Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterated $Q$-Network: Beyond the One-Step Bellman Operator

Value-based Reinforcement Learning (RL) methods rely on the application of the Bellman operator, which needs to be approximated from samples. Most approaches consist of an iterative scheme alternating the application of the Bellman operator and a subsequent projection step onto a considered function space. However, we observe that these algorithms can be improved by considering multiple iterations of the Bellman operator at once. Thus, we introduce iterated $Q$-Networks (iQN), a novel approach that learns a sequence of $Q$-function approximations where each $Q$-function serves as the target for the next one in a chain of consecutive Bellman iterations. We demonstrate that iQN is theoretically sound and show how it can be seamlessly used in value-based and actor-critic methods. We empirically demonstrate its advantages on Atari $2600$ games and in continuous-control MuJoCo environments.
Inverted-circuit zero-noise extrapolation for quantum gate error mitigation

A common approach to deal with gate errors in modern quantum-computing hardware is zero-noise extrapolation. By artificially amplifying errors and extrapolating the expectation values obtained with different error strengths towards the zero-error (zero-noise) limit, the technique aims at rectifying errors in noisy quantum computing systems. For an accurate extrapolation, it is essential to know the exact factors of the noise amplification. In this article, we propose a simple method for estimating the strength of errors occurring in a quantum circuit and demonstrate improved extrapolation results. The method determines the error strength for a circuit by appending to it the inverted circuit and measuring the probability of the initial state. The estimation of error strengths is easy to implement for arbitrary circuits and does not require a previous characterisation of noise properties. We compare this method with the conventional zero-noise extrapolation method and show that the novel method leads to a more accurate calculation of expectation values. Our method proves to be particularly effective on current hardware, showcasing its suitability for near-term quantum computing applications.
Learning to Ask Critical Questions for Assisting Product Search

Product search plays an essential role in eCommerce. It was treated as a special type of information retrieval problem. Most existing works make use of historical data to improve the search performance, which do not take the opportunity to ask for user's current interest directly. Some session-aware methods take the user's clicks within the session as implicit feedback, but it is still just a guess on user's preference. To address this problem, recent conversational or question-based search models interact with users directly for understanding the user's interest explicitly. However, most users do not have a clear picture on what to buy at the initial stage. Asking critical attributes that the user is looking for after they explored for a while should be a more efficient way to help them searching for the target items. In this paper, we propose a dual-learning model that hybrids the best from both implicit session feedback and proactively clarifying with users on the most critical questions. We first establish a novel utility score to measure whether a clicked item provides useful information for finding the target. Then we develop the dual Selection Net and Ranking Net for choosing the critical questions and ranking the items. It innovatively links traditional click-stream data and text-based questions together. To verify our proposal, we did extensive experiments on a public dataset, and our model largely outperformed other state-of-the-art methods.
Agent-based Modeling and Simulation of Human Muscle For Development of Human Gait Analyzer Application

Despite the fact that only a small portion of muscles are affected in motion disease and disorders, medical therapies do not distinguish between healthy and unhealthy muscles. In this paper, a method is devised in order to calculate the neural stimuli of the lower body during gait cycle and check if any group of muscles are not acting properly. For this reason, an agent-based model of human muscle is proposed. The agent is able to convert neural stimuli to force generated by the muscle and vice versa. It can be used in many researches including medical education and research and prosthesis development. Then, Boots algorithm is designed based on a biomechanical model of human lower body to do a reverse dynamics of human motion by computing the forces generated by each muscle group. Using the agent-driven model of human muscle and boots algorithm, a user-friendly application is developed which can calculate the number of neural stimuli received by each muscle during gait cycle. The application can be used by clinical experts to distinguish between healthy and unhealthy muscles.
Longitudinal Analysis Over Decades Reveals the Development and Immune Implications of Type I Interferon Autoantibodies in an Aging Population

