Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling drug-induced liver injury: current status and future prospects

ABSTRACT Introduction Idiosyncratic drug-induced liver injury (iDILI) is a challenging and unpredictable multifactorial condition. At present, validated preclinical models for the prediction of the hepatotoxic potential of a given drug are scarce. Areas covered This review intends to sum up the current knowledge about in vitro (including hepatocyte 2D cultures, cocultures with non-parenchymal cells, 3D configurations and non-typical closer to reality in vitro models), in vivo (covering models for immunological and oxidative stress features, humanized mouse-based and non-rodent models) and in silico approaches for iDILI modeling, highlighting the recent advances in each topic. Expert opinion The future strategy for iDILI modeling should be patient-centered. Future animal and cell-based models, with more predictive value, will be easier to design by using a more translational approach based on mechanisms demonstrated in humans. Genetic and epigenetic information gathered from iDILI patients, together with data from in vitro and in vivo studies, could be used to develop sophisticated predictive in silico models to find compounds with iDILI potential. Collecting genetic, metabolic, and biomarker data from patient cohorts might be another option to create a ‘fingerprint’ characteristic of people at risk, allowing for the development of new, mechanistic strategies to enhance iDILI in vitro evaluation.
Family-centred service in paediatric acquired brain injury rehabilitation: perspectives of children and adolescents and their families

Abstract Purpose Family-centred service is considered best practice in paediatric acquired brain injury (ABI) rehabilitation. However, there is no universal model to guide clinicians and services in implementing this approach with a lack of research evidence informed by the lived experience of families. This study explored experiences of family-centred service during rehabilitation from the perspectives of parents/caregivers, siblings, and children/adolescents with ABI. Materials and methods Semi-structured interviews were conducted with participants from eight families, and included 10 parents/caregivers, five siblings, and four children/adolescents with moderate to severe ABI. Interview transcripts and additional data were analysed using constructivist grounded theory methods. Results Two themes and five sub-themes were developed: (1) Working together as a team: Valuing clinicians’ expert knowledge; Doing rehabilitation together; and Sharing family knowledge; and (2) Navigating rehabilitation as a family: Recognising family needs; Juggling family life; and Making rehabilitation work for the family. Participants reflected on the centrality of the child/adolescent with ABI during rehabilitation, and the ways that family needs, life, and involvement in rehabilitation change over time. Conclusions These findings highlight the importance of clinicians actively seeking to understand families’ unique contexts, needs, and priorities during rehabilitation through two-way information sharing with families. Through developing a greater understanding of families’ unique contexts, clinicians can be better placed to tailor rehabilitation according to their unique circumstances and needs. Implications for rehabilitation Rehabilitation involves active collaboration between families and clinicians that is responsive to changing needs over time. Family involvement in rehabilitation is shaped by family life, and families have unique needs following paediatric acquired brain injury. It is important that clinicians strive to develop an understanding of families’ unique needs, values, preferences, and lives to determine how to best support and involve them in rehabilitation.
A Unified Bregman Alternating Minimization Algorithm for Generalized DC Programming with Application to Imaging Data

In this paper, we consider a class of nonconvex (not necessarily differentiable) optimization problems called generalized DC (Difference-of-Convex functions) programming, which is minimizing the sum of two separable DC parts and one two-block-variable coupling function. To circumvent the nonconvexity and nonseparability of the problem under consideration, we accordingly introduce a Unified Bregman Alternating Minimization Algorithm (UBAMA) by maximally exploiting the favorable DC structure of the objective. Specifically, we first follow the spirit of alternating minimization to update each block variable in a sequential order, which can efficiently tackle the nonseparablitity caused by the coupling function. Then, we employ the Fenchel-Young inequality to approximate the second DC components (i.e., concave parts) so that each subproblem reduces to a convex optimization problem, thereby alleviating the computational burden of the nonconvex DC parts. Moreover, each subproblem absorbs a Bregman proximal regularization term, which is usually beneficial for inducing closed-form solutions of subproblems for many cases via choosing appropriate Bregman kernel functions. It is remarkable that our algorithm not only provides an algorithmic framework to understand the iterative schemes of some novel existing algorithms, but also enjoys implementable schemes with easier subproblems than some state-of-the-art first-order algorithms developed for generic nonconvex and nonsmooth optimization problems. Theoretically, we prove that the sequence generated by our algorithm globally converges to a critical point under the Kurdyka-{\L}ojasiewicz (K{\L}) condition. Besides, we estimate the local convergence rates of our algorithm when we further know the prior information of the K{\L} exponent.
The stochastic sandpile model on complete graphs

