Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Human-Like Translation Strategy with Large Language Models

Abstract Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-level intelligence. Among their numerous skills, the translation abilities of LLMs have received considerable attention. Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process, which might take preparatory steps to ensure high-quality translation. This work explores this possibility by proposing the MAPS framework, which stands for Multi-Aspect Prompting and Selection. Specifically, we enable LLMs first to analyze the given source sentence and induce three aspects of translation-related knowledge (keywords, topics, and relevant demonstrations) to guide the final translation process. Moreover, we employ a selection mechanism based on quality estimation to filter out noisy and unhelpful knowledge. Both automatic (3 LLMs × 11 directions × 2 automatic metrics) and human evaluation (preference study and MQM) demonstrate the effectiveness of MAPS. Further analysis shows that by mimicking the human translation process, MAPS reduces various translation errors such as hallucination, ambiguity, mistranslation, awkward style, untranslated text, and omission. Source code is available at https://github.com/zwhe99/MAPS-mt.
Disentangled Multi-Fidelity Deep Bayesian Active Learning

To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. However, existing approaches based on Gaussian processes are hardly scalable to high-dimensional data. Deep learning-based methods often impose a hierarchical structure in hidden representations, which only supports passing information from low-fidelity to high-fidelity. These approaches can lead to the undesirable propagation of errors from low-fidelity representations to high-fidelity ones. We propose a novel framework called Disentangled Multi-fidelity Deep Bayesian Active Learning (D-MFDAL), which learns the surrogate models conditioned on the distribution of functions at multiple fidelities. On benchmark tasks of learning deep surrogates of partial differential equations including heat equation, Poisson's equation and fluid simulations, our approach significantly outperforms state-of-the-art in prediction accuracy and sample efficiency.
Newman-Janis algorithm’s application to regular black hole models

We examine the Newman-Janis algorithm's application to an exact regular static solution sustained by a minimally coupled scalar field with a non-standard kinetic term. Although coordinate complexification leads to a regular Kerr-like black hole, we are facing discrepancies in Einstein's equations in a fairly small domain, for which the regularizing parameter is responsible. Outside this most intriguing region, the geometry is nothing but the standard Kerr spacetime.
NorBench – A Benchmark for Norwegian Language Models

We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.
Alternative Foundations of Class Analysis

The concept of class is one of the most contested concepts within sociology. Sometimes this is just a question of how the word class is being used, but behind the alternative uses of the term there often lurks deeper theoretical disagreements about how best to understand the nature and consequences of economic inequality in contemporary societies. This seminar will explore in a systematic and rigorous manner the full range of alternative conceptualizations of class. The focus will be on contemporary approaches, although there will be some attention to classic statements. The course will give students an opportunity both to gain a deeper understanding of the substantive issues around the analysis of class, but also to engage in a very fine-grained manner the problem of carefully defining concepts. Sociologists often take a quite casual attitude towards the problem of concept-formation. While they may worry quite a bit about the theoretical arguments that link concepts together, much less attention is frequently paid to the underlying logic of the abstract definitions of the concepts themselves.
Extension of the wave packet molecular dynamics method towards the accurate quantum simulations of electron dynamics

In this paper, we review a multiple-wavepacket version of the Antisymmetrized Wave Packet Molecular Dynamics (AWPMD), that may be utilized to increase the accuracy and the performance of this quantum simulation method. The original WPMD method is based on parameterization of the electron wave function by a single Gaussian wave packet. It gives qualitatively better results than the classical Molecular Dynamics but the quantitative description of essentially quantum systems is rather poor. In this work, we describe a new technique based on multiple Gaussian expansion of the single-electron wave function, which is called Split Wave Packet Molecular Dynamics (SWPMD). The related theoretical formalism is given, followed by the analysis of static and dynamical properties of a quantum system of several particles, where the simulation results may be compared to the theoretical predictions. The tests are based on ionization of hydrogen atom under a strong laser pulse. We demonstrate that the SWPMD method may significantly improve the accuracy of the model wave function and enables one to simulate the quantum branching processes, including the tunneling, which was impossible in the single-wavepacket version of the method.
Comparison of Performances of GeneXpert MTB/RIF, Bactec MGIT 960, and Bactec Myco/F Systems in Detecting Mycobacterium tuberculosis in Biopsy Tissues: a Retrospective Study