Pre-existing autoantibodies (autoAbs) neutralizing type I interferons (IFN-Is: IFN, IFN{beta}, IFN{omega}) have recently been described as significant contributors to the severity of viral infectious diseases. Here, we explore the development and consequences of anti-IFN-I autoAbs at high-resolution using retrospective samples and data from 1876 well-treated individuals >65 years of age enrolled in the Swiss HIV Cohort Study, a nationwide, longitudinal cohort with up to 35 years of follow-up. Approximately 1.9% of individuals developed anti-IFN-I autoAbs, with a median onset age of ~63 years (range 45-80). Once developed, anti-IFN-I autoAbs persisted for life, and generally increased in titer over years. Most individuals developed distinct neutralizing and non-neutralizing anti-IFN-I autoAb repertoires at discrete times that selectively targeted various combinations of IFN, IFN{beta}, and IFN{omega}. Longitudinal analyses further revealed that emergence of neutralizing anti-IFN autoAbs correlated with reduced IFN-stimulated gene (ISG) levels, indicating impairment of innate immunity. Patient data review suggested that prior recorded viral infections and autoimmune history influence the likelihood of mounting anti-IFN-I autoAbs. Indeed, systematic measurements in biobanked samples revealed significant enrichment of pre-existing autoreactivity against clinically relevant autoantigens in individuals who later developed anti-IFN-I autoAbs. In this context, we describe lifelong neutralizing anti-IFN autoAbs (and impaired innate immunity), that manifested in an individual following IFN therapy, and who was retrospectively found to have had pre-existing autoreactivity to {beta}2-glycoprotein-I before IFN treatment. Our decades-spanning longitudinal analyses illuminate the development and immune implications of anti-IFN-I autoAbs in an aging population, and support a 'two-hit' hypothesis whereby loss of self-tolerance prior to immune-triggering with endogenous or exogenous IFN-I may pose a risk for developing late-onset, lifelong IFN-I functional deficiency.
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses

I study adversarial attacks against stochastic bandit algorithms. At each round, the learner chooses an arm, and a stochastic reward is generated. The adversary strategically adds corruption to the reward, and the learner is only able to observe the corrupted reward at each round. Two sets of results are presented in this paper. The first set studies the optimal attack strategies for the adversary. The adversary has a target arm he wishes to promote, and his goal is to manipulate the learner into choosing this target arm $T - o(T)$ times. I design attack strategies against UCB and Thompson Sampling that only spend $\widehat{O}(\sqrt{\log T})$ cost. Matching lower bounds are presented, and the vulnerability of UCB, Thompson sampling, and $\varepsilon$-greedy are exactly characterized. The second set studies how the learner can defend against the adversary. Inspired by literature on smoothed analysis and behavioral economics, I present two simple algorithms that achieve a competitive ratio arbitrarily close to 1.
Manipulating the wavelength of single photons in insulating van der Waals heterostructures: theory and application to bilayer hexagonal boron nitride

Van der Waals heterostructures are promising for adding new functionalities to two-dimensional materials. In this study, we focus on single photon emitters hosted in one layer and adjacent to other insulating two-dimensional material. Specifically, we show how the emission energy is modified by such a heterostructure. We developed a general approach to elucidate the mechanisms affecting the emission energy and studied the particular case of carbon substitutions in the hexagonal boron nitride bilayer.
Generative Software Engineering

The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which generative tasks are the most concern by researchers, where pre-trained models and LLMs possess powerful language representation and contextual awareness capabilities, enabling them to leverage diverse training data and adapt to generative tasks through fine-tuning, transfer learning, and prompt engineering. These advantages make them effective tools in generative tasks and have demonstrated excellent performance. In this paper, we present a comprehensive literature review of generative tasks in SE using pre-trained models and LLMs. We accurately categorize SE generative tasks based on software engineering methodologies and summarize the advanced pre-trained models and LLMs involved, as well as the datasets and evaluation metrics used. Additionally, we identify key strengths, weaknesses, and gaps in existing approaches, and propose potential research directions. This review aims to provide researchers and practitioners with an in-depth analysis and guidance on the application of pre-trained models and LLMs in generative tasks within SE.
Call admission control in mobile cellular networks: a comprehensive survey

