Search

We discuss dark shower signals at the LHC from a dark QCD sector, containing GeV-scale dark pions. The portal with the Standard Model is given by the mixing of the Z boson with a dark Z' coupled to the dark quarks. Both mass and kinetic mixings are included, but the mass mixing is the essential ingredient, as it is the one mediating visible decays of the long-lived dark pions. We focus especially on the possibility that the dark Z' is lighter than the Z. Indirect constraints are dominated by electroweak precision tests, which we thoroughly discuss, showing that both Z-pole and low-energy observables are important. We then recast CMS and LHCb searches for displaced dimuon resonances to dark shower signals initiated by the production of on-shell Z or Z', where the visible signature is left by a dark pion decaying to $\mu^+ \mu^-$. We demonstrate how dark shower topologies have already tested new parameter space in Run 2, reaching better sensitivity on a light dark Z' compared to the flavor-changing decays of B mesons, which can produce a single dark pion at a time, and the electroweak precision tests., Comment: 25 pages + appendices and references, 7 figures; v2: added one sentence and one reference on EWPT, matches published version

In many popular parallel programming models, e.g., OpenMP (OpenMP, 2013), applications are usually dispatched into several dedicated scheduling entities (named "threads " in common) for which the processor time of physical platform is provided through the OS schedulers. This behavior requires for a hierarchical scheduling framework, considering each thread as a virtual processor (VP). Moreover, hierarchical scheduling allow separate applications to execute together on a common hardware platform, with each application having the "illusion " of executing on a dedicated component. However, the problem for scheduling parallel real-time tasks on virtual multiprocessor platform has not been addressed yet. An analogous approach to virtual scheduling for parallel real-time tasks is federeted scheudling, where each task exclusively executes on a set of dedicated physical processors. However, federated scheduling suffers significant resource wasting. In this article, we study the scheduling of real-time parallel task on virtual multiprocessors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings.

In automotive systems, an important timing requirement is that the time disparity (the maximum difference among the timestamps of all raw data produced by sensors that an output originates from) must be bounded in a certain range, so that information from different sensors can be correctly synchronized and fused. In this paper, we study the problem of analyzing the worst-case time disparity in cause-effect chains. In particular, we present two bounds, where the first one assumes all chains are independent from each other and the second one takes the fork-join structures into consideration to perform more precise analysis. Moreover, we propose a solution to cut down the worst-case time disparity for a task by designing buffers with proper sizes. Experiments are conducted to show the correctness and effectiveness of both our analysis and optimization methods.

ROS (Robot Operating System) is one of the most popular robotic software development frameworks. Robotic systems in safety-critical domains are usually subject to hard real-time constraints, so timing behaviors must be formally modeled and analyzed to guarantee that real-time constraints are always honored at run-time. Although a series of analysis techniques has been proposed to analyze the timing performance of ROS 2, the state-of-the-art still generates pessimistic results for ROS 2 systems modeled as DAG (Directed Acyclic Graph). This paper focuses on the analysis of such systems, and proposes techniques to analyze the timing performance in a more precise manner. Experiments with both randomly generated workload and a case study are conducted to evaluate and demonstrate our results.

Anomaly detection on high-dimensional data is crucial for real-world industrial applications. Recent works adopt the Knowledge Distillation (KD) technique to improve the accuracy of anomaly detection Neural Networks (NN). Most KD-based solutions only adopt a single teacher NN and have not yet fully incorporated the distinct advantages of different NN structures. To fill this gap, this paper proposes a novel Multi-teacher Knowledge Distillation approach, which effectively integrates multiple teachers with importance weights to provide guidance for the accurate anomaly detection of students. However, the importance weights are hard to get when training only with normal data. To overcome this challenge, we use an autoencoder-based reconstruction process to update teacher importance weights. In the meantime, the student model parameters are optimized by giving a set of teacher importance weights. Anomalies are then detected based on the deviations between the outputs of teacher and student, as well as the reconstruction errors through the student network. Our proposed approach is evaluated on both CIFAR10 and MVTec datasets. The results show good performance on both high-level semantic anomaly detection and low-level pixel anomaly detection.

Xu Jiang,1 Sungyeun Bae,1 Deok Yong Yoon,2 Shin Jung Park,3 Jaeseong Oh,1 Joo-Youn Cho,1,4 Kyung-Sang Yu1,4 1Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Republic of Korea; 2Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA; 3Department of Pharmaceutical Research Laboratory, Chong Kun Dang Research Institute, Chong Kun Dang Pharmaceutical Corporation, Yongin, Republic of Korea; 4Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of KoreaCorrespondence: Kyung-Sang Yu, Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, 101, Dae-hak ro, Jong-ro gu, Seoul, 03080, Republic of Korea, Tel/Fax +82-2072-1666, Email ksyu@snu.ac.krPurpose: Empagliflozin is a sodium-glucose cotransporter 2 inhibitor that is commonly used for the treatment of type 2 diabetes mellitus. As cocrystal formulation can improve the chemical properties of drugs, CKD-370 was newly developed as a cocrystal formulation of empagliflozin with solvate L-proline. This study aimed to compare the pharmacokinetics, safety, and tolerability of these two empagliflozin formulations in healthy Korean subjects.Methods: A randomized, open-label, two-sequence, two-period crossover study was conducted on healthy Korean participants. The subjects received a single oral 25 mg dose of either test (CKD-370) or reference treatment (Jardiance®) tablet at each period. Plasma empagliflozin concentrations were determined using liquid chromatography with tandem mass spectrometry. Pharmacokinetic (PK) parameters were analyzed using non-compartmental methods. The primary PK parameters included the maximum concentration (Cmax) and the area under the concentration-time curve from 0 to last (AUClast). The safety of both formulations was monitored and evaluated.