Mycobacterium tuberculosis (MTB) poses a severe threat to public health worldwide. The diagnosis of tuberculosis is challenging due to the low load of the microorganism in samples. ABSTRACT Tuberculosis remains a major global public concern as a leading cause of health care-associated infections. The detection of Mycobacterium tuberculosis (MTB) is challenging due to the paucibacillary nature of the pathogen. For suspected pulmonary and extrapulmonary tuberculosis patients, if sputum, bronchoalveolar lavage fluid (BALF), related samples are negative for MTB, or suspected tumors, biopsy tissues may provide a better diagnostic yield. This study was aimed at comparing the performances of three methods in identifying MTB in biopsy tissues, including the Bactec mycobacterial growth indicator tube 960 (MGIT 960) system, the GeneXpert MTB/RIF assay (GeneXpert), and the Bactec Myco/F lytic culture (Myco/F) system. Biopsy samples from 3,209 nonduplicated patients were retrospectively enrolled between January 2018 and September 2021, of which 180 (5.6%) were positive for MTB by at least one method. GeneXpert revealed the highest recovery rate (134/162, 82.7%), followed by MGIT 960 (99/135, 73.3%) and Myco/F (26/143, 18.1%), and the composite positive rate for GeneXpert and MGIT 960 was 96.6% (173/179). Pairwise comparisons were conducted after completion of both tests, and the results showed that Myco/F had significantly lower detection rates than GeneXpert and MGIT 960 (16.4% versus 82.8%, P < 0.001; 14.3% versus 71.4%, P < 0.001). In summary, GeneXpert was the most sensitive and recommended method for MTB detection in biopsy tissues, and the combination of GeneXpert and MGIT 960 could improve the overall diagnostic yield. IMPORTANCE Mycobacterium tuberculosis (MTB) poses a severe threat to public health worldwide. The diagnosis of tuberculosis is challenging due to the low load of the microorganism in samples. Biopsy tissues are sometimes collected via invasive procedures with limited size, and additional samples are often inaccessible. The GeneXpert MTB/RIF assay, Bactec MGIT 960 system, and Bactec Myco/F lytic system have been used in detecting MTB in our laboratory. Here, we evaluated the performances of these three methods in 3,209 biopsy tissues samples to establish a more effective protocol based on clinical requirements. Attempts for a locally optimized protocol should be always made.
Survey of the Endophytic Bacteria Inhabiting Wild Daucus Seed Using 16S rRNA Gene Amplicon Sequencing

We report a preliminary survey of the endophytic bacterial microbiota of seed from wild carrot (Daucus carota) using 16S rRNA gene amplicon sequencing. Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria were the most abundant phyla detected, while Bacillus, Massilia, Paenibacillus, Pantoea, Pseudomonas, Rhizobium, Sphingomonas, and Xanthomonas were the most abundant genera. ABSTRACT We report a preliminary survey of the endophytic bacterial microbiota of seed from wild carrot (Daucus carota) using 16S rRNA gene amplicon sequencing. Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria were the most abundant phyla detected, while Bacillus, Massilia, Paenibacillus, Pantoea, Pseudomonas, Rhizobium, Sphingomonas, and Xanthomonas were the most abundant genera.
Eradication of Staphylococcus epidermidis within Biofilms: Comparison of Systemic versus Supratherapeutic Concentrations of Antibiotics

