Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacokinetic/Pharmacodynamic Index Linked to In Vivo Efficacy of the Ampicillin-Ceftriaxone Combination against Enterococcus faecalis

Combination therapy with ampicillin plus ceftriaxone (AMP+CRO) is the first-line therapy for treating severe infections due to Enterococcus faecalis. However, the pharmacokinetic/pharmacodynamic (PK/PD) index linked to the in vivo efficacy of the combination is not yet defined, hindering dose optimization in the clinic. ABSTRACT Combination therapy with ampicillin plus ceftriaxone (AMP+CRO) is the first-line therapy for treating severe infections due to Enterococcus faecalis. However, the pharmacokinetic/pharmacodynamic (PK/PD) index linked to the in vivo efficacy of the combination is not yet defined, hindering dose optimization in the clinic. Because classical PK/PD indices are not directly applicable to antimicrobial combinations, two novel indices were tested in the optimized murine model of infection by E. faecalis to delineate the potentiation of AMP by CRO: the time above the CRO threshold (T>threshold) and the time above the AMP instantaneous MIC (T>MICi). The potential clinical relevance was evaluated by simulating human doses of AMP and CRO. Hill’s equation fitted well the exposure-response data in terms of T>threshold, with a CRO threshold of 1 mg/L. The required exposures were 46%, 49%, and 52% for stasis and 1- and 2-log10 killing, respectively. Human ceftriaxone doses of 2 g every 12 h (q12h) would reach the target in >90% of strains with thresholds ≤64 mg/L. The AMP T>MICi index also fitted well, and the required exposures were 37%, 41%, and 46% for stasis and 1- and 2-log10 killing, respectively. In humans, the addition of CRO would allow use of lower AMP doses to reach the same T>MICi and to treat strains with higher MICs. This is the first report of the PK/PD indices and required magnitudes linked to AMP+CRO against E. faecalis; these results can be used as the basis to guide the design of clinical trials to improve combined therapy against enterococci.
An interactive optimization system for bulk-cargo ship scheduling

This article considers the efficient scheduling of a fleet of ships engaged in pickup and delivery of bulk cargoes. Our optimization system begins by generating a menu of candidate schedules for each ship. This menu can contain all feasible solutions, which guarantees we will find an optimal solution or can be heuristically limited to contain only those schedules likely to be in an optimal solution. The problem of choosing from this menu an optimal schedule for the fleet is formulated as a set-packing problem and solved with a dual algorithm. Computational experience is presented based on real data obtained from the Military Sealift Command of the U. S. Navy. Run times for this data were reasonable and solutions were generated with the potential of saving up to about $30 million per year over the manual system currently in place. We also describe a color-graphics interface developed to facilitate interaction with the optimization system.
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection

Adverse drug events (ADEs) are an important aspect of drug safety. Various texts such as biomedical literature, drug reviews, and user posts on social media and medical forums contain a wealth of information about ADEs. Recent studies have applied word embedding and deep learning -based natural language processing to automate ADE detection from text. However, they did not explore incorporating explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. This paper adopts the heterogenous text graph which describes relationships between documents, words and concepts, augments it with medical knowledge from the Unified Medical Language System, and proposes a concept-aware attention mechanism which learns features differently for the different types of nodes in the graph. We further utilize contextualized embeddings from pretrained language models and convolutional graph neural networks for effective feature representation and relational learning. Experiments on four public datasets show that our model achieves performance competitive to the recent advances and the concept-aware attention consistently outperforms other attention mechanisms.
The Frontier of Intractability for EFX with Two Agents

We consider the problem of sharing a set of indivisible goods among agents in a fair manner, namely such that the allocation is envy-free up to any good (EFX). We focus on the problem of computing an EFX allocation in the two-agent case and characterize the computational complexity of the problem for most well-known valuation classes. We present a simple greedy algorithm that solves the problem when the agent valuations are weakly well-layered, a class which contains gross substitutes and budget-additive valuations. For the next largest valuation class we prove a negative result: the problem is PLS-complete for submodular valuations. All of our results also hold for the setting where there are many agents with identical valuations.
On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems

