Miracle-dz/UniFAR-FTUs huggingface dataset:
Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception

When moving a piano or dancing tango with a partner, how should I control my arm muscles to sense their movements and follow or guide them smoothly? Here we observe how physically connected pairs tracking a moving target with the arm modify muscle coactivation with their visual acuity and the partner's performance. They coactivate muscles to stiffen the arm when the partner's performance is worse, and relax with blurry visual feedback. Computational modelling shows that this adaptive sensing property cannot be explained by the minimization of movement error hypothesis that has previously explained adaptation in dynamic environments. Instead, individuals skillfully control the stiffness to guide the arm towards the planned motion while minimizing effort and extracting useful information from the partner's movement. The central nervous system regulates muscles' activation to guide motion with accurate task information from vision and haptics while minimizing the metabolic cost. As a consequence, the partner with the most accurate target information leads the movement.
Towards Generating Adversarial Examples on Mixed-type Data

The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as financial forecasting, fraudulent detection, and anomaly detection, the data samples are usually mixed-type, which contain plenty of numerical and categorical features at the same time. However, how to generate adversarial examples with mixed-type data is still seldom studied. In this paper, we propose a novel attack algorithm M-Attack, which can effectively generate adversarial examples in mixed-type data. Based on M-Attack, attackers can attempt to mislead the targeted classification model's prediction, by only slightly perturbing both the numerical and categorical features in the given data samples. More importantly, by adding designed regularizations, our generated adversarial examples can evade potential detection models, which makes the attack indeed insidious. Through extensive empirical studies, we validate the effectiveness and efficiency of our attack method and evaluate the robustness of existing classification models against our proposed attack. The experimental results highlight the feasibility of generating adversarial examples toward machine learning models in real-world applications.
Post-hoc analysis of Arabic transformer models

Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects have surfaced. While there have been an extrinsic evaluation of these models with respect to downstream NLP tasks, no work has been carried out to analyze and compare their internal representations. We probe how linguistic information is encoded in the transformer models, trained on different Arabic dialects. We perform a layer and neuron analysis on the models using morphological tagging tasks for different dialects of Arabic and a dialectal identification task. Our analysis enlightens interesting findings such as: i) word morphology is learned at the lower and middle layers, ii) while syntactic dependencies are predominantly captured at the higher layers, iii) despite a large overlap in their vocabulary, the MSA-based models fail to capture the nuances of Arabic dialects, iv) we found that neurons in embedding layers are polysemous in nature, while the neurons in middle layers are exclusive to specific properties.
A human-ML collaboration framework for improving video content reviews

We deal with the problem of localized in-video taxonomic human annotation in the video content moderation domain, where the goal is to identify video segments that violate granular policies, e.g., community guidelines on an online video platform. High quality human labeling is critical for enforcement in content moderation. This is challenging due to the problem of information overload - raters need to apply a large taxonomy of granular policy violations with ambiguous definitions, within a limited review duration to relatively long videos. Our key contribution is a novel human-machine learning (ML) collaboration framework aimed at maximizing the quality and efficiency of human decisions in this setting - human labels are used to train segment-level models, the predictions of which are displayed as"hints"to human raters, indicating probable regions of the video with specific policy violations. The human verified/corrected segment labels can help refine the model further, hence creating a human-ML positive feedback loop. Experiments show improved human video moderation decision quality, and efficiency through more granular annotations submitted within a similar review duration, which enable a 5-8% AUC improvement in the hint generation models.
Proximity-induced equilibrium supercurrent and perfect superconducting diode effect due to band asymmetry

We theoretically investigate the consequences of proximity-induced conventional superconductivity in metals that break time-reversal and inversion symmetries through their energy dispersion. We discover behaviors impossible in an isolated superconductor such as an equilibrium supercurrent that apparently violates a no-go theorem and, at suitable topological defects, non-conservation of electric charge reminiscent of the chiral anomaly. The equilibrium supercurrent is expected to be trainable by a helical electromagnetic field in the normal state. Remarkably, if the band asymmetry exceeds the critical current of the parent superconductor in appropriate units, we predict a perfect superconducting diode effect with diode coefficient unity. We propose toroidal metals such as UNi$_{4}$B and metals with directional scalar spin chiral order as potential platforms.
Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross Disciplinary Technologies

The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals. This article aims to establish a carbon-oriented framework to examine the role carbon emission is playing within a power grid that is rapidly transitioning to an integrated multi-energy system. We divide the carbon flows in the multi-energy systems into three stages: carbon allowances initialization/allocation, exchanging/pricing, and circulation. Then, we discuss how cross-disciplinary technologies, such as game theory, optimization, and machine learning can facilitate the modeling and analysis of the proposed framework.
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations

Although reinforcement learning has found widespread use in dense reward settings, training autonomous agents with sparse rewards remains challenging. To address this difficulty, prior work has shown promising results when using not only task-specific demonstrations but also task-agnostic albeit somewhat related demonstrations. In most cases, the available demonstrations are distilled into an implicit prior, commonly represented via a single deep net. Explicit priors in the form of a database that can be queried have also been shown to lead to encouraging results. To better benefit from available demonstrations, we develop a method to Combine Explicit and Implicit Priors (CEIP). CEIP exploits multiple implicit priors in the form of normalizing flows in parallel to form a single complex prior. Moreover, CEIP uses an effective explicit retrieval and push-forward mechanism to condition the implicit priors. In three challenging environments, we find the proposed CEIP method to improve upon sophisticated state-of-the-art techniques.
Watch Your Back: Identifying Cybercrime Financial Relationships in Bitcoin through Back-and-Forth Exploration

Cybercriminals often leverage Bitcoin for their illicit activities. In this work, we propose back-and-forth exploration, a novel automated Bitcoin transaction tracing technique to identify cybercrime financial relationships. Given seed addresses belonging to a cybercrime campaign, it outputs a transaction graph, and identifies paths corresponding to relationships between the campaign under study and external services and other cybercrime campaigns. Back-and-forth exploration provides two key contributions. First, it explores both forward and backwards, instead of only forward as done by prior work, enabling the discovery of relationships that cannot be found by only exploring forward (e.g., deposits from clients of a mixer). Second, it prevents graph explosion by combining a tagging database with a machine learning classifier for identifying addresses belonging to exchanges. We evaluate back-and-forth exploration on 30 malware families. We build oracles for 4 families using Bitcoin for C&C and use them to demonstrate that back-and-forth exploration identifies 13 C&C signaling addresses missed by prior work, 8 of which are fundamentally missed by forward-only explorations. Our approach uncovers a wealth of services used by the malware including 44 exchanges, 11 gambling sites, 5 payment service providers, 4 underground markets, 4 mining pools, and 2 mixers. In 4 families, the relations include new attribution points missed by forward-only explorations. It also identifies relationships between the malware families and other cybercrime campaigns, highlighting how some malware operators participate in a variety of cybercriminal activities.
Orthogonal Policy Learning Under Ambiguity

This paper studies the problem of estimating individualized treatment rules when treatment eﬀects are partially identiﬁed, as it is often the case with observational data. By drawing connections between the treatment assignment problem and classical decision theory, we characterize several notions of optimal treatment policies in the presence of partial identiﬁcation. The proposed framework allows to incorporate user-deﬁned constraints on the policies, such as restrictions for transparency or interpretability, while also ensuring computational feasibility. We show that partial identiﬁcation leads to a novel statistical learning problem with risk directionally – but not fully – diﬀerentiable with respect to an inﬁnite-dimensional nuisance component. We propose an estimation procedure that ensures Neyman-orthogonality with respect to the nuisance component and provide statistical guarantees that depend on the amount of concentration around the points of non-diﬀerentiability in the data-generating process. The proposed method is illustrated using data from the Job Partnership Training Act study.
Transport coefficients and hard probes of strongly coupled $\mathcal{N}=4$ super Yang-Mills plasma on the Coulomb branch

We study $\mathcal{N} = 4$ super Yang-Mills theory on the Coulomb branch (cSYM) by using its Type IIB supergravity dual. We compute the transport coefficients, and hard probes of $\mathcal{N} = 4$ cSYM at finite temperature $T$. We use the rotating black 3-brane solution of Type IIB supergravity with a single non-zero rotation parameter $r_{0}$ after analytically continuing $r_{0}\rightarrow -ir_{0}$, and in a new ensemble where $T$ and $r_{0}$ are held fixed. We find that the bulk viscosity to entropy density ratio of the large black hole branch decreases with temperature and has a maxima around the critical temperature $T_{c}$ while, for the small black hole branch, it increases with temperature. The other transport coefficients and parameters of hard probes, such as the conductivity, jet quenching parameter, drag force, and momentum diffusion coefficients of the large black hole branch increase with temperature and asymptote to their conformal value while, for the small black hole branch, they decrease with temperature which can be taken as a signature of a thermal gauge theory ($\mathcal{N }= 4$ cSYM) in its hadronizing phase where the color degrees of freedom are decreasing due to thermal hadron emission or Hawking radiation. We also identify the a-function of $\mathcal{N }= 4$ cSYM in its hadronizing phase which measures its number of degrees of freedom at an energy scale $T$ and decreases monotonically with renormalization group flow.
Domain Decomposition Methods for Elastic Materials with Compressible and Almost Incompressible Components