Call admission control (CAC) is a key element in the provision of guaranteed quality of service (QoS) in wireless networks. The design of CAC algorithms for mobile cellular networks is especially challenging given the limited and highly variable resources, and the mobility of users encountered in such networks. This article provides a survey of admission control schemes for cellular networks and the research in this area. Our goal is to provide a broad classification and thorough discussion of existing CAC schemes. We classify these schemes based on factors such as deterministic/stochastic guarantees, distributed/local control and adaptivity to traffic conditions. In addition to this, we present some modeling and analysis basics to help in better understanding the performance and efficiency of admission control schemes in cellular networks. We describe several admission control schemes and compare them in terms of performance and complexity. Handoff prioritization is the common characteristic of these schemes. We survey different approaches proposed for achieving handoff prioritization with a focus on reservation schemes. Moreover, optimal and near-optimal reservation schemes are presented and discussed. Also, we overview other important schemes such as those designed for multi-service networks and hierarchical systems as well as complete knowledge schemes and those using pricing for CAC. Finally, the paper concludes on the state of current research and points out some of the key issues that need to be addressed in the context of CAC for future cellular networks. Copyright © 2005 John Wiley & Sons, Ltd.
AXNav: Replaying Accessibility Tests from Natural Language

a formative study. From this we build a system that takes as input a manual accessibility test (e.g., “Search for a show in VoiceOver”) and uses an LLM combined with pixel-based UI Understanding models to execute the test and produce a chaptered, navigable video. In each video, to help QA testers we apply heuristics to detect and flag accessibility issues (e.g., Text size not increasing with Large Text enabled, VoiceOver navigation loops). We evaluate this system through a 10 participant user study with accessibility QA professionals who indicated that the tool would be very useful in their current work and performed tests similarly to how they would manually test the features. The study also reveals insights for future work on using LLMs for accessibility testing.
MUSCLE ENDURANCE, NEUROMUSCULAR FATIGABILITY AND COGNITIVE CONTROL DURING PROLONGED DUAL-TASK IN PEOPLE WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE: A CASE-CONTROL STUDY

Recent studies suggest that, compared to healthy individuals, people with chronic obstructive pulmonary disease (pwCOPD) present a reduced capacity to perform cognitive-motor dual-task (CMDT). However, these studies were focused on short-duration CMDT offering limited insight to prolonged CMDT inducing fatigue, which can be encountered in daily life. The present study aimed to explore the effect of adding a cognitive task during repeated muscle contractions on muscle endurance, neuromuscular fatigability and cognitive control in pwCOPD compared to healthy participants Thirteen pwCOPD and thirteen age- and sex-matched healthy participants performed submaximal isometric contractions of the knee extensors until exhaustion in two experimental sessions: (1) without cognitive task and (2) with a concurrent working memory task (i.e., 1-back task). Neuromuscular fatigability (as well as central and peripheral components measured by peripheral magnetic stimulation), cognitive performance and perceived muscle fatigue were assessed throughout the fatiguing tasks. Independently to the experimental condition, pwCOPD exhibited lower muscle endurance compared to healthy participants (p=0.039), mainly explained by earlier peripheral fatigue and faster attainment of higher perceived muscle fatigue (p<0.05). However, neither effect of cognitive task (p=0.223) nor interaction effect (group x condition ; p=0.136) was revealed for muscle endurance. Interestingly, cognitive control was significantly reduced only in pwCOPD at the end of CMDT (p<0.015), suggesting greater difficulty for patients with dual-tasking under fatigue. These findings provide novel insights into how and why fatigue develops in COPD in dual-task context, offering a rationale for including such tasks in rehabilitation programs.
Woods with "Quercus petraea" (Matt.) Liebl. in Tuscany (Italy): a vegetation classification approach