The stochastic sandpile model (SSM) is a generalisation of the standard Abelian sandpile model (ASM), in which topplings of unstable vertices are made random. When unstable, a vertex sends one grain to each of its neighbours independently with probability $p \in (0,1)$. We study the SSM on complete graphs. We provide a stochastic version of Dhar's burning algorithm to check if a given (stable) state is recurrent or not. We show that the recurrent states for the SSM are given by convex sums of recurrent states for the ASM. This allows us to recover a well-known result: that the number of integer lattice points in the $n$-dimensional permutation polytope is the number of labeled spanning forests on $n$ vertices. Finally, we study a family of so-called"partial"SSMs, in which some vertices topple randomly, while others topple deterministically (as in the ASM, sending one grain to all neighbours). We show that this distinction is meaningful, yielding sets of recurrent states that are in general different from those of both the ASM and SSM. We also show that to get all recurrent states of the SSM, we can allow up to two vertices to topple deterministically.
Rethinking Round-trip Translation for Automatic Machine Translation Evaluation

A parallel corpus is generally required to automatically evaluate the translation quality us-ing the metrics, such as BLEU, METEOR and BERTScore. While the reference-based evaluation paradigm is widely used in many machine translation tasks, it is difﬁcult to be applied to translation with low-resource languages, as those languages suffer from a de-ﬁciency of corpora. Round-trip translation provides an encouraging way to alleviate the urgent requirement of the parallel corpus, although it was unfortunately not observed to correlate with forwarding translation in the era of statistical machine translation. In this paper, we ﬁrstly observe that forward translation quality consistently correlates to corresponding round-trip translation quality in the scope of neural machine translation. Then, we care-fully analyse and unveil the reason for the contradictory results on statistical machine translation systems. Secondly, we propose a simple yet effective regression method to predict the performance of forward translation scores based on round-trip translation scores for various language pairs, including those between very low-resource languages. We conduct extensive experiments to show the effectiveness and robustness of the predictive models on 1,000+ language pairs. Finally, we test our method on challenging settings, such as predicting scores: i) for unseen language pairs in training and ii) on real-world WMT shared tasks but in new domains. The extensive experiments demonstrate the robustness and utility of our approach. We believe our work will inspire works on very low-resource multilingual machine translation.
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control

In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent rein-forcement learning remains challenging both in its theoretical analysis and empirical design of algorithms, especially for large swarms of embodied robotic agents where a definitive toolchain remains part of active research. We use emerging state-of-the-art mean-field control techniques in order to convert many-agent swarm control into more classical single-agent control of distributions. This allows profiting from advances in single-agent reinforcement learning at the cost of assuming weak interaction between agents. However, the mean-field model is violated by the nature of real systems with embodied, physically colliding agents. Thus, we combine collision avoidance and learning of mean-field control into a unified framework for tractably designing intelligent robotic swarm behavior. On the theoretical side, we provide novel approximation guarantees for general mean-field control both in continuous spaces and with collision avoidance. On the practical side, we show that our approach outperforms multi-agent reinforcement learning and allows for decentralized open-loop application while avoiding collisions, both in simulation and real UAV swarms. Overall, we propose a framework for the design of swarm behavior that is both mathematically well-founded and practically useful, enabling the solution of otherwise intractable swarm problems.
Sharp Interface Limit of the Cahn-Hilliard Reaction Model for Lithium-ion Batteries

We propose a weak solution theory for the sharp interface limit of the Cahn-Hilliard reaction model, a variational PDE for lithium-ion batteries. An essential feature of this model is the use of Butler-Volmer kinetics for lithium-ion insertion, which arises as a Robin-type boundary condition relating the flux of the chemical potential to the reaction rate, itself a nonlinear function of the chemical potential and the ion concentration. To pass through the nonlinearity as interface width vanishes, we introduce solution concepts at the diffuse and sharp interface level describing dynamics principally in terms of an optimal dissipation inequality. Using this functional framework and under an energy convergence hypothesis, we show that solutions of the Cahn-Hilliard reaction model converge to a Mullins-Sekerka type geometric evolution equation.
On the dissipation of ideal Hamiltonian Monte Carlo sampler

We report on what seems to be an intriguing connection between variable integration time and partial velocity refreshment of Ideal Hamiltonian Monte Carlo samplers, both of which can be used for reducing the dissipative behaviour of the dynamics. More concretely, we show that on quadratic potentials, efficiency can be improved through these means by a κ$$ \sqrt{\kappa } $$ factor in Wasserstein‐2 distance, compared to classical constant integration time, fully refreshed HMC. We additionally explore the benefit of randomized integrators for simulating the Hamiltonian dynamics under higher order regularity conditions.
Measuring Geographic Performance Disparities of Offensive Language Classifiers