We study the real effects of certification to demonstrate the value of mandatory certification over and above mandatory disclosure in enhancing investment efficiency. In our model, a firm's manager selects a project to maximize the firm's short-term stock price, which is a function of her certification and disclosure decisions about the outcome of the project. Although the manager might be either forthcoming or strategic with regard to the disclosure of her private information, she can strategically choose whether to incur a cost or not to certify her disclosure, unless mandated. The manager always selects the first-best project when both certification and disclosure are mandatory. However, when certification is voluntary, project selection is inefficient. In addition, mandating disclosure without mandating certification can lead to lower investment efficiency than mandating neither. In justifying why mandatory certification is beneficial for public firms, our results offer a note of caution regarding the contemplated regulatory moves for increased disclosures by public firms without corresponding certification requirements, for example, the recent SEC proposal requiring extensive climate-related disclosure.

We study the economic impacts of adopting harmonized accounting standards when firms’ investments exhibit beauty contest features. We model harmonized accounting standards as common/correlated noises in firms’ accounting reports. We show that while more harmonized accounting standards have ambiguous effects on the reports’ informativeness in representing firms’ underlying fundamentals, they always reduce their usefulness in forecasting aggregate investments. Therefore, the stronger the beauty contest features, the more important the forecasts about the aggregate investment, thus calling for less harmonized accounting standards. We also find that, absent beauty contest features, mandatory adoption of harmonized accounting standards can be unnecessary; however, such a mandate is warranted when beauty contest features are strong as firms, when left on their own, would not voluntarily do so. Taken together, our results provide both a justification for and identification of an unintended consequence of the recent mandates toward adopting harmonized accounting standards. © 2023 American Accounting Association. All rights reserved.

We analytically study the economic consequences of the disclosure of managerial compensation contracts in a setting where two firms, by designing compensation contracts for their respective managers, compete for a new investment opportunity. Each manager is privately informed about her firm's profitability from this investment. We find that the disclosure leads to firms' emphasizing short-term stock performance in their managers' contracts. This, in turn, induces managers to signal favorable private information via myopic overinvestment, which deters rival firms' investments and gains their own firms a competitive edge. Nonetheless, such strategic use of compensation contracts is absent when the contracts are not disclosed; under this regime, equilibrium contracts only focus on long-term outcomes. Moreover, while disclosure-mandate-induced managerial myopia erodes firm profits, it may benefit consumer and social welfare. Our theory illuminates the economic consequences of the Compensation Discussion and Analysis (CD&A) disclosure implemented in 2006.

Abstract: Interface engineering has been regarded as an effective strategy to manipulate the thermoelectric performance of materials. Here, we use a facile chemical electroless plating and a spark plasma sintering process to fabricate Ag-plated SnTe bulk. After sintering, a small amount of plated Ag can be doped into SnTe to suppress the Sn vacancies and the others form Ag precipitates with a size distribution from nanoscale to microscale, which introduces Ag/SnTe interfaces to enhance the Seebeck coefficient via energy filtering effect. Simultaneously, these structures result in strong scattering to reach a low lattice thermal conductivity of ~ 0.62 W·m–1·K–1. Consequently, a maximum figure of merit (zT) of ~ 0.67 at 823 K is achieved in 2 wt% Ag-plated SnTe, which is ~ 60% higher than that of pristine SnTe. Moreover, the microhardness indentation test results show that the mean microhardness of 2 wt% Ag-plated SnTe is HV 64.26, which is much higher than that of pristine SnTe, indicating that Ag electroless plating can improve the mechanical properties of SnTe. This work has provided a facile and eco-friendly method to realize the interface engineering for manipulating the thermoelectric and mechanical properties of SnTe. Graphical abstract: [Figure not available: see fulltext.]

Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in Ref.~\cite{Hajer:2018kqm}, between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a $t\bar{t}\gamma\gamma$ final state: $t\bar t h$, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, which results in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for both signals are found to be encouraging., Comment: 30 pages, 18 figures

All existing work on real-time scheduling of parallel task graph models with shared resources assumes that a critical section must be contained inside a single vertex. However, this assumption does not hold in many realistic parallel real-time software. In this work, we conduct the first study on real-time scheduling and analysis of parallel task graphs where critical sections are allowed to cross different vertices. We show that allowing this may potentially lead to deadlocks and the so-called resource unrelated blocking time problem. We formalize the conditions for the deadlocks and resource unrelated blocking time to happen, and propose two different solutions to address them and develop corresponding schedulability analysis techniques. We conduct comprehensive experiments to evaluate our method. The results indicate that there is a significant impact to the system schedulability when tasks incur deadlock and resource unrelated blocking. Moreover, the schedulability can benefit from the execution of workload in parallel with critical sections if tasks can be carefully designed so that all deadlocks and resource unrelated blocking time can be avoided, and our methods are efficient to determine the schedulability of systems where critical sections across different vertices exist.

ROS (Robot Operating System) is currently one of the most popular development frameworks for robotic software, which is usually subject to hard real-time constraints in safe-critical domains. Designers must formally model and analyze its timing behaviors to guarantee that real-time constraints are always honored at run-time. This paper studies real-time scheduling and analysis under a multi-threaded executor in ROS 2. We present a formal description of the scheduling model of multi-threaded executors, and develop response time analysis techniques for processing chains executing on it. Moreover, we identify a risk of increasing the response time of chains that may be caused by improper design when deploying systems on multi-threaded executors, which provides a useful guidance to designers. We conduct experiments with both randomly generated workloads and case studies on a realistic ROS 2 platform to evaluate and demonstrate our results.