Biofilm-forming bacterial infections result in clinical failure, recurring infections, and high health care costs. The antibiotic concentrations needed to eradicate biofilm require further research. ABSTRACT Biofilm-forming bacterial infections result in clinical failure, recurring infections, and high health care costs. The antibiotic concentrations needed to eradicate biofilm require further research. We aimed to model an in vitro prosthetic joint infection (PJI) to elucidate the activity of traditional systemic concentrations versus supratherapeutic concentrations to eradicate a Staphylococcus epidermidis biofilm PJI. We evaluated S. epidermidis high-biofilm-forming (ATCC 35984) and low-biofilm-forming (ATCC 12228) isolates in an in vitro pharmacodynamic biofilm reactor model with chromium cobalt coupons to simulate prosthetic joint infection. Vancomycin, daptomycin, levofloxacin, and minocycline were used alone and combined with rifampin to evaluate the effect of biofilm eradication. We simulated three exposures: (i) humanized systemic dosing alone, (ii) supratherapeutic doses (1,000× MIC), and (iii) and dosing in combination with rifampin. Resistance development was monitored throughout the study. Simulated humanized systemic doses of a lipoglycopeptide (daptomycin), a fluoroquinolone (levofloxacin), a tetracycline (minocycline), and a glycopeptide (vancomycin) alone failed to eradicate a formed S. epidermidis biofilm. Supratherapeutic doses of vancomycin (2,000 μg/mL) and minocycline (15 μg/mL) with or without rifampin (15 μg/mL) failed to eradicate biofilms. However, a levofloxacin supratherapeutic dose (125 μg/mL) with rifampin eradicated the high-biofilm-producing isolate by 48 h. Interestingly, supratherapeutic-dose exposures of daptomycin (500 μg/mL) alone eradicated high- and low-biofilm-forming isolates in established biofilms. The concentrations needed to eradicate biofilms on foreign materials are not obtained with systemic dosing regimens. The failure of systemic dosing regimens to eradicate biofilms validates clinical findings with recurring infections. The addition of rifampin to supratherapeutic dosing regimens does not result in synergy. Supratherapeutic daptomycin dosing may be effective at the site of action to eradicate biofilms. Further studies are needed.
Performance optimization of target detection based on edge-to-cloud deep learning

With the development of mobile internet, real-time target detection using mobile devices has wide application prospects, but the computing power of the terminal greatly limits the speed and accuracy of target detection. Edge-cloud collaborative computing is the main method to solve the lack of computing power of mobile terminals. The current method can't settle the problem of computation scheduling in the edge-cloud collaboration system. Given the existing problems, this paper proposes the pruning technology of classical target detection deep learning networks; training and prediction offloading strategy of edge-to-cloud deep learning network; dynamic load balancing migration strategy based on CPU, memory, bandwidth, and disk state-changing in cluster. After testing, the edge-to-cloud deep learning method can reduce the inference delay by 50% and increase the system throughput by 40%. The maximum waiting time for operation can be reduced by about 20%. The efficiency and accuracy of target detection are effectively improved.
Lung Injury Risk in Traumatic Brain Injury Managed With Optimal Cerebral Perfusion Pressure Guided-Therapy