Text classifiers are applied at scale in the form of one-size-fits-all solutions. Nevertheless, many studies show that classifiers are biased regarding different languages and dialects. When measuring and discovering these biases, some gaps present themselves and should be addressed. First, “Does language, dialect, and topical content vary across geographical regions? ” and secondly “If there are differences across the regions, do they impact model performance? ”. We introduce a novel dataset called GeoOLID with more than 14 thousand examples across 15 geographically and demographically diverse cities to address these questions. We perform a comprehensive analysis of geographical-related content and their impact on performance disparities of offensive language detection models. Overall, we find that current models do not generalize across locations. Likewise, we show that while offensive language models produce false positives on African American English, model performance is not correlated with each city’s minority population proportions. Warning: This paper contains offensive language.
Do Cloud Developers Prefer CLIs or Web Consoles? CLIs Mostly, Though It Varies by Task

Despite the increased importance of Cloud tooling, and many large-scale studies of Cloud users, research has yet to answer what tool modalities (e.g. CLI or web console) developers prefer. In formulating our studies, we quickly found that preference varies heavily based on the programming task at hand. To address this gap, we conducted a two-part research study that quantifies modality preference as a function of programming task. Part one surveys how preference for three tool modalities (CLI, IDE, web console) varies across three classes of task (CRUD, debugging, monitoring). The survey shows, among 60 respondents, developers most prefer the CLI modality, especially for CRUD tasks. Monitoring tasks are the exception for which developers prefer the web console. Part two observes how four participants complete a task using the kubectl CLI and the OpenShift web console. All four participants prefer using the CLI to accomplish the task.
Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives

Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has gained significant attention in computer vision. Over the past few years, various deep learning methods have been developed to achieve state-of-the-art performance. The methods evolved over time vary in many aspects such as model architecture, input pipeline, learning paradigm, computational complexity, and accuracy gains etc. In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting. Although few surveys exist on the topic, our survey is most up-to date and different in several aspects. First, it provides a more meaningful categorization of the most significant contributions by model architectures, learning methods (i.e., loss functions), and evaluation methods (i.e., evaluation metrics). We chose prominent and distinct works and excluded similar works. We also sort the well-known crowd counting models by their performance over benchmark datasets. We believe that this survey can be a good resource for novice researchers to understand the progressive developments and contributions over time and the current state-of-the-art.
An analytical study of the MHD clamshell instability on a sphere

Abstract This paper studies the instability of two-dimensional magnetohydrodynamic systems on a sphere using analytical methods. The underlying flow consists of a zonal differential rotation and a toroidal magnetic field is present. Semicircle rules that prescribe the possible domain of the wave velocity in the complex plane for general flow and field profiles are derived. The paper then sets out an analytical study of the ‘clamshell instability’, which features field lines on the two hemispheres tilting in opposite directions (Cally, Sol. Phys., vol. 199, 2001, pp. 231–249). An asymptotic solution for the instability problem is derived for the limit of weak shear of the zonal flow, via the method of matched asymptotic expansions. It is shown that when the zonal flow is solid body rotation, there exists a neutral mode that tilts the magnetic field lines, referred to as the ‘tilting mode’. A weak shear of the zonal flow excites the critical layer of the tilting mode, which reverses the tilting direction to form the clamshell pattern and induces the instability. The asymptotic solution provides insights into properties of the instability for a range of flow and field profiles. A remarkable feature is that the magnetic field affects the instability only through its local behaviour in the critical layer.
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)

Mean-Field Control (MFC) has recently been proven to be a scalable tool to approximately solve large-scale multi-agent reinforcement learning (MARL) problems. However, these studies are typically limited to unconstrained cumulative reward maximization framework. In this paper, we show that one can use the MFC approach to approximate the MARL problem even in the presence of constraints. Speciﬁcally, we prove that, an N -agent constrained MARL problem, with state, and action spaces of each individual agents being of sizes |X| , and |U| respectively, can be approximated by an associated constrained MFC problem with an error, e (cid:44) O (cid:16) [ (cid:112) |X| + (cid:112) |U| ] / √ N (cid:17) . In a special case where the reward, cost, and state transition functions are independent of the action distribution of the population, we prove that the error can be improved to e = O ( (cid:112) |X| / √ N ) . Also, we provide a Natural Policy Gradient based algorithm, and prove that it can solve the constrained MARL problem within an error of O ( e ) with a sample complexity of O ( e − 6 ) .
Robust Locomotion on Legged Robots through Planning on Motion Primitive Graphs