As one of the hypothetical principles in the Standard Model (SM), lepton flavor universality (LFU) should be tested with a precision as high as possible such that the physics violating this principle can be fully examined. The run of $Z$ factory at a future $e^+e^-$ collider such as CEPC or FCC-$ee$ provides a great opportunity to perform this task because of the large statistics and high reconstruction efficiencies for $b$-hadrons at $Z$ pole. In this paper, we present a systematic study on the LFU test in the future $Z$ factories. The goal is three-fold. Firstly, we study the sensitivities of measuring the LFU-violating observables of $b\to c \tau \nu$, $i.e.$, $R_{J/\psi}$, $R_{D_s}$, $R_{D_s^\ast}$ and $R_{\Lambda_c}$, where $\tau$ decays muonically. For this purpose, we develop the strategies for event reconstruction, based on the track information significantly. Secondly, we explore the sensitivity robustness against detector performance and its potential improvement with the message of event shape or beyond the $b$-hadron decays. A picture is drawn on the variation of analysis sensitivities with the detector tracking resolution and soft photon detectability, and the impact of Fox-Wolfram moments is studied on the measurement of relevant flavor events. Finally, we interpret the projected sensitivities in the SM effective field theory, by combining the LFU tests of $b\to c \tau \nu$ and the measurements of $b\to s \tau^+\tau^-$ and $b\to s \bar{\nu} \nu$. We show that the limits on the LFU-violating energy scale can be pushed up to $\sim \mathcal{O} (10)$~TeV for $\lesssim \mathcal O(1)$ Wilson coefficients at Tera-$Z$., Comment: 63 pages, 27 figures

Locks are the most widely used mechanisms to coordinate simultaneous accesses to exclusive shared resources. While locking protocols and associated schedulability analysis techniques have been extensively studied for sequential real-time tasks, work for parallel tasks largely lags behind. In the limited existing work on this topic, a common assumption is that a critical section must execute sequentially. However, this is not necessarily the case with parallel programming languages. In this paper, we study the analysis of parallel heavy real-time tasks (the density of which is greater than 1) with critical sections in parallel structures. We show that applying existing analysis techniques directly could be unsafe or much pessimistic for the considered model, and develop new techniques to address these problems. Comprehensive experiments are conducted to evaluate the performance of our method.

Deep frying is one of the most popular culinary processes, which induces oils generating a large number of polar compounds (PC) with complex structure, resulting in the difficulty of isolation and identification. In this review, we aimed to discuss the components of PC and then summary highly selective and highly sensitive approaches to analysis PC. It is found that infrared is the most convenient method for qualitative analysis, and chromatography is suitable for quantitative analysis, and electron spin resonance is applicable for mechanism analysis. In the future, sophisticated forecast technologies and comprehensive analytical methods will characterize the development trends in PC analysis, which will be beneficial for a full understanding of deep-frying oil food safety and quality control.

As an ornamental flower crop, the long-term continuous monocropping of cut chrysanthemum causes frequent occurrence of diseases, seriously affecting the quality of cut chrysanthemum. The rhizosphere microbial community plays an important role in maintaining the healthy growth of plants, whereas the composition and dynamics of rhizosphere microbial community under continuous monocropping of cut chrysanthemum have not been fully revealed. In this study, the Illumina MiSeq high-throughput sequencing platform was used to monitor the dynamic changes of rhizosphere microbial communities in four varieties of cut chrysanthemum during 0-3 years of monocropping, and the soil physicochemical properties were also determined. Results showed that continuous monocropping significantly increased the fungal community richness and altered the profiles of the bacterial and fungal communities, leading to variation of community beta-diversity. With the increase of continuous cropping time, biocontrol bacteria decreased, while some plant pathogenic fungi were enriched in the rhizosphere of cut chrysanthemum. FAPROTAX-based functional prediction showed that the abundance of gene related to nitrogen and sulfur metabolism and chitin lysis was reduced in the rhizosphere of cut chrysanthemum. FUNGuild-based fungal function prediction showed that plant pathogenic fungal taxa were increasing in the rhizosphere of cut chrysanthemum, mainly Acremonium, Plectosphaerellaceae, Fusarium, and Cladosporium. Continuous cropping also reduced the content of ammonium nitrogen and increased soil salinity, resulting in deterioration of soil physical and chemical properties, which, together with the transformation of rhizosphere microbial community, became part of the reasons for the continuous cropping obstacle of cut chrysanthemum.

In this study, the Cutaneotrichosporon dermatis strain M503 was isolated and could efficiently degrade tetracycline, doxycycline, and chlorotetracyline. The characteristics of tetracycline degradation were investigated under a broad range of cultural conditions. Response surface methodology (RSM) predicted that the highest degradation rate of tetracycline could be obtained under the following conditions: 39.69 °C, pH of 8.79, and inoculum dose of 4.0% (v/v, ~3.5 × 106 cells/mL in the medium). In accordance with the five identified degradation products of tetracycline, two putative degradation pathways, which included the shedding of methyl and amino groups, were proposed. Moreover, the well diffusion method showed that the strain of M503 decreases the antibacterial potency of tetracycline, doxycycline, and chlorotetracycline. These findings proposed a putative mechanism of tetracycline degradation by a fungus strain and contributed to the estimation of the fate of tetracycline in the aquatic environment.