In Tuscany (Italy), oak mixed woods with high cover values of Quercus petraea are rather infrequent but well documented within local and national phytosociological studies, even if, in the literature, not always analyzed and well characterized from the syntaxonomic view point. We gathered 71 published and unpublished Tuscan phytosociological releves where Q. petraea was dominant or with relevant cover values, that were investigated by means of multivariate analysis. The ecological requirements of the resulting groups were indirectly calculated by means of Ellenberg Indicator Values (EIV), and the fidelity coefficient (PHI) for the diagnostic species of each group was calculated. According to our analysis, five different types of Q. petraea woods were found to be present in Tuscany. Each group was characterized floristically and ecologically, allowing to investigate their syntaxonomic aspects. Thus we have attributed the Tuscan Q. petraea communities to five different associations, two of which already existing and three are here described as new associations. In the end, some conservation aspects of these woods regarding Natura 2000 habitats are discussed.
Wearable near-infrared spectroscopy: reliability and sensitivity among different endurance cycling exercise intensities

The present study investigated the reliability and sensitivity of a wearable near-infrared spectroscopy (wNIRS) device in moderate and heavy exercise intensity domains. On three separate days, eleven males performed an incremental test to exhaustion, and in the following visits, four submaximal constant-load bouts (i.e., test and retest) were performed in the moderate-intensity domain (100 and 130 W) and heavy-intensity domain (160 and 190 W). The local tissue oxygen saturation index (SmO2) and pulmonary oxygen uptake (V̇O2) were measured continuously. The absolute SmO2 and V̇O2 values and the change (Δ) from the 3rd to 6th min of exercise were calculated. There was good reliability for SmO2 measurements, as indicated by the high intraclass correlation coefficient analysis (ICC ≥0.84 for all) and low coefficient of variation between the two trials (CV ≤4.1% for all). Steady-state responses were observed for SmO2 and V̇O2 from the 3rd to the 6th min in the two moderate-intensity bouts (P>0.05), whereas SmO2 decreased and V̇O2 increased from the 3rd to the 6th min in the two heavy-intensity bouts (P<0.05). Together, these findings suggested that the SmO2 measured with a wNIRS device is reliable and sensitive to track local metabolic changes provoked by slight increments in exercise intensity.
Microbiome sequencing revealed the abundance of uncultured bacteria in the Phatthalung sago palm-growing soil

Environmental variations have been observed to influence bacterial community composition, thereby impacting biological activities in the soil. Together, the information on bacterial functional groups in Phatthalung sago palm-growing soils remains limited. In this work, the core soil bacterial community in the Phatthalung sago palm-growing areas during both the summer and rainy seasons was examined using V3-V4 amplicon sequencing. Our findings demonstrated that the seasons had no significant effects on the alpha diversity, but the beta diversity of the community was influenced by seasonal variations. The bacteria in the phyla Acidobacteriota, Actinobacteriota, Chloroflexi, Methylomirabilota, Planctomycetota, and Proteobacteria were predominantly identified across the soil samples. Among these, 26 genera were classified as a core microbiome, mostly belonging to uncultured bacteria. Gene functions related to photorespiration and methanogenesis were enriched in both seasons. Genes related to aerobic chemoheterotrophy metabolisms and nitrogen fixation were more abundant in the rainy season soils, while, human pathogen pneumonia-related genes were overrepresented in the summer season. The investigation not only provides into the bacterial composition inherent to the sago palm-cultivated soil but also the gene functions during the shift in seasons.
The fission yeast NDR kinase Orb6 and its signalling pathway MOR regulate cytoplasmic microtubule organization during the cell cycle