The performance of neural network-based speech enhancement systems is primarily influenced by the model architecture, whereas training times and computational resource utilization are primarily affected by training parameters such as the batch size. Since noisy and reverberant speech mixtures can have different duration, a batching strategy is required to handle variable size inputs during training, in particular for state-of-the-art end-to-end systems. Such strategies usually strive for a compromise between zero-padding and data randomization, and can be combined with a dynamic batch size for a more consistent amount of data in each batch. However, the effect of these strategies on resource utilization and more importantly network performance is not well documented. This paper systematically investigates the effect of different batching strategies and batch sizes on the training statistics and speech enhancement performance of a Conv-TasNet, evaluated in both matched and mismatched conditions. We find that using a small batch size during training improves performance in both conditions for all batching strategies. Moreover, using sorted or bucket batching with a dynamic batch size allows for reduced training time and GPU memory usage while achieving similar performance compared to random batching with a fixed batch size.
A Majority Logic Synthesis Framework For Single Flux Quantum Circuits

Exascale computing and its associated applications have required increasing degrees of efficiency. Semiconductor-Transistor-based Circuits (STbCs) have struggled with increasing the GHz frequency while dealing with power dissipation issues. Emerging as an alternative to STbC, single flux quantum (SFQ) logic in the superconducting electrons (SCE) technology promises higher-speed clock frequencies at ultra-low power consumption. However, its quantized pulse-based operation and high environmental requirements, process variations and other SFQ-specific non-idealities are the significant causes of logic error for SFQ circuits. A suitable method of minimizing the impact of the afore-mentioned error sources is to minimize the number of Josephson Junctions (JJs) in the circuits, hence an essential part of the design flow of large SFQ circuits. This paper presents a novel SFQ logic synthesis framework that given a netlist, offers an automated mapping solution including majority (MAJ) logic with the goal of minimizing the number of JJs, while catering to the unique characteristics and requirements of the design. Our experiments confirm that our synthesis framework significantly outperforms the state-of-the-art academic SFQ technology mapper, namely reducing the number of JJs on average by 35.0%.
Increasing flow efficiency of high-pressure and low-pressure steam turbine stages from numerical optimization of 3D blading

3D blading of a high-pressure and low-pressure steam turbine stage is optimized using Nelder–Mead method of deformed polyhedron. Values of the minimized objective function, i.e. stage losses with the exit energy are found from 3D viscous compressible flow computations, including turbulence effects. Among the optimized parameters are stator and rotor blade numbers and stagger angles, rotor blade twist angle, stator blade sweep and lean, both straight and compound. The blade sections (profiles) are assumed not to change during the optimization. There are constraints imposed on the design parameters, including the mass flow rate and stage reaction. Optimization gives designs with new 3D blade stacking lines, and with increased efficiencies, compared with the original design.
Integration of neutron time-of-flight single-crystal Bragg peaks in reciprocal space

The intensity of single-crystal Bragg peaks obtained by mapping neutron time-of-flight event data into reciprocal space and integrating in various ways is compared. These methods include spherical integration with a fixed radius, ellipsoid fitting and integration of the peak intensity, and one-dimensional peak profile fitting. In comparison to intensities obtained by integrating in real detector histogram space, the data integrated in reciprocal space result in better agreement factors and more accurate atomic parameters. Furthermore, structure refinement using integrated intensities from one-dimensional profile fitting is demonstrated to be more accurate than simple peak-minus-background integration.
DEJA VU: Continual Model Generalization For Unseen Domains

In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptation ability. However, these DA methods typically only provide good performance after a long period of adaptation, and perform poorly on new domains before and during adaptation - in what we call the"Unfamiliar Period", especially when domain shifts happen suddenly and significantly. On the other hand, domain generalization (DG) methods have been proposed to improve the model generalization ability on unadapted domains. However, existing DG works are ineffective for continually changing domains due to severe catastrophic forgetting of learned knowledge. To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains. RaTP includes a training-free data augmentation module to prepare data for TDG, a novel pseudo-labeling mechanism to provide reliable supervision for TDA, and a prototype contrastive alignment algorithm to align different domains for achieving TDG, TDA and FA. Extensive experiments on Digits, PACS, and DomainNet demonstrate that RaTP significantly outperforms state-of-the-art works from Continual DA, Source-Free DA, Test-Time/Online DA, Single DG, Multiple DG and Unified DA&DG in TDG, and achieves comparable TDA and FA capabilities.
Automated Multilingual Detection of Pro-Kremlin Propaganda in Newspapers and Telegram Posts

The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data reflecting opposing ideologies and narratives. These polarized campaigns have led to mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for readers worldwide. This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian, French and English. We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features. We analyse the advantages and disadvantages of both methods, their adaptability to new genres and languages, and ethical considerations of their usage for content moderation. With this work, we aim to lay the foundation for further development of moderation tools tailored to the current conflict.
Atomic data for opacity calculations: XXIV. The boron-like sequence