Economists broadly agree that the economic burden of corporate taxes is not entirely borne by shareholders but also borne in part by employees and consumers. We examine corporate tax avoidance in a setting where shareholders do not bear the entire economic burden of the corporate tax. We show that the relation between corporate tax incidence and corporate tax avoidance depends on the elasticity of labor supply, the productivity of capital relative to labor, and the tax deductibility of labor and capital. These forces operate through two channels (firm scale and input mix), making the actual association between tax avoidance and incidence an empirical question. We find that firms whose shareholders bear less of the economic burden of corporate taxes engage in less avoidance. Our findings suggest that maximizing after-tax profits might entail less tax avoidance if shareholders do not entirely bear the corporate tax burden. In particular, when the incidence of the corporate tax falls on the firm, firms avoid more taxes. This tendency is stronger if firms use a higher level of capital input, if the deductibility of the cost of capital investment is limited, if firms have high capital productivity, or if tax enforcement is strong.

In 1969, Graham developed a well-known response time bound for a DAG task using the total workload and the longest path of the DAG, which has been widely applied to solve many scheduling and analysis problems of DAG-based task systems. This paper presents a new response time bound for a DAG task using the total workload and the lengths of multiple long paths of the DAG, instead of the longest path in Graham's bound. Our new bound theoretically dominates and empirically outperforms Graham's bound. We further extend the proposed approach to multi-DAG task systems. Our schedulability test theoretically dominates federated scheduling and outperforms the state-of-the-art by a considerable margin.

Xu Jiang,1,* Yang Pan,1,* Sha Zhu,1 Yaxi Wang,1 Ruxin Gu,1 Yinyin Jiang,1 Bo Shen,1 Jun Zhu,1 Shulan Xu,1 Jun Yan,1 Jingde Dong,1 Wenbin Zhang,2 Chaoyong Xiao,3 Li Zhang1 1Department of Geriatrics, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Radiology, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li Zhang, Department of Geriatrics, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, People’s Republic of China, Email jx13773958367@163.comObjective: Patients with rapid eye movement (REM) sleep behavior disorder (RBD) in Parkinson’s disease (PD-RBD) tend to have poor cognitive performance and faster cognitive deterioration, and the potential mechanism is still ambiguous. Therefore, this study aimed to detect the alterations in local brain function in PD-RBD.Methods: Fifty patients, including 23 patients with PD-RBD and 27 patients with PD without RBD (PD-nRBD), and 26 healthy controls were enrolled. All subjects were subjected to one-night polysomnography and underwent resting-state functional magnetic resonance imaging (rs-fMRI). The fMRI images of the three groups were analyzed by regional homogeneity (ReHo) to observe the local neural activity. Correlations between altered ReHo values and chin electromyographic (EMG) density scores and cognitive scores in the PD subgroups were assessed.Results: Compared with the patients with PD-nRBD, the patients with PD-RBD had higher ReHo values in the frontal cortex (the right superior frontal gyrus, the right middle frontal gyrus and the left medial superior frontal gyrus), the right caudate nucleus and the right anterior cingulate gyrus, and compared with

Understanding the wheel-terrain interaction is of great importance to improve the maneuverability and traversability of the rovers. A well-developed sensing device carried by the rover would greatly facilitate the complex risk-reducing operations on sandy terrains. In this paper, an instrumented wheel-on-limb (WOL) system of planetary rovers for wheel-terrain interaction characterization is presented. Assuming the function of a passive suspension of the wheel, the WOL system allows itself to follow the terrain contour, and keep the wheel remain lowered onto the ground during rover motion including climbing and descending, as well as deploy and place the wheel on the ground before a drive commanding. The system concept, functional requirements, and pre-design work, as well as the system integration are presented., Comment: 2nd International Conference on Robotics and Control Engineering, ACM RobCE 2022, March 25, 2022, Nanjing, China

Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.

Sha Zhu, Yaxi Wang, Yinyin Jiang, Ruxin Gu, Min Zhong, Xu Jiang, Bo Shen, Jun Zhu, Jun Yan, Yang Pan, Li Zhang Department of Geriatric Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of ChinaCorrespondence: Li Zhang, Department of Geriatric Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, Jiangsu Province, 210029, People’s Republic of China, Email neuro_zhangli@163.comBackground: Transcranial ultrasound (TCS) can be used to reveal structural changes in the substantia nigra (SN) and is a potential tool for the early diagnosis of Parkinson’s disease (PD). This study aimed to explore the relationship between substantia nigra hyperechogenicity (SNH) and the clinical features of PD patients.Methods: A total of 96 PD patients were included in our study. All patients were detected by TCS and divided into two groups: PD patients with SNH (PDSN+) and those with normal SN echogenicity (PDSN-). The Unified Parkinson’s Disease Rating Scale (UPDRS) and the Hoehn & Yahr stage were used to assess the extent of disease-related disability of the PD patients. Non-motor symptoms were evaluated by using several scales. The instrumented stand and walk test was performed on all subjects, and gait data were gathered using a JiBuEn gait analysis system.Results: Seventy-five PD patients were successfully assessed by TCS. We found that SNH was associated with a higher UPDRS II scores (p = 0.028). In addition, compared with PDSN- group, the PDSN+ group exhibited more severe gait impairment, including increased variability in stride length (p = 0.042), decreased heel strike angle (p = 0.017), decreased range of motion of hip joints (p = 0.031), and a more asymmetrical walking pattern (p = 0.028).Conclusion: Our study demonstrated that SNH significantly correlated with activities of daily living and gait impairment in Chinese patients with PD, suggesting the formation of SNH mi