ABSTRACT Introduction Management of traumatic brain injury (TBI) has to counterbalance prevention of secondary brain injury without systemic complications, namely lung injury. The potential risk of developing acute respiratory distress syndrome (ARDS) leads to therapeutic decisions such as fluid balance restriction, high PEEP and other lung protective measures, that may conflict with neurologic outcome. In fact, low cerebral perfusion pressure (CPP) may induce secondary ischemic injury and mortality, but disproportionate high CPP may also increase morbidity and worse lung compliance and hypoxia with the risk of developing ARDS and fatal outcome. The evaluation of cerebral autoregulation at bedside and individualized (optimal CPP) CPPopt-guided therapy, may not only be a relevant measure to protect the brain, but also a safe measure to avoid systemic complications. Aim of the study We aimed to study the safety of CPPopt-guided-therapy and the risk of secondary lung injury association with bad outcome. Methods and results Single-center retrospective analysis of 92 severe TBI patients admitted to the Neurocritical Care Unit managed with CPPopt-guided-therapy by PRx (pressure reactivity index). During the first 10 days, we collected data from blood gas, ventilation and brain variables. Evolution along time was analyzed using linear mixed-effects regression models. 86% were male with mean age 53±21 years. 49% presented multiple trauma and 21% thoracic trauma. At hospital admission, median GCS was 7 and after 3-months GOS was 3. Monitoring data was CPP 86±7mmHg, CPP-CPPopt -2.8±10.2mmHg and PRx 0.03±0.19. The average PFratio (PaO2/FiO2) was 305±88 and driving pressure 15.9±3.5cmH2O. PFratio exhibited a significant quadratic dependence across time and PRx and driving pressure presented significant negative association with PFRatio. CPP and CPPopt did not present significant effect on PFratio (p=0.533; p=0.556). A significant positive association between outcome and the difference CPP-CPPopt was found. Conclusion Management of TBI using CPPopt-guided-therapy was associated with better outcome and seems to be safe regarding the development of secondary lung injury.
Awake Prone Decubitus Positioning in COVID-19 Patients: A Systematic Review and MetaAnalysis

ABSTRACT To date, recommendations for the implementation of awake prone positioning in patients with hypoxia secondary to SARSCoV2 infection have been extrapolated from prior studies on respiratory distress. Thus, we carried out a systematic review and metaanalysis to evaluate the benefits of pronation on the oxygenation, need for endotracheal intubation (ETI), and mortality of this group of patients. We carried out a systematic search in the PubMed and Embase databases between June 2020 and November 2021. A randomeffects metaanalysis was performed to evaluate the impact of pronation on the ETI and mortality rates. A total of 213 articles were identified, 15 of which were finally included in this review. A significant decrease in the mortality rate was observed in the group of pronated patients (relative risk [RR] = 0.69; 95% confidence interval [CI]: 0.480.99; p = 0.044), but no significant effect was observed on the need for ETI (RR = 0.79; 95% CI: 0.631.00; p = 0.051). However, a subgroup analysis of randomized clinical trials (RCTs) did reveal a significant decrease in the need for this intervention (RR = 0.83; 95% CI: 0.710.97). Prone positioning was found to significantly reduce mortality, also diminishing the need for ETI, although this effect was statistically significant only in the subgroup analysis of RCTs. Patients’ response to awake prone positioning could be greater when this procedure is implemented early and in combination with noninvasive mechanical ventilation (NIMV) or highflow nasal cannula (HFNC) therapy.
Lazy Synthesis of Symbolic Output-Feedback Controllers for State-Based Safety Specifications

This short paper presents a lazy symbolic output-feedback controller synthesis algorithm for state-based safety specifications over large transition systems. The novel idea of our approach is to integrate an iterative algorithm for observer design with an online adaptable safety controller synthesis algorithm. This allows us to iteratively update the safety controller to observer refinements and to guide these refinements by the existing controller. This results in efficient lazy synthesis of a safety controller whose domain increases with the time spent in synthesis. We present simulation results for a synthetic robot motion planning example showing the benefits of our algorithm compared to the standard approach.
Comments on "A Fast Recursive Algorithm to Calculate the Reliability of a Communication Network"

EBERHARDHANSLER Abstract-In the above paper,' a procedure is described that deter- mines the probability that all communication paths between two nodes in a network are interrupted. The problem is solved by recur- sively splitting the original problem into smaller subproblems. In this correspondence, we demonstrate that in certain classes of networks identical subproblems have to be solved several times. Thus the efficiency of the algorithm can be further improved by storing the subnetworks and the results of their evaluation.
Towards Sparse Synchronous Programming in Lua