Energy levels, oscillator strengths and photo-ionization cross sections are calculated for all levels with n10 in boron-like ions of the elements C, N, O, Ne, Na, Mg, Al, Si, S and Fe. The R-matrix method is used. Particular attention is paid to the determination of accurate wavefunctions for the Be-like `target' states in configurations 1s22s2,1s22s2p and 1s22p2.
Neural mechanisms of temporal and rhythmic structure processing in non-musicians

Music is increasingly being used as a therapeutic tool in the field of rehabilitation medicine and psychophysiology. One of the main key components of music is its temporal organization. The characteristics of neurocognitive processes during music perception of meter in different tempo variations technique have been studied by using the event-related potentials technique. The study involved 20 volunteers (6 men, the median age of the participants was 23 years). The participants were asked to listen to 4 experimental series that differed in tempo (fast vs. slow) and meter (duple vs. triple). Each series consisted of 625 audio stimuli, 85% of which were organized with a standard metric structure (standard stimulus) while 15% included unexpected accents (deviant stimulus). The results revealed that the type of metric structure influences the detection of the change in stimuli. The analysis showed that the N200 wave occurred significantly faster for stimuli with duple meter and fast tempo and was the slowest for stimuli with triple meter and fast pace.
An Efficient Approximate Method for Online Convolutional Dictionary Learning

—Most existing convolutional dictionary learning (CDL) algorithms are based on batch learning, where the dictionary ﬁlters and the convolutional sparse representations are optimized in an alternating manner using a training dataset. When large training datasets are used, batch CDL algorithms become prohibitively memory-intensive. An online-learning tech-nique is used to reduce the memory requirements of CDL by optimizing the dictionary incrementally after ﬁnding the sparse representations of each training sample. Nevertheless, learning large dictionaries using the existing online CDL (OCDL) algorithms remains highly computationally expensive. In this paper, we present a novel approximate OCDL method that incorporates sparse decomposition of the training samples. The resulting optimization problems are addressed using the alternating direction method of multipliers. Extensive experimental evaluations using several image datasets show that the proposed method substantially reduces computational costs while preserving the effectiveness of the state-of-the-art OCDL algorithms.
Inference in Marginal Structural Models by Automatic Targeted Bayesian and Minimum Loss-Based Estimation

Two of the principle tasks of causal inference are to define and estimate the effect of a treatment on an outcome of interest. Formally, such treatment effects are defined as a possibly functional summary of the data generating distribution, and are referred to as target parameters. Estimation of the target parameter can be difficult, especially when it is high-dimensional. Marginal Structural Models (MSMs) provide a way to summarize such target parameters in terms of a lower dimensional working model. We introduce the semi-parametric efficiency bound for estimating MSM parameters in a general setting. We then present a frequentist estimator that achieves this bound based on Targeted Minimum Loss-Based Estimation. Our results are derived in a general context, and can be easily adapted to specific data structures and target parameters. We then describe a novel targeted Bayesian estimator and provide a Bernstein von-Mises type result analyzing its asymptotic behavior. We propose a universal algorithm that uses automatic differentiation to put the estimator into practice for arbitrary choice of working model. The frequentist and Bayesian estimators have been implemented in the Julia software package TargetedMSM.jl. Finally, we illustrate our proposed methods by investigating the effect of interventions on family planning behavior using data from a randomized field experiment conducted in Malawi.
Sequential Bayesian Learning for Hidden Semi-Markov Models

In this paper, we explore the class of the Hidden Semi-Markov Model (HSMM), a flexible extension of the popular Hidden Markov Model (HMM) that allows the underlying stochastic process to be a semi-Markov chain. HSMMs are typically used less frequently than their basic HMM counterpart due to the increased computational challenges when evaluating the likelihood function. Moreover, while both models are sequential in nature, parameter estimation is mainly conducted via batch estimation methods. Thus, a major motivation of this paper is to provide methods to estimate HSMMs (1) in a computationally feasible time, (2) in an exact manner, i.e. only subject to Monte Carlo error, and (3) in a sequential setting. We provide and verify an efficient computational scheme for Bayesian parameter estimation on HSMMs. Additionally, we explore the performance of HSMMs on the VIX time series using Autoregressive (AR) models with hidden semi-Markov states and demonstrate how this algorithm can be used for regime switching, model selection and clustering purposes.
ARDIAS: AI-Enhanced Research Management, Discovery, and Advisory System