Interface engineering has been regarded as an effective strategy to improve thermoelectric (TE) performance by modulating electrical transport and enhancing phonon scattering. Herein, we develop a new interface engineering strategy in SnTe-based TE materials. We first use a one-step solvothermal method to synthesize SnTe powders decorated by Sb2Te3 nanoplates. After subsequent spark plasma sintering, we found that an ion-exchange reaction between the Sb2Te3 and SnTe matrixes happens to result in Sb doping and the formation of SnSb nanoparticles and the recrystallization of the nanograined SnTe at the grain boundaries of the SnTe matrix. Benefitting from this unique engineering, a significantly reduced lattice thermal conductivity of ∼0.64 W m-1 K-1 and a high zT of ∼1.08 (∼100% enhanced) at 873 K are achieved in SnTe-Sb0.06. Such improved TE properties are attributed to the optimized carrier concentration and valence band convergence due to the Sb doping and enhanced phonon scattering by interface engineering at the grain boundaries. This work has demonstrated a facile and effective method to realize high-TE-performance SnTe via interface engineering.

As a promising alternative to the toxic PbTe-based thermoelectric materials, eco-friendly SnTe has attracted considerable attention. Here, we use a facile solvothermal method to in-situ synthesize MXene/SnTe nanocomposites. Comprehensive characterization results indicate that the incorporation of two-dimensional MXene in SnTe matrix can suppress Sn vacancies to result in a reduced carrier concentration and induce abundant MXene/SnTe interfaces, which simultaneously optimizes the electrical and thermal transport properties. As a result, with the optimized MXene content, the maximum figure of merit of ∼0.63 ​at 823 ​K is obtained in SnTe with 0.6 ​wt% MXene, which shows a 60% enhancement compared to pristine SnTe. This work explores a new strategy to introduce two-dimensional MXene into SnTe-based materials with improved thermoelectric properties, which can inspire new pathways into designing and synthesizing high-performance thermoelectric materials.

Locking protocol is a crucial component in scheduling of real-time systems. The digraph real-time task model (DRT) is the state-of-the-art graph-based task model, which is a generalization of most previous real-time task models. To our best knowledge, the only work addressing resource sharing problem in DRT task model proposes a resource sharing protocol, called ACP, as well as a scheduling strategy EDF+ACP. Although EDF+ACP is optimal for scheduling DRT tasks with non-nested resource access, it cannot handle the situation of nested resource accesses. In this paper, we propose a new protocol, called N-ACP, by modifying ACP to manage nested resource accesses in task graph models. We apply N-ACP to EDF scheduling to obtain a new scheduling strategy EDF+N-ACP. We develop schedulability analysis techniques for EDF+N-ACP and evaluate its performance by a widely-used quantitative metrics speedup factor. We derive its speedup factor as a function of the maximal nesting level of resource accesses in the system.

OpenMP is a popular programming framework in both general and high-performance computing and has recently drawn much interest in embedded and real-time computing. Although the execution semantics of OpenMP are similar to the DAG task model, the constraints posed by the OpenMP specification make them significantly more challenging to analyze. A tied task is an important feature in OpenMP that must execute on the same thread throughout its entire life cycle. A previous work [1] succeeded in analyzing the real-time scheduling of tied tasks by modifying the Task Scheduling Constraints (TSCs) in OpenMP specification. In this article, we also study the real-time scheduling of OpenMP task systems with tied tasks but without changing the original TSCs. In particular, we propose a partitioning-based algorithm, P-EDF-omp, by which the tied constraint can be automatically guaranteed as long as an OpenMP task system can be successfully partitioned to a multiprocessor platform. Furthermore, we conduct comprehensive experiments with both synthetic workloads and established OpenMP benchmarks to show that our approach consistently outperforms the work in [1] -even without modifying the TSCs.

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. Although the design and analysis of locking protocols have been intensively studied for sequential real-time tasks, there has been a little work on this topic for parallel real-time tasks. In this article, we study the analysis of parallel real-time tasks using spin locks to protect accesses to shared resources in three commonly used request serving orders (unordered, FIFO-order, and priority-order). A remarkable feature making our analysis method more accurate is to systematically analyze the blocking time which may delay a task's finishing time, where the impact to the total workload and the longest path length is jointly considered, rather than analyzing them separately and counting all blocking time as the workload that delays a task's finishing time, as commonly assumed in the state-of-the-art.

We fine-tune GPT-3 to answer long-form questions using a text-based web-browsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit., Comment: 32 pages

Min Zhong,1 Xu Jiang,1 Sha Zhu,1 Ruxin Gu,1 Yu Bai,2 Hong He,3 Yang Pan,1 Pingyi Xu,4 Jun Yan,1 Li Zhang1 1Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada; 3School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People’s Republic of China; 4Department of Neurology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of ChinaCorrespondence: Li Zhang; Jun YanDepartment of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, Jiangsu Province, 210029, People’s Republic of ChinaEmail neuro_zhangli@163.com; 815473709@qq.comPurpose: Sleep disturbance is one of the common symptoms in Parkinson’s disease (PD). The study of sleep disturbance used to concentrate on treated PD. This study aimed to investigate the factors that are associated with the sleep quality of drug-naïve patients with PD.Patients and Methods: All participants were interviewed using a standard questionnaire to collect basic information. PD severity, depression symptoms, anxiety symptoms, sleep quality, cognitive status, life quality, and the presence of rapid eye movement (REM) sleep behavior disorder (RBD) and minor hallucination were assessed using corresponding rating scales. The patients with a Pittsburgh Sleep Quality Index score ≤ 6 fell into the poor sleep group, and those with REM Sleep Behavior Disorder Screening Questionnaire score ≥ 5 were considered to have probable RBD.Results: Seventy drug-naive patients with PD and 30 healthy controls matched for age, sex, and education were recruited. Up to 41.4% of the patients suffered from sleep disturbance, and 24.3% of the patients had RBD. Poor sleepers were more likely to have left-side predominant motor symptoms. Comp

