Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro activity of ceftazidime-avibactam, imipenem-relebactam, aztreonam-avibactam, and comparators toward carbapenem-resistant and hypervirulent Klebsiella pneumoniae isolates

ABSTRACT Carbapenem-resistant and hypervirulent Klebsiella pneumoniae (CR-hvKP) strains are increasingly reported, posing a significant threat to public health. Therefore, effective antimicrobial therapy is urgently needed. This study aimed to analyze the in vitro activity of ceftazidime-avibactam (CZA), imipenem-relebactam (IMR), and aztreonam-avibactam (AZA) toward CR-hvKP and carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates. Non-repetitive clinical CRKP and CR-hvKP strains were collected from Sichuan Provincial People’s Hospital between August 2018 and June 2022. CR-hvKP strains were screened using string tests and polymerase chain reaction (PCR). The microbroth dilution method was used to evaluate in vitro antibacterial activity of CZA, IMR, and AZA toward CRKP and CR-hvKP stains. The molecular characteristics of CRKP and CR-hvKP strains were investigated using PCR amplification. The virulence features of CR-hvKP strains were investigated using serum resistance assays and a Galleria mellonella infection model. A total of 114 CRKP and 40 CR-hvKP strains were collected. The susceptibility rates of CRKP and CR-hvKP to tigecycline, colistin, and polymyxin B exceeded 89.5%. The susceptibility rates of CRKP and CR-hvKP to CZA were 64.0% and 77.5%, respectively; the susceptibility rates to IMR were 92.5% and 71.9%, respectively; and the susceptibility rates to AZA were 89.5% and 75.0%, respectively. Multilocus sequence typing and wzi-loci sequencing identified sequence type 11 KL64 as the predominant type in CRKP and CR-hvKP strains. Carbapenemase genes were dominated by blaKPC-2 and blaNDM-1. IMR and AZA may be promising therapeutic agents for the treatment of infections caused by CRKP and CR-hvKP isolates. IMPORTANCE To our knowledge, this is the first study to report the in vitro activity of two novel antimicrobial drugs, including imipenem-relebactam (IMR) and aztreonam-avibactam (AZA), toward carbapenem-resistant and hypervirulent Klebsiella pneumoniae (CR-hvKP) strains. Our in vitro activity study revealed that only few antibacterial agents (including several novel agents) exhibit high antimicrobial activity toward carbapenem-resistant Klebsiella pneumoniae (CRKP) and CR-hvKP isolates. IMR and AZA may be promising therapeutic agents for the treatment of infections caused by CRKP and CR-hvKP isolates. To our knowledge, this is the first study to report the in vitro activity of two novel antimicrobial drugs, including imipenem-relebactam (IMR) and aztreonam-avibactam (AZA), toward carbapenem-resistant and hypervirulent Klebsiella pneumoniae (CR-hvKP) strains. Our in vitro activity study revealed that only few antibacterial agents (including several novel agents) exhibit high antimicrobial activity toward carbapenem-resistant Klebsiella pneumoniae (CRKP) and CR-hvKP isolates. IMR and AZA may be promising therapeutic agents for the treatment of infections caused by CRKP and CR-hvKP isolates.
Exploring the thermally-controlled fentanyl transdermal therapy to provide constant drug delivery by physics-based digital twins

Transdermal drug delivery is suitable for low-molecular-weight drugs with specific lipophilicity, like fentanyl, which is widely used for cancer-induced pain management. However, fentanyl's transdermal therapy displays high intra-individual variability. Factors like skin characteristics at application sites and ambient temperature contribute to this variation. In this study, we developed a physics-based digital twin of the human body to cope with this variability and propose better adapted setups. This twin includes an in-silico skin model for drug penetration, a pharmacokinetic model, and a pharmacodynamic model. Based on the results of our simulations, applying the patch on the flank (side abdominal area) showed a 15.3% higher maxi-mum fentanyl concentration in the plasma than on the chest. Additionally, the time to reach this maximum concentration when delivered through the flank was 19.8 h, which was 10.3 h earlier than via the upper arm. Finally, this variation led to an 18% lower minimum pain intensity for delivery via the flank than the chest. Moreover, the impact of seasonal changes on ambient temperature and skin temperature by considering the activity level was investigated. Based on our result, the fentanyl uptake flux by capillaries increased by up to 11.8% from an inactive state in winter to an active state in summer. We also evaluated the effect of controlling fentanyl delivery by adjusting the temperature of the patch to alleviate the pain to reach a mild pain intensity (rated three on the VAS scale). By implementing this strategy, the average pain intensity decreased by 1.1 points, and the standard deviation for fentanyl concentration in plasma and average pain intensity reduced by 37.5% and 33.3%, respectively. Therefore, our digital twin demonstrated the efficacy of controlled drug release through temperature regulation, ensuring the therapy toward the intended target outcome and reducing therapy outcome variability. This holds promise as a potentially useful tool for physicians.
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems

Evaluating the risk level of adversarial images is essential for safely deploying face authentication models in the real world. Popular approaches for physical-world attacks, such as print or replay attacks, suffer from some limitations, like including physical and geometrical artifacts. Recently adversarial attacks have gained attraction, which try to digitally deceive the learning strategy of a recognition system using slight modifications to the captured image. While most previous research assumes that the adversarial image could be digitally fed into the authentication systems, this is not always the case for systems deployed in the real world. This paper demonstrates the vulnerability of face authentication systems to adversarial images in physical world scenarios. We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting. Using this attack strategy, the attack success rate goes up to 82.01%. We test AdvGen extensively on four datasets and ten state-of-the-art PADs. We also demonstrate the effectiveness of our attack by conducting experiments in a realistic, physical environment.
Optimal control of thermal and mechanical loads in activation processes of mechanical components

This paper develops a mathematical framework that aims to control the temperature and rotational speed in the activation process of a gas turbine in an optimal way. These controls influence the deformation and the stress in the component due to centripetal loads and transient thermal stress. An optimal control problem is formulated as the minimization of maximal von Mises stress over a given time and over the whole component. To find a solution for this, we need to solve the linear thermoelasticity and the heat equations using the finite element method. The results for the optimal control as functions of the rotation speed and external gas temperature over time are computed by sequential quadratic programming, where gradients are computed using finite differences.The overall outcome reveals a significant reduction of approximately 10\%, from $830 \frac{N}{mm^2}$ to $750 \frac{N}{mm^2}$, in von Mises stress by controlling two parameters, along with the temporal separation of physical control phenomena.
Power Aware Scheduling of Tasks on FPGAs in Data Centers

A variety of computing platform like Field Programmable Gate Array (FPGA), Graphics Processing Unit (GPU) and multicore Central Processing Unit (CPU) in data centers are suitable for acceleration of data-intensive workloads. Especially, FPGA platforms in data centers are gaining popularity for high-performance computations due to their high speed, reconfigurable nature and cost effectiveness. Such heterogeneous, highly parallel computational architectures in data centers, combined with high-speed communication technologies like 5G, are becoming increasingly suitable for real-time applications. However, flexibility, cost-effectiveness, high computational capabilities, and energy efficiency remain challenging issues in FPGA based data centers. In this context an energy efficient scheduling solution is required to maximize the resource profitability of FPGA. This paper introduces a power-aware scheduling methodology aimed at accommodating periodic hardware tasks within the available FPGAs of a data center at their potentially maximum speed. This proposed methodology guarantees the execution of these tasks us ing the maximum number of parallel computation units possible to implement in the FPGAs, with minimum power consumption. The proposed scheduling methodology is implemented in a data center with multiple Alveo-50 Xilinx-AMD FPGAs and Vitis 2023 tool. The evidence from the implementation shows the proposed scheduling methodology is efficient compared to existing solutions.
Measurement of the jet mass and angularities in Pb-Pb collisions at 5.02 TeV with ALICE

Jet substructure observables provide powerful tools to search for new physics and test theoretical descriptions of perturbative and non-perturbative processes in QCD. In heavy-ion collisions, jet substructure observables are used to elucidate the structure and dynamics of the quark-gluon plasma. Jet mass is one such observable, which probes the virtuality of hard-scattered partons and their modified fragmentation. Additionally, generalized jet angularities provide a powerful tool for differential measurements of the jet shower and its modification, as two parameters vary the weight of the jet constituents' relative angle and $p_\mathrm{T}$. Previous measurements of the jet mass and jet angularities have shown conflicting differences in comparison with models. To clarify these results, we present new measurements of the jet mass and jet angularities using an identical jet sample. The high-precision tracking system of ALICE enables these measurements over a broad range in $p_\mathrm{T}$, with low-$p_\mathrm{T}$ reach that is unique at the LHC. We report the generalized jet mass and jet angularities using charged-particle tracks in Pb-Pb collisions at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV. Various jet angularity parameters are investigated for the jet resolution parameter $R = 0.2$. Results are compared to pp collisions and theoretical models.
Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction

We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios like collaborative manufacturing where prexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot's behavior, but such models do not account for the influence the robot has on the human's actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot's behavior based on a conditional model of the human that depends on the robot's future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human's intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and path generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users' interactions with a robot collaborator quantitatively and qualitatively.
A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

As an emerging mode of urban transportation, Autonomous Mobility-on-Demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services. However, the spatiotemporal imbalances between mobility demand and supply may lead to inefficiencies and a low quality of service. Vehicle rebalancing (i.e., dispatching idle vehicles to high-demand areas), is a potential solution for efficient AMoD fleet management. In this paper, we formulate the vehicle rebalancing problem as a coverage control problem for the deployment of a fleet of mobile agents for AMoD operation in urban areas. Performance is demonstrated via microscopic simulations representing a large urban road network of Shenzhen, China. Results reveal the potential of the proposed method in improving service rates and decreasing passenger waiting times.
Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning

In this work, we use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs). Without the requirement of complex equipment, our method only takes simple RGB images captured by a drone as inputs to enable physically based and photorealistic novel-view rendering, relighting, and editing. However, a real-world facade usually has complex appearances ranging from diffuse rocks with subtle details to large-area glass windows with specular reflections, making it hard to attend to everything. As a result, previous methods can preserve the geometry details but fail to reconstruct smooth glass windows or verse vise. In order to address this challenge, we introduce three spatial- and semantic-adaptive optimization strategies, including a semantic regularization approach based on zero-shot segmentation techniques to improve material consistency, a frequency-aware geometry regularization to balance surface smoothness and details in different surfaces, and a visibility probe-based scheme to enable efficient modeling of the local lighting in large-scale outdoor environments. In addition, we capture a real-world facade aerial 3D scanning image set and corresponding point clouds for training and benchmarking. The experiment demonstrates the superior quality of our method on facade holistic inverse rendering, novel view synthesis, and scene editing compared to state-of-the-art baselines.
Analysing landmarks in nature and elements of geospatial images to support wayfinding

Author Pyry Kettunen Name of the doctoral dissertation Analysing landmarks in nature and elements of geospatial images to support wayfinding Publisher Finnish Geodetic Institute Unit Department of Geoinformatics and Cartography Series Publications of the Finnish Geodetic Institute Field of research Geoinformatics Manuscript submitted 9 May 2014 Date of the defence 4 October 2014 Permission to publish granted 6 August 2014 Language English 2 Monograph 4 Article dissertation (summary + original articles)
BeamSync: Over-The-Air Synchronization for Distributed Massive MIMO Systems

In distributed massive multiple-input multiple-output (MIMO) systems, multiple geographically separated access points (APs) communicate simultaneously with a user, leveraging the benefits of multi-antenna coherent MIMO processing and macro-diversity gains from the distributed setups. However, time and frequency synchronization of the multiple APs is crucial to achieve good performance and enable joint precoding. In this paper, we analyze the synchronization requirement among multiple APs from a reciprocity perspective, taking into account the multiplicative impairments caused by mismatches in radio frequency (RF) hardware. We demonstrate that a phase calibration of reciprocity-calibrated APs is sufficient for the joint coherent transmission of data to the user. To achieve synchronization, we propose a novel over-the-air synchronization protocol, named BeamSync, to calibrate the geographically separated APs without sending any measurements to the central processing unit (CPU) through fronthaul. We show that sending the synchronization signal in the dominant direction of the channel between APs is optimal. Additionally, we derive the optimal phase and frequency offset estimators. Simulation results indicate that the proposed BeamSync method enhances performance by 3 dB when the number of antennas at the APs is doubled. Moreover, the method performs well compared to traditional beamforming techniques.
Tactics2D: A Multi-agent Reinforcement Learning Environment for Driving Decision-making

Tactics2D is an open-source multi-agent reinforcement learning library with a Python backend. Its goal is to provide a convenient toolset for researchers to develop decision-making algorithms for autonomous driving. The library includes diverse traffic scenarios implemented as gym -based environments equipped with multi-sensory capabilities and violation detection for traffic rules. Additionally, it features a reinforcement learning baseline tested with reasonable evaluation metrics. Tactics2D is highly modular and customizable. The source code of Tactics2D is available on https://github.com/WoodOxen/Tactics2D .
Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons

The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational neuroscience and has been instrumental in studying our brains $\textit{in silico}$. Due to the sequential nature of simulating these neural models, a commonly faced issue is the speed-accuracy trade-off: either accurately simulate a neuron using a small discretisation time-step (DT), which is slow, or more quickly simulate a neuron using a larger DT and incur a loss in simulation accuracy. Here we provide a solution to this dilemma, by algorithmically reinterpreting the ALIF model, reducing the sequential simulation complexity and permitting a more efficient parallelisation on GPUs. We computationally validate our implementation to obtain over a $50\times$ training speedup using small DTs on synthetic benchmarks. We also obtained a comparable performance to the standard ALIF implementation on different supervised classification tasks - yet in a fraction of the training time. Lastly, we showcase how our model makes it possible to quickly and accurately fit real electrophysiological recordings of cortical neurons, where very fine sub-millisecond DTs are crucial for capturing exact spike timing.
Hydrogen Doping Induced $p_x\pm ip_y$ Triplet Superconductivity in Quasi-One-Dimensional K$_2$Cr$_3$As$_3$

Quasi-one-dimensional (Q1D) Cr-based pnictide K$_2$Cr$_3$As$_3$ has aroused great research interest due to its possible triplet superconducting pairing symmetry. Recent experiments have shown that incorporating hydrogen atoms into K$_2$Cr$_3$As$_3$ would significantly change its electronic and magnetic properties. Hence, it's necessary to investigate the impact of hydrogen doping in superconducting pairing symmetry of this material. Employing the hydrogen as an non-trivial electron-doping, our calculates show that, different from the $p_z$-wave obtained without hydrogen, the system exhibits $p_x\pm ip_y$ pairing superconductivity under specific hydrogen doping. Specifically, we adopt the random-phase-approximation approach based on a six-band tight-binding model equipped with multi-orbital Hubbard interactions to study the hydrogen-doping dependence of the pairing symmetry and superconducting $T_c$. Under the rigid-band approximation, our pairing phase diagram shows the spin-triplet pairing states is dominated through out the hydrogen-doping regime $x\in (0,0.7)$. Particularly, the $T_c\sim x$ curve shows a peak at the 3D-quasi-1D Lifshitz transition point, and the pairing symmetry around this doping level is $p_x\pm ip_y$. The physical origin of this pairing symmetry is that the density of states is mainly concentrated at $k_x(k_y)$ with large momentum. Due to the three-dimensional character of the real material, this $p_x\pm ip_y$-wave superconducting state possesses point gap nodes. We further provide experiment prediction to identify this triplet $p_x\pm ip_y$-wave superconductivity.
Robust Condition-Based Operations&Maintenance: Synergizing Multi-Asset Degradation Rate Interactions and Operations-Induced Degradation

Effective operations and maintenance (O&M) in modern production systems hinges on careful orchestration of economic and degradation dependencies across a multitude of assets. While the economic dependencies are well studied, degradation dependencies and their impact on system operations remain an open challenge. To address this challenge, we model condition-based production and maintenance decisions for multi-asset systems with degradation interactions. There is a rich literature on condition-based O&M policies for single-asset systems. These models fail to represent modern systems composed of multiple interacting assets. We are providing the first O&M model to optimize O&M in multi-asset systems with embedded decision-dependent degradation interactions. We formulate robust optimization models that inherently capture degradation and failure risks by embedding degradation signals via a set of constraints, and building condition-based uncertainty sets to model probable degradation scenarios. We offer multiple reformulations and a solution algorithm to ensure computational scalability. Performance of the proposed O&M model is evaluated through extensive experiments, where the degradation is either emulated or taken from vibration-based readings from a rotating machinery system. The proposed model provides significant improvements in terms of operation, maintenance, and reliability metrics. Due to a myriad of dependencies across assets and decisions, it is often difficult to translate asset-level failure predictions to system-level O&M decisions. This challenge puts a significant barrier to the return on investment in condition monitoring and smart maintenance systems. Our approach offers a seamless integration of data-driven failure modeling and mathematical programming to bridge the gap across predictive and prescriptive models.
Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names

Every human has their own name, a fundamental aspect of their identity and cultural heritage. The name often conveys a wealth of information, including details about an individual's background, ethnicity, and, especially, their gender. By detecting gender through the analysis of names, researchers can unlock valuable insights into linguistic patterns and cultural norms, which can be applied to practical applications. Hence, this work presents a novel dataset for Japanese name gender detection comprising 64,139 full names in romaji, hiragana, and kanji forms, along with their biological genders. Moreover, we propose Gendec, a framework for gender detection from Japanese names that leverages diverse approaches, including traditional machine learning techniques or cutting-edge transfer learning models, to predict the gender associated with Japanese names accurately. Through a thorough investigation, the proposed framework is expected to be effective and serve potential applications in various domains.