Most software considers timing a performance issue, but for many embedded applications, the timing of a result is as important as its value. Most modern computers do have precise hardware timers, but they are not easily used to make a whole system timing-aware. Earlier, we presented the Sparse Synchronous Model for specifying deterministic, concurrent, timing-aware systems and proposed an awkward-to-use C library; here, we present lua-ssm, a Lua library that provides the benefits of SSM in a more accessible setting. Relying on Lua’s incremental garbage collector and support for coroutines, lua-ssm is both easier to use and was simpler to implement than its C counterpart. It provides both a flexible way for users to construct SSM systems and a way for us to more quickly experiment with new features.
Reachability Analysis for Linear Systems with Uncertain Parameters using Polynomial Zonotopes

In real world applications, uncertain parameters are the rule rather than the exception. We present a reachability algorithm for linear systems with uncertain parameters and inputs using set propagation of polynomial zonotopes. In contrast to previous methods, our approach is able to tightly capture the non-convexity of the reachable set. Building up on our main result, we show how our reachability algorithm can be extended to handle linear time-varying systems as well as linear systems with time-varying parameters. Moreover, our approach opens up new possibilities for reachability analysis of linear time-invariant systems, nonlinear systems, and hybrid systems. We compare our approach to other state of the art methods, with superior tightness on two benchmarks including a 9-dimensional vehicle platooning system.
Characterization of the ordering of path-complete stability certificates with addition-closed templates

As part of the development of Lyapunov techniques for cyber-physical systems, we study and compare graph-based stability certificates with respect to their conservatism. Previous work have highlighted the dependence of this ordering with respect to the properties of the chosen template of candidate Lyapunov functions. We extend here previous results from the literature to the case of templates closed under addition, as for instance the set of quadratic functions. In this context, we provide a characterization of the ordering, using an approach based on abstract operations on graphs, called lifts, which encode in a combinatorial way the algebraic properties of the chosen template. We finally provide a numerical method to algorithmically check the ordering relation.
Intelligent Distributed Charging Control for Large Scale Electric Vehicles: A Multi-Cluster Mean Field Game Approach

This paper presents an intelligent distributed optimal charging control for large-scale Electric Vehicles (LS-EVs) systems. The rising popularity of electric vehicles in smart cities is putting a strain on the power capacity, leading to high demand for energy and a surge in time-of-use (TOU) prices, which is becoming a pressing issue to address. When solving this issue for LS-EVs, the conventional cooperative centralized system encounters communication and computational complexity challenges. To address these issues, a distributed optimal charging control strategy has been developed. Specifically, the LS-EVs in a smart city has been divided into a finite number of clusters. Then, a mean field game (MFG) theory has been utilized for intra and inter clusters EVs to obtain the distributed optimal charging control. The MFG technique incorporates the state of charge (SOC) of all EVs from a particular cluster into a probability density function(PDF). Then, the PDFs of all clusters are employed to achieve the optimal charging decision for each EV in the city. In addition, a multi-actor-critic-mass (MACM) reinforcement learning algorithm is constructed to learn the optimal solution of the multi-cluster mean field game distributed charging control. Finally, to show the effectiveness of the presented schemes, numerical simulations have been performed.
A WSSL Implementation for Critical Cyber-Physical Systems Applications

The advancements in wireless communication technologies have enabled unprecedented pervasiveness and ubiquity of Cyber-Physical Systems (CPS). Such technologies can now empower true Systems-of-Systems, which cooperate to achieve more complex and efficient functionalities. However, for CPS applications to become a reality, safety and security must be guaranteed, particularly in critical systems, since they rely on open communication systems prone to intentional and non-intentional interferences. We propose designing a Wireless Safety and Security Layer (WSSL) architecture to be implemented in critical CPS applications to address these issues. WSSL increases the reliability of these critical communications by enabling the detection of communication errors. Furthermore, it increases the CPS security using a message signature process that uniquely identifies the sender. So, we present the WSSL architecture and its implementation over an MQTT protocol. We prove that WSSL does not significantly increase the system transmission costs and demonstrate its capability to ensure safety and security, allowing it to be used in any general or critical CPS.