In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning. Although some graph-based methods have achieved satisfactory performance, they ignore the underlying data structure across different views. Besides, their pre-defined laplacian graphs are sensitive to the noises in the original data space, and fail to get the optimal neighbor assignment. To address the above problems, we propose a novel unsupervised multi-view feature selection model based on graph learning, and the contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Therefore, the proposed model can reveal the data relationship from the feature subset. (2) a reasonable rank constraint is added to optimize the similarity matrix to obtain more accurate information; (3) an auto-weighted framework is presented to assign view weights adaptively, and an effective alternative iterative algorithm is proposed to optimize the problem. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.

Zhuang Wu,1,* Hang Xu,2,* Sha Zhu,1 Ruxin Gu,1 Min Zhong,1 Xu Jiang,1 Bo Shen,1 Jun Zhu,1 Yang Pan,1 Jingde Dong,1 Jun Yan,1 Wenbin Zhang,3 Li Zhang1 1Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Neurology, The Yancheng Clinical College of Xuzhou Medical University, Yancheng, People’s Republic of China; 3Department of Neurosurgery, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li Zhang; Jun Yan Email neuro_zhangli@163.com; 2519002414@qq.comBackground and Purpose: Gait analysis and the effects of levodopa on the gait characteristics in Mild parkinsonian signs (MPS) are rarely published. The present research aimed to (1) analyze the gait characteristics in MPS; (2) explore the effects of levodopa on the gait performance of MPS.Methods: We enrolled 22 inpatients with MPS and 20 healthy control subjects (HC) from Nanjing Brain Hospital. The Unified Parkinson’s Disease Rating Scale was used to evaluate motor symptoms. Acute levodopa challenge test was performed to explore the effects of levodopa on the gait performance of MPS. The instrumented stand and walk test was conducted for each participant and the JiBuEn gait analysis system was used to collect gait data.Results: For spatiotemporal parameters: Compared with HC, the state before taking levodopa/benserazide in MPS group (meds-off) demonstrated a decrease in stride length (SL) (p≤ 0.001), an increase in SL variability (p≤ 0.001), and swing phase time variability (p=0.016). Compared with meds-off, the state after 1 hour of taking levodopa/benserazide in MPS group (meds-on) exhibited an increase in SL (p≤ 0.001), a decrease in SL variability (p≤ 0.001). For kinematic parameters: Compared with HC, meds-off demonstrated a decrease in heel strike ang

Malachite green is a common environmental pollutant that poses a great threat to non-target organisms, including humans. This study reports the characterization of a bacterial strain, Pseudomonas veronii JW3-6, which was isolated from a malachite green enrichment culture. This strain degraded malachite green efficiently in a wide range of temperature and pH levels. Under optimal degradation conditions (32.4 °C, pH 7.1, and inoculum amount of 2.5 × 107 cfu/mL), P. veronii JW3-6 could degrade 93.5% of 50 mg/L malachite green within seven days. Five intermediate products from the degradation of malachite green were identified: leucomalachite green, 4-(dimethylamino) benzophenone, 4-dimethylaminophenol, benzaldehyde, and hydroquinone. We propose a possible degradation pathway based on these findings. The present study is the first to report the degradation of malachite green by P. veronii and the identification of hydroquinone as a metabolite in the degradation pathway.

ROS (Robot Operating System) is currently the most popular robotic software development framework. Robotic software in safe-critical domain are usually subject to hard realtime constraints, so designers must formally model and analyze their timing behaviors to guarantee that real-time constraints are always honored at runtime. This paper studies real-time scheduling and analysis of processing chains in ROS2, the second-generation ROS with a major consideration of real-time capability. First, we study response time analysis of processing chains on ROS2 executors. We show that the only existing result of this problem is both optimistic and pessimistic, and develop new techniques to address these problems and significantly improve the analysis precision. Second, we reveal that the response time of a processing chain on an executor only depends on its last scheduling entity (callback), which provides useful guidance for designers to improve not only the response time bound, but also the actual worst-case/average response time of the system at little design cost. We conduct experiments with both randomly generated workload and a case study on realistic ROS2 platforms to evaluate and demonstrate our results.

Real-time scheduling and locking protocols are fundamental facilities to construct time-critical systems. For parallel real-time tasks, predictable locking protocols are required when concurrent sub-jobs mutually exclusive access to shared resources. This paper for the first time studies the distributed synchronization framework of parallel real-time tasks, where both tasks and global resources are partitioned to designated processors, and requests to each global resource are conducted on the processor on which the resource is partitioned. We extend the Distributed Priority Ceiling Protocol (DPCP) for parallel tasks under federated scheduling, with which we proved that a request can be blocked by at most one lower-priority request. We develop task and resource partitioning heuristics and propose analysis techniques to safely bound the task response times. Numerical evaluation (with heavy tasks on 8-, 16-, and 32-core processors) indicates that the proposed methods improve the schedulability significantly compared to the state-of-the-art locking protocols under federated scheduling.