Domain decomposition methods are iterative methods to solve large systems of equations, obtained, e.g., from finite element discretization. Here, the domain is decomposed into smaller subproblems, which can be solved in parallel. In the first part of this work, new condition number bounds are proven for a FETI-DP type (Finite Element Tearing and Interconnecting Dual-Primal) domain decomposition method for compressible linear elasticity in 3D. Each subdomain may contain an inclusion having different material properties. The condition number bound only depends on the subdomain diameter, the finite element diameter, and the thickness of the compressible hull. It is independent of the material parameters in the inclusions, thus almost incompressible inclusions are also possible. In the second part of this thesis a new coarse space for FETI-DP methods for almost incompressible linear elasticity on the whole domain is presented. This coarse space is much smaller than the standard coarse space for FETI-DP or BDDC methods for almost incompressible linear elasticity.
Patterns of Population Structure and Introgression Among Recently Differentiated Drosophila melanogaster Populations

Abstract Despite a century of genetic analysis, the evolutionary processes that have generated the patterns of exceptional genetic and phenotypic variation in the model organism Drosophila melanogaster remains poorly understood. In particular, how genetic variation is partitioned within its putative ancestral range in Southern Africa remains unresolved. Here, we study patterns of population genetic structure, admixture, and the spatial structuring of candidate incompatibility alleles across a global sample, including 223 new accessions, predominantly from remote regions in Southern Africa. We identify nine major ancestries, six that primarily occur in Africa and one that has not been previously described. We find evidence for both contemporary and historical admixture between ancestries, with admixture rates varying both within and between continents. For example, while previous work has highlighted an admixture zone between broadly defined African and European ancestries in the Caribbean and southeastern USA, we identify West African ancestry as the most likely African contributor. Moreover, loci showing the strongest signal of introgression between West Africa and the Caribbean/southeastern USA include several genes relating to neurological development and male courtship behavior, in line with previous work showing shared mating behaviors between these regions. Finally, while we hypothesized that potential incompatibility loci may contribute to population genetic structure across the range of D. melanogaster; these loci are, on average, not highly differentiated between ancestries. This work contributes to our understanding of the evolutionary history of a key model system, and provides insight into the partitioning of diversity across its range.
Progression of the pluripotent epiblast depends upon the NMD factor UPF2

ABSTRACT Nonsense-mediated RNA decay (NMD) is a highly conserved RNA turnover pathway that degrades RNAs harboring in-frame stop codons in specific contexts. Loss of NMD factors leads to embryonic lethality in organisms spanning the phylogenetic scale, but the mechanism remains unknown. Here, we report that the core NMD factor, UPF2, is required for expansion of epiblast cells within the inner cell mass of mice in vivo. We identify NMD target mRNAs in mouse blastocysts – both canonical and alternatively processed mRNAs – including those encoding cell cycle arrest and apoptosis factors, raising the possibility that NMD is essential for embryonic cell proliferation and survival. In support, the inner cell mass of Upf2-null blastocysts rapidly regresses with outgrowth and is incompetent for embryonic stem cell derivation in vitro. In addition, we uncovered concordant temporal- and lineage-specific regulation of NMD factors and mRNA targets, indicative of a shift in NMD magnitude during peri-implantation development. Together, our results reveal developmental and molecular functions of the NMD pathway in the early embryo.
The applicability of the Second Revision of the International Staging System for patients with multiple myeloma receiving immunomodulatory drugs or proteasome inhibitor‐based regimens as induction treatment: A real‐world analysis

The Second Revision of the International Staging System (R2‐ISS) was recently introduced to improve risk stratification over that provided by the extensively applied standard revised International Staging System (R‐ISS). In addition to the variables included in the R‐ISS, the R2‐ISS incorporates chromosome 1q gain/amplification and divides the patients into 4 groups with different survival outcomes, better stratifying patients within the R‐ISS intermediate‐risk. The new model was developed based on a great quantity of data from patients participating in uniform clinical trials and has not been validated in real‐world clinical practice. Therefore, we retrospectively analyzed the prognostic value of the R2‐ISS in 474 consecutive patients with multiple myeloma receiving immunomodulatory drugs or proteasome inhibitor‐based regimens as their first‐line treatment. According to the R2‐ISS, 41 (8.6%), 76 (16%), 275 (58%), and 82 (17.3%) patients were identified as R2‐ISS I, R2‐ISS II, R2‐ISS III, and R2‐ISS IV, respectively. The median progression‐free survival (PFS) was 48 (95% CI: 38–58), 35 (95% CI: 23–47), 24 (95% CI: 21–27), and 12 (95% CI: 7–17) months, and the estimated median overall survival (OS) was 110 (95% CI: 42–178), 88 (95% CI: 75–101), 50 (95% CI: 43–57), and 26 (95% CI: 19–33) months (p < 0.001) in the 4 groups, respectively. The R2‐ISS could also classify groups with distinct survival among patients with renal impairment or classified as R‐ISS II. Adjusted by age, sex, treatment approaches and transplantation status, the R2‐ISS was an independent prognostic factor associated with OS with a hazard ratio of 7.055 (95% CI: 3.626–13.726) (p < 0.001) for R2‐ISS IV versus R2‐ISS I and 2.707 (95% CI: 1.436–5.103) (p = 0.002) for R2‐ISS III versus R2‐ISS I. In conclusion, our results suggest that the R2‐ISS is a simple and robust risk stratification tool for patients with multiple myeloma treated with novel drugs and could be used in everyday clinical practice.
Modern yeast development: finding the balance between tradition and innovation in contemporary winemaking