The functional demands of robotic systems often require completing various tasks or behaviors under the effect of disturbances or uncertain environments. Of increasing interest is the autonomy for dynamic robots, such as multirotors, motor vehicles, and legged platforms. Here, disturbances and environmental conditions can have significant impact on the successful performance of the individual dynamic behaviors, referred to as “motion primitives”. Despite this, robustness can be achieved by switching to and transitioning through suitable motion primitives. This paper contributes such a method by presenting an abstraction of the motion primitive dynamics and a corresponding”motion primitive transfer function”. From this, a mixed discrete and continuous “motion primitive graph” is constructed, and an algorithm capable of online search of this graph is detailed. The result is a framework capable of realizing holistic robustness on dynamic systems. This is experimentally demonstrated for a set of motion primitives on a quadrupedal robot, subject to various environmental and intentional disturbances.
Control Barrier Function Contracts for Vehicular Mission Planning Under Signal Temporal Logic Specifications

We present a compositional control synthesis method based on assume-guarantee contracts with application to correct-by-construction design of vehicular mission plans. In our approach, a mission-level specification expressed in a fragment of signal temporal logic (STL) is decomposed into predicates defined on non-overlapping time intervals. The STL predicates are then mapped to an aggregation of contracts associated with continuously differentiable time-varying control barrier functions. The barrier functions are used to constrain the lower-level control synthesis problem, which is solved via quadratic programming. Our approach can avoid the conservatism of previous methods for task-driven control based on under-approximations. We illustrate its effectiveness on a case study motivated by vehicular mission planning under safety constraints as well as constraints imposed by traffic regulations under vehicle-to-vehicle and vehicle-to-infrastructure communication.
Supersymmetry of Tensionless Rotating Strings in AdS 5 × S 5 , and Nearly-BPS Operators

: It is shown that a class of rotating strings in AdS 5 × S 5 with SO (6) angular momenta ( J, J ′ , J ′ ) preserve 1/8-supersymmetry for large J, J ′ , in which limit they are eﬀectively tensionless; when J = 0, supersymmetry is enhanced to 1/4. These results imply that recent checks of the AdS/CFT correspondence actually test a nearly-BPS sector.
Super-Real Fields: Totally Ordered Fields with Additional Structure

Introduction 1. Ordered sets and ordered groups 2. Ordered fields 3. Completions of ordered groups and fields 4. Algebras of continuous functions 5. Normability and universality 6. The operational calculus and the field R 7. Examples 8. Non-standard structures for super-real fields and the gap theorem 9. R as a hyper-real field 10. Models and weak Cauchy completeness 11. Rigid fields and solids structures 12. Open questions
Potential molecular mechanism of ascites syndrome in broilers

SUMMARY Ascites syndrome in broilers (AS), also known as broiler pulmonary hypertension syndrome (PHS), is a common nutritional and metabolic disease in commercial broilers. It is a disease characterised by a progressive increase in pulmonary artery pressure, followed by right ventricular hypertrophy, right heart failure and ultimately death. Diseased chickens with AS have characteristic lesions including right heart hypertrophy, ascites, pulmonary congestion and pulmonary oedema can be observed after opening the thoracic cavity. Pulmonary arterial hypertension (PAH) is a key link in the development of AS in broilers. Pulmonary artery vasoconstriction and pulmonary artery remodelling promote the development of AS. The AS is commonly influenced by a variety of mechanisms including hypoxia, endothelial dependency regulation, vascular active material imbalance, oxidative stress, inflammation, in situ thrombosis and genetic tendency. This review focuses on the regulation of vasoactive substances, oxidative stress, Hypoxia-inducible factors, immunomodulation and non-epigenetic phenomena on AS. The aim of this study is to review the current understanding of the pathogenesis of AS and to provide insights into the future development of this growing field for the control of AS in broiler chickens.
Conformal indices of riemannian manifolds

Soit P un operateur differentiel formellement auto-adjoint, conformement covariant sur des champs tensoriels, defini pour des varietes de Riemann (M, g), et on suppose que le symbole principal de P est toujours definit positif. On obtient des resultats sur l'invariance du coefficient t° du developpement asymptotique de Minakshisundaram-Pleijel de la trace L 2 de l'operateur de la chaleur exp(-tP)
A space fibre as added value in finite element modelling for optimal analysis of problems involving contact

The present work deals with the non-linear formulation of an axisymmetric hyperelastic solid model, based on the concept space fibre rotation (SFR). The SFR-Axi model uses the kinematics of a space fibre to obtain a quite accurate displacement field. It improves in a significant way the precision of the linear element Q4-Axi. It can even be compared, on the accuracy and CPU time level, with the high-order elements as Q8-Axi for instance. A hyperelastic law, based on the Mooney–Rivlin model, is implemented to allow to the present model a better simulation of the forming process of hollow plastic bodies. The numerical results relate to primarily some known tests of hyperelastic structures, with and without contact (swellings).