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. Although the design and analysis of locking protocols have been intensively studied for sequential real-time tasks, there has been little work on this topic for parallel real-time tasks. In this paper, we study the analysis of parallel real-time tasks using spin locks to protect accesses to shared resources in three commonly used request serving orders (unordered, FIFO-order and priority-order). A remarkable feature making our analysis method more accurate is to systematically analyze the blocking time which may delay a task's finishing time, where the impact to the total workload and the longest path length is jointly considered, rather than analyzing them separately and counting all blocking time as the workload that delays a task's finishing time, as commonly assumed in the state-of-the-art.

Schedulability bounds not only serve as efficient tests to decide schedulability of real-time task systems but also reveal insights about the worst-case performance of scheduling algorithms. Different from sequential real-time task systems for which utilization is a suitable metric to develop schedulability bounds, schedulability of parallel real-time tasks depends on not only utilization but also the workload graph structure of tasks, which can be well represented by the tensity metric. In this paper, we develop new analysis techniques for parallel real-time task systems under Global Rate-Monotonic (G-RM) scheduling and obtain new results on schedulability bounds based on these two metrics: utilization and tensity. First, we develop the first utilization-tensity bound for G-RM. Second, we improve the capacity augmentation bound of G-RM from the best known value 3.73 to 3.18. These schedulability bounds not only provide theoretical insights about real-time performance of G-RM, but also serve as highly efficient schedulability tests, which are particularly suitable to design scenarios in which detailed task graph structures are unknown or may change at run-time. Experiments with randomly generated task sets show that our new results consistently outperform the state-of-the-art with a significant margin under different parameter settings., Comment: 11 pages

The Teacher-Student (T-S) framework is widely utilized in the classification tasks, through which the performance of one neural network (the student) can be improved by transferring knowledge from another trained neural network (the teacher). Since the transferring knowledge is related to the network capacities and structures between the teacher and the student, how to define efficient knowledge remains an open question. To address this issue, we design a novel transferring knowledge, the Self-Attention based Inter-Class Correlation (ICC) map in the output layer, and propose our T-S framework, Inter-Class Correlation Transfer (ICCT)., Comment: 10 pages, 5 figures

This paper probes into the issue of short network lifetime caused by unbalanced routing energy consumption of wireless sensor network, and discovers that the reason for the problem is unbalanced network load. To tackle the issue, the author constructs a load-balanced vector field by the non-viscous fluid model in fluid mechanics, and optimizes the vector field-based energy routing by artificial fish-swarm algorithm (AFSA). On this basis, the author builds up an AFSA-inspired vector field-based energy routing algorithm based on fluid mechanics. Besides, the author conducts simulation analysis of the algorithm and common routing algorithms. Through comparison, it is discovered that the routing algorithm proposed by this paper has higher energy efficiency than the traditional routing algorithms, which prolongs the lifetime of the wireless sensor network.

Zhuang Wu,1 Min Zhong,1 Xu Jiang,1 Bo Shen,1 Jun Zhu,1 Yang Pan,1 Jingde Dong,1 Jun Yan,1 Pingyi Xu,2 Wenbin Zhang,3 Yang Gao,4 Li Zhang1 1Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Neurology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 3Department of Neurosurgery, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 4Department of Computer Science and Technology, Nanjing University, Nanjing, People’s Republic of ChinaCorrespondence: Li Zhang Email neuro_zhangli@163.comPurpose: Gait impairment is a common clinical symptom of patients with Parkinson’s disease (PD). Detecting specific gait parameters’ changes in order to guide clinical intervention is at present lacking. The present study aimed to (1) quantify gait impairments in different PD subtypes and (2) explore whether the results of quantitative gait analysis are beneficial to clinical treatment.Patients and Methods: We enrolled 86 patients with PD (48 men, and 38 women) from the Department of Geriatrics of the Affiliated Brain Hospital of Nanjing Medical University. Unified Parkinson’s Disease Rating Scale (UPDRS) and Hoehn-Yahr Scale were used to evaluate the motor symptoms of PD. All patients stopped anti-Parkinsonian medication for 24 hours (72 hours for controlled release medicine). The patients were divided into two subtypes, namely, postural instability gait difficulty (PIGD; n=56) and tremor dominant (TD; n=30) subtypes according to UPDRS. All patients completed the instrumented stand and walk test, and a set of JiBuEn gait analysis system was used in gait data collection.Results: We observed a shorter stride length (p=0.021), a longer stride time (p=0.036), a faster cadence (p=0.036), and a more variable stride length (p=0.012) in the PIGD g

In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, \releta is able to effectively reduce the system peak temperature without compromising the application performance. We implement and evaluate \releta on a real platform in comparison with the state-of-the-art approaches. Experimental results show \releta can reduce the average peak temperature by 4 $^{\circ}$C and the maximum difference is up to 13 $^{\circ}$C.

Capacity augmentation bound is a widely used quantitative metric in theoretical studies of schedulability analysis for directed acyclic graph (DAG) parallel real-time tasks, which not only quantifies the suboptimality of the scheduling algorithms, but also serves as a simple linear-time schedulability test. Earlier studies on capacity augmentation bounds of the sporadic DAG task model were either restricted to a single DAG task or a set of tasks with implicit deadlines. In this paper, we consider parallel tasks with constrained deadlines under global earliest deadline first policy. We first show that it is impossible to obtain a constant bound for our problem setting, and derive both lower and upper bounds of the capacity augmentation bound as a function with respect to the maximum ratio of task period to deadline. Our upper bound is at most 1.47 times larger than the optimal one. We conduct experiments to compare the acceptance ratio of our capacity augmentation bound with the existing schedulability test also having linear-time complexity. The results show that our capacity augmentation bound significantly outperforms the existing linear-time schedulability test under different parameter settings.