Abstract A key driver of quality in wines is the microbial population that undertakes fermentation of grape must. Winemakers can utilise both indigenous and purposefully inoculated yeasts to undertake alcoholic fermentation, imparting wines with aromas, flavours and palate structure and in many cases contributing to complexity and uniqueness. Importantly, having a toolbox of microbes helps winemakers make best use of the grapes they are presented with, and tackle fermentation difficulties with flexibility and efficiency. Each year the number of strains available commercially expands and more recently, includes strains of non-Saccharomyces, strains that have been improved using both classical and modern yeast technology and mixed cultures. Here we review what is available commercially, and what may be in the future, by exploring recent advances in fermentation relevant strain improvement technologies. We also report on the current use of microbes in the Australian wine industry, as reported by winemakers, as well as regulations around, and sentiment about the potential use of genetically modified organisms in the future.
Prevalence, antimicrobial resistance, virulence gene profile and molecular typing of Campylobacter species isolated from poultry meat samples

Abstract Background Campylobacter jejuni and Campylobacter coli are the most significant Campylobacter species responsible for severe gastrointestinal disorders. Raw poultry meat is considered a source of Campylobacter transmission to the human population. Objectives The present study was aimed to assess the prevalence rate, antibiotic resistance properties, virulence characters and molecular typing of C. jejuni and C. coli strains isolated from raw poultry meat samples. Methods Three hundred and eighty raw poultry meat samples were collected and analysed for the presence of Campylobacter spp. using the microbial culture. Species identification was done using the Polymerase Chain Reaction. Disk diffusion was developed to assess the antimicrobial resistance pattern of isolates. The distribution of virulence and antimicrobial resistance genes was determined by PCR. Enterobacterial Repetitive Intergenic Consensus‐PCR was used for molecular typing. Results Campylobacter species were isolated from 6.25% of examined samples. C. jejuni and C. coli contamination rates were found to be 57.44% and 48.14%, respectively. C. jejuni strains harboured the highest resistance rate against serythromycin (42.59%), ampicillin (38.88%), ciprofloxacin (33.33%), chloramphenicol (31.48%) and tetracycline (31.48%). C. coli isolates harboured the highest resistance rate against ampicillin (73.07%), ciprofloxacin (73.07%), erythromycin (65.38%) and chloramphenicol (50%). AadE1 (44.44%), blaOXA‐61 (42.59%) and tet(O) (35.18%) were the most commonly detected resistance genes in C. jejuni and cmeB (34.61%) and blaOXA‐61 (34.61%) were the most commonly detected among C. coli strains. The most frequent virulence factors among the C. jejuni isolates were flaA (100%), ciaB (100%), racR (83.33%), dnaJ (81.48%), cdtB (81.48%), cdtC (79.62%) and cadF (74.07%). The most frequent virulence factors among the C. coli isolates were flaA (100%), ciaB (100%), pldA (65.38%) and cadF (61.53%). Conclusions The majority of C. jejuni and C. coli strains had more than 80% similarities in their ERIC‐PCR pattern, which may show their common source of transmission. The role of goose and quebec meat samples as reservoirs of virulent and antimicrobial resistant Campylobacter spp. was determined.
The Single-Cell Revelation of Thermogenic Adipose Tissue

The past two decades have witnessed an upsurge in the appreciation of adipose tissue (AT) as an immuno-metabolic hub harbouring heterogeneous cell populations that collectively fine-tune systemic metabolic homeostasis. Technological advancements, especially single-cell transcriptomics, have offered an unprecedented opportunity for dissecting the sophisticated cellular networks and compositional dynamics underpinning AT remodelling. The “re-discovery” of functional brown adipose tissue dissipating heat energy in human adults has aroused tremendous interest in exploiting the mechanisms underpinning the engagement of AT thermogenesis for combating human obesity. In this review, we aim to summarise and evaluate the use of single-cell transcriptomics that contribute to a better appreciation of the cellular plasticity and intercellular crosstalk in thermogenic AT.