Microtubule organization and reorganization during the cell cycle are achieved by regulation of the number, distribution and activity of microtubule-organizing centres (MTOCs). In fission yeast, the Mto1/2 complex determines the activity and distribution of cytoplasmic MTOCs. Upon mitosis, cytoplasmic microtubule nucleation ceases; inactivation of the Mto1/2 complex is triggered by Mto2 hyperphosphorylation. However, the protein kinase(s) that phosphorylates Mto2 remains elusive. Here we show that a conserved signalling network, called MOR (morphogenesis Orb6 network) in fission yeast, negatively regulates cytoplasmic MTOCs through Mto2 phosphorylation to ensure proper microtubule organization. Inactivation of Orb6 kinase, the most downstream MOR component, by attenuation of MOR signalling leads to reduced Mto2 phosphorylation, coincident with increased number of both Mto2 puncta and cytoplasmic microtubules. These defects cause the emergence of uncoordinated mitotic cells with cytoplasmic microtubules, resulting in reduced spindle assembly. Thus, the regulation of Mto2 by the MOR is crucial for cytoplasmic microtubule organization and contributes to reorganization of the microtubule cytoskeletons during the cell cycle.
Functional response of Franklinothrips vespiformis (Thysanoptera: Aeolothripidae) to eggs and nymphs of Bemisia tabaci (Hemiptera: Aleyrodidae)

Abstract The Middle East Asia Minor 1 biotype of Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) is a greenhouse and field crop pest of global significance. The objective of this study was to assess the potential of the generalist predatory thrips, Franklinothrips vespiformis Crawford (Thysanoptera: Aeolothripidae), as a biological control agent for B. tabaci. This was achieved by determining the functional responses of F. vespiformis larvae and adults to the egg and nymphal stages of B. tabaci under laboratory conditions. Analyses consisted of 10 replicates of each predator and prey stage combination on bean leaf discs for a 24-h period. Following logistic regression analyses to determine the functional response type exhibited, response parameters were estimated with nonlinear least squares regression using Roger’s equation. Results showed that F. vespiformis larvae and adults exhibited a Type II functional response when feeding on immature B. tabaci. The handling times (Th) of F. vespiformis larvae and adults were magnitudes higher for B. tabaci nymphs than they were for eggs, which were in part driven by the higher attack rates (a) observed on eggs. The maximum attack rate (T/Th) for B. tabaci eggs and nymphs exhibited by first-stage larvae, second-stage larvae, and adult F. vespiformis increased with increasing predator age. Results from this study suggest that F. vespiformis larvae and particularly adults are promising biological control agents for B. tabaci and are efficient predators at both low and high prey densities.
How automated machines influence employment in manufacturing enterprises?

This paper theoretically analyzes and empirically examines the impact and mechanisms of automated machines on employment in manufacturing enterprises, drawing on task-based model and using micro data from listed Chinese manufacturing enterprises between 2012 and 2019. Our findings reveal that: (1) Automated machines in manufacturing enterprises leads to a substitution effect on the total labor force, with a substitution effect on low-skilled labor and a creation effect on high-skilled labor in terms of employment structure. (2) Further analysis indicates that automated machines primarily have a positive effect on R&D and technical staff, a non-significant effect on sales staff, and a negative impact on production, administrative, and financial staff. (3) The primary influencing mechanisms of automated machines on employment in manufacturing firms are productivity effects and output scale effects, based on the mediation effect model. (4) Considering the industry linkage effect, we employ the input-output method and the Input-Output Table and find that automated machines for upstream (downstream) manufacturing enterprises will result in a substitution effect on employment for downstream (upstream) enterprises. The novelties and research contributions are as follows: (1) we conduct a structural decomposition of total employment, and further decompose employment positions into production, R&D, sales, finance, and administration. (2) We try to investigate the industry linkage effect about the impact of automated machines on the employment of upstream and downstream enterprises. (3) We use data from listed manufacturing companies, and the data of existing research are about provincial and industry-level data.