Federated scheduling is a promising approach to schedule parallel real-time tasks on multi-cores, where each heavy task exclusively executes on a number of dedicated processors, while light tasks are treated as sequential sporadic tasks and share the remaining processors. However, federated scheduling suffers resource waste since a heavy task with processing capacity requirement x + is an element of (where x is an integer and 0 < is an element of < 1) needs x+ 1 dedicated processors. In the extreme case, almost half of the processing capacity is wasted. In this paper we propose the semi-federate scheduling approach, which only grants x dedicated processors to a heavy task with processing capacity requirement x+ is an element of, and schedules the remaining is an element of part together with light tasks on shared processors. Experiments with randomly generated task sets show the semi-federated scheduling approach significantly outperforms not only federated scheduling, but also all existing approaches for scheduling parallel real-time tasks on multi-cores.

Longitudinal relaxation (T1) contrast in MRI is important for studying brain morphology and is widely used in clinical applications. Although MRI only detects signals from water hydrogen (1H) protons (WPs), T1 contrast is known to be influenced by other species of 1H protons, including those in macromolecules (MPs), such as lipids and proteins, through magnetization transfer (MT) between WPs and MPs. This complicates the use and quantification of T1 contrast for studying the underlying tissue composition and the physiology of the brain. MT contributes to T1 contrast to an extent that is generally dependent on MT kinetics, as well as the concentration and NMR spectral properties of MPs. However, the MP spectral properties and MT kinetics are both difficult to measure directly, as the signal from MPs is generally invisible to MRI. Therefore, to investigate MT kinetics and further quantify T1 contrast, we first developed a reliable way to indirectly measure the MP fraction and their exchange rate with WPs, with minimal dependence on the spectral properties of MPs. For this purpose, we used brief, high-power radiofrequency (RF) NMR excitation pulses to almost completely saturate the magnetization of MPs. Based on this, both MT kinetics and the contribution of MPs to T1 contrast through MT were studied. The thus obtained knowledge allowed us to subsequently infer the spectral properties of MPs by applying low-power, frequency-selective off-resonance RF pulses and measuring the offset-frequency dependent effect of MPs on the WP MRI signal. A two-pool exchange model was used in both cases to account for direct effects of the RF pulse on WP magnetization. Consistent with earlier works using MRI at low-field and post-mortem analysis of brain tissue, our novel measurement approach found that MPs constitute an up to 27% fraction of the total 1

We describe two experiments demonstrating that a cylindrical cloak formerly introduced for linear surface liquid waves works equally well for sound and electromagnetic waves. This structured cloak behaves like an acoustic cloak with an effective anisotropic density and an electromagnetic cloak with an effective anisotropic permittivity, respectively. Measured forward scattering for pressure and magnetic fields are in good agreement and provide first evidence of broadband cloaking. Microwave experiments and 3D electromagnetic wave simulations further confirm reduced forward and backscattering when a rectangular metallic obstacle is surrounded by the structured cloak for cloaking frequencies between 2.6 and 7.0 GHz. This suggests, as supported by 2D finite element simulations, sound waves are cloaked between 3 and 8 KHz and linear surface liquid waves between 5 and 16 Hz. Moreover, microwave experiments show the field is reduced by 10 to 30 dB inside the invisibility region, which suggests the multi-wave cloak could be used as a protection against water, sonic or microwaves.

We introduce a general framework in performing transformation optics by purely rotating the optical axis of the same anisotropic dielectric material on the subwavelength scale. The transformation medium realizes any area-preserving maps with maximum anisotropy only limited by the original material. By applying different optical-axis profiles on the same medium, a wave expander and a virtual shifter are constructed as examples. The investigations are potentially useful for designing transformation optical devices using only one type of material. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.

Managerial ties, the personal networks of senior managers, have been found to be facilitators of firm performance because of their network benefits. However, social network theory suggests that managerial ties only play a "conduit" role by providing possibilities and opportunities to approach external resources. How can firms turn these possibilities and opportunities into internal knowledge assets and further transform them into firm innovation? Extant research constructs a direct mechanism for the managerial ties-firm innovation link. The research reported here, however, provides and investigates an indirect ties-innovation argument where organizational knowledge creation processes, including knowledge exchange and knowledge combination, are mediators. And managerial ties are examined through two traditional dimensions, business ties and political ties. This study employs empirical data from 270 firms in China and uses structural equation modeling techniques to reveal interesting findings. First, the results support the key argument that the influence of managerial ties on firm innovation is indirect. Second, knowledge exchange and knowledge combination are different constructs and the former positively influences the latter. More interestingly, business ties can exert a significant direct impact on both knowledge exchange and knowledge combination, while political ties can only influence knowledge exchange directly. Although both knowledge exchange and knowledge combination impact product innovation directly, only knowledge combination can directly influence process innovation. These findings indicate that the role of political ties is declining, but business ties still have substantial influence on firm innovation in transitional China. Different processes of organizational knowledge creation, such as knowledge exchange and knowledge combination, make distinct contributions to firm innovation. Product innovation, as opposed to process innovation, is more externa