, they are likely going to have to identify and judge potential use cases in their careers in industrial practice. Therefore, we propose a guided three-day curriculum that goes all the way from manual data inspection to the implementation of several models in Python, including several evaluation metrics. We focus on a computer vision task identifying traffic signs due to its conceptual simplicity and similarity to tasks in vision-based quality assurance. We share our material as OER as well as experiences from four offerings of the bootcamp.