In this work, we present ARDIAS, a web-based application that aims to provide researchers with a full suite of discovery and collaboration tools. ARDIAS currently allows searching for authors and articles by name and gaining insights into the research topics of a particular researcher. With the aid of AI-based tools, ARDIAS aims to recommend potential collaborators and topics to researchers. In the near future, we aim to add tools that allow researchers to communicate with each other and start new projects.
A Mathematical Programming approach to Overlapping community detection by cluster's Shapley value

We propose a new model to detect the overlapping communities of a network that is based on cooperative games and mathematical programming. More specifically, communities are defined as stable coalitions of a weighted graph community game and they are revealed as the optimal solution of a mixed-integer linear programming problem. Exact optimal solutions are obtained for small and medium sized instances and and it is shown that they provide useful information about the network structure, improving on previous contributions. Next, a heuristic algorithm is developed to solve the largest instances and used to compare two variations of the objective function.
Hydrodynamic collapse of the Leidenfrost vapor layer

During the Leidenfrost effect, a stable vapor film can separate a hot solid from an evaporating liquid. Eventually, after formation and upon cooling, the vapor layer cannot be sustained and undergoes a violent collapse evidenced by explosive boiling. Computationally, modeling this instability involves an interplay between hydrodynamics, thermodynamics, rapid evaporation, and length-scales from $\mu$m to cm. Selective assumptions, made to reduce computational costs, have limited most previous studies to steady-state investigations. Here, we combine two-phase laminar flow, heat transfer, and evaporation in a finite-element simulation to examine the failure of Leidenfrost vapor layers during cooling. During periods of quiescence, the geometry of the vapor layer agrees well with steady-state lubrication theory. In the simulations, we report the local temperature of the solid at failure, $T_-$, which provides a lower bound for recent experimental work using the same geometric and material conditions. Surprisingly, we find that inertial forces, which are typically ignored in theoretical treatments of the vapor layer, are responsible for initiating the instability leading to failure.
A Fast Feature Point Matching Algorithm Based on IMU Sensor

—In simultaneous localization and mapping (SLAM), image feature point matching process consume a lot of time. The capacity of low-power systems such as embedded systems is almost limited. It is difficult to ensure the timely processing of each image information. To reduce time consuming when matching feature points in SLAM, an algorithm of using inertial measurement unit (IMU) to optimize the efficiency of image feature point matching is proposed. When matching two image feature points, the presented algorithm doesn’t need to traverse the whole image for matching feature points, just around the predicted point within a small range traversal search to find matching feature points. After compared with the traditional algorithm, the experimental results show that this method has greatly reduced the consumption of image feature points matching time. All the conclusions will help research how to use the IMU optimize the efficiency of image feature point matching and improve the real-time performance in SLAM.
Values of $p$-adic hypergeometric functions, and $p$-adic analogue of Kummer's linear identity

Let p be an odd prime and F p be the ﬁnite ﬁeld with p elements. This paper focuses on the study of values of a generic family of hypergeometric functions in the p -adic setting which we denote by 3 n − 1 G 3 n − 1 ( p, t ) , where n ≥ 1 and t ∈ F p . These values are expressed in terms of numbers of zeros of certain polynomials over F p . These results lead to certain p -adic analogues of classical hypergeometric identities. Namely, we obtain p -adic analogues of particular cases of a Gauss’ theorem and a Kummer’s theorem. Moreover, we examine the zeros of these functions. For instance, if n is odd then we obtain zeros of 3 n − 1 G 3 n − 1 ( p, t ) = 0 under certain condition on t . In contrast we show that if n is even then the function 3 n − 1 G 3 n − 1 ( p, t ) has no non-trivial zeros for any prime p .
Transferable Fairness for Cold-Start Recommendation

With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem. Previous works on fairness-aware recommendation mainly focus on a predefined set of (usually warm-start) users. However, recommender systems often face more challenging fairness issues for new users or cold-start users due to their insuf-ficient amount of interactions. Therefore, it is essential to study whether the trained model still performs fairly for a new set of cold-start users. This paper considers the scenario where the recommender system meets new users who only have limited or even no interaction with the platform, and aims at providing high-quality and fair recommendations to such users effectively. The sufficient interaction data from warm users is treated as the source user domain, while the data from new users is treated as the target user domain, and we consider to transfer the counterfactual fairness from the source users to the target users. To this end, we introduce a framework to achieve transferable counterfactual fairness in recommendation. The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users. Experiments on two real-world datasets with representative recommendation algorithms show that our method not only promotes fairness for the target users, but also outperforms comparative models in terms of recommendation performance.