Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic monitoring of body condition score of hight yielding Holstein cows using three-dimensional imaging technique

Body condition scoring (BCS) refers to the relative amount of subcutaneous body fat or energy reserve in the dairy cow. BCS is an important management tool for maximizing milk production and reproductive efficiency while reducing the incidence of metabolic and other peripartum diseases. The threedimensional BCS camera, made by the Swedish company “DeLaval” was used in the research. This camera is connected to the cow identification system. According to the camera location place, the cow is evaluated 2 to 10 times per day. The obtained information is sent to the computer. DeLaval 3D camera has been developed for three different cow breed types. The research was done at the Latvia University of Agriculture research and study farm “Vecauce” and the results of 10 primiparous and 10 multiparous Holstein breed cows in the robotic voluntary milking system were analysed. The data were collected from February to December 2016. The research shows that milking cow BCS during 120 lactation days was reduced from 3.5 till 2.7 units and the correlation among the parameters average tight (r = 0.72). The research cows were with a high productivity, in 120 lactation days it was 41.7 kg in average. The largest BCS during 120 lactation days was noticed to primiparous cows and the correlation between the BCS and milk yield was r = -0.71. The parameters among older lactation cow BCS and yielding were less (r = -0.22). The fastest increase of all age cows has been in the first lactation month, where the milk yield increase of primiparous cows was 13.5 %, but the BCS decrease was 5.7 %. The milk yield increase of multiparous cows was 10 %, but the BCS decrease was 5.7 %. Primiparous cows, which become pregnant in the first insemination time, were inseminated in the 89 ± 8.9 day and their BCS was 3.0 ± 0.12 units. Multiparous cows, which become pregnant, were inseminated in the 84 ± 3.5 lactation day and their BCS was 2.7 ± 0.3 units.
Critique and Consequence∗

Abstract After describing the landscape in macroeconomics and econometrics in Spring 1973 when Robert E. Lucas (1976) first presented his Critique at the inaugural CarnegieRochester conference, I add a fourth example based on Sargent and Wallace (1973) to those in section 5 of Lucas’s paper. To portray consequences of Lucas’s Critique, I use it as a vehicle to describe the time inconsistency of optimal plans and their credibility. A theory of government policy affects chains of influence among money creation and inflation rates at different dates. Different theories of policy bring different state vectors in recursive representations of inflation-money-supply outcomes.
Running head: CONCEPT LEARNING 1 Perception and Simulation During Concept Learning

A key component of humans’ striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel concepts. Bongard problems, which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. Bongard problems are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new type of Bongard problem called Physical Bongard Problems (PBPs) is introduced, which requires solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The PATHS (Perceiving And Testing Hypotheses on Structures) computational model which can solve many PBPs is presented, and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP. Spatially or temporally juxtaposing similar (relative to dissimilar) scenes promotes category learning when the scenes belong to different categories, but hinders learning when the similar scenes belong to the same category. The core theoretical commitments of PATHS, which we believe to also exemplify open-ended human category learning, are a) the continual perception of new scene descriptions over the course of category learning, b) the context-dependent nature of that perceptual process, in which the perceived scenes establish the context for the perception of subsequent scenes, c) hypothesis construction by combining descriptions into explicit rules, and d) bi-directional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories.
Few Shot Learning Approaches for Classifying Rare Mobile-App Encrypted Traffic Samples

Deep Learning (DL) is effective for classifying encrypted network traffic. However, it requires large amounts of labeled data to feed typical data-hungry training processes. Unfortunately, collecting and labeling rich network-traffic datasets is a complex and costly procedure not always affordable in practice, possibly hindering DL solutions. Few Shot Learning (FSL) aims at tackling this shortcoming, providing means to leverage non-few knowledge to support classification tasks related to traffic with few labeled data available. Although FSL has been largely investigated in other domains (e.g., computer vision), it has been only preliminarily adopted for the classification of encrypted traffic. In this work, we provide a first attempt in adopting FSL for classifying mobile-app encrypted traffic. Specifically, we consider the two most popular FSL paradigms: meta learning (learn to learn) and transfer learning (knowledge transfer from related tasks). We consider a number of variants for each (namely MatchingNet, ProtoNet, RelationNet, MetaOptNet, fo-MAML, ANIL, Fine-Tuning, and Freezing) and provide an empirical assessment of these approaches when adopted for mobile-app traffic classification considering the Mirage-2019 dataset as a test bench. Results show that FSL in mobile-app traffic classification is feasible, reaching satisfactory results (up to 80% F1-score), but leaving room for improvement.
Learning-Based Fronthaul Compression for Uplink Cloud Radio Access Networks

This paper investigates the uplink signal dimension reduction problem for a user-centric cloud radio access network, in which each single-antenna user communicates with the central processor (CP) through a cluster of remote radio heads (RRHs). To reduce the fronthaul traffic, each RRH applies a compression matrix to reduce the dimension of the received signal before relaying it to the CP. However, the optimal design of the compression matrices requires significant communication overhead for transmitting the high-dimensional channel state information (CSI) matrices from the RRHs to the CP. To address this issue, this paper proposes a deep learning framework to first learn a sub-optimal compression matrix at each RRH based on the local CSI, then iteratively refine the learned compression matrix using a meta-learning-based gradient method. To reduce the communication cost for CSI sharing and gradients transmission, this paper proposes an efficient signaling scheme that only requires the transmission of low-dimensional effective CSI and its gradient between the CP and each RRH. Furthermore, a meta-learning-based gated recurrent unit (GRU) network is proposed to reduce the number of signaling transmission rounds. For the sum-rate maximization problem, simulation results show that the proposed two-stage neural network can perform closely to the fully cooperative global CSI-based benchmark with significantly reduced communication overhead. Moreover, using the first stage alone can already outperform the existing local CSI-based benchmark.
Beliefs about causes of major depression: Clinical and treatment correlates among African Americans in an urban community

NewYork State Psychiatric Institute Correspondence NewYorkStatePsychiatric Institute, 1051RiversideDrive,Unit 24,NewYork,NY10032. Email: em713@cumc.columbia. edu, emurphy@nyspi.columbia. edu Funding information This studywasmadepossiblewith funding support fromtheNational Institute ofMinorityHealth andHealthDisparities1K22MD006140 (P.I.,Murphy, E.) and theNational InstituteofMentalHealth 1K23MH102540 (P.I.,Hankerson, S). Wehavenoconflicts of interest.We thankRuth Ottman forher valuable feedbackandedits of the manuscript. Abstract Objectives Major depression is increasingly viewed in the United States public as a medical disorder with biological and psychosocial causes. Yet little is known about how causal attributions about depression vary among low-income racial minorities. This study examined beliefs about causes of depression and their demographic, clinical and treatment correlates in a lower incomeAfricanAmerican sample.
Study on Translation Project Management Process of Translation Enterprises in the Post-epidemic Era

Since the new crown pneumonia epidemic in early 2020, the epidemic has had a huge impact on translation companies, and the operational risks of translation companies have further increased. This paper will take the overall translation project management process as a guide, grasp the main features of the project management process, apply the principles of project integrated management to optimize resource allocation, introduce risk management, Summarize the impact of the epidemic on the various process systems of translation companies, try to comprehensively analyze the project management operation of Chinese translation companies, and establish a risk management system to achieve risk response under the epidemic. The analysis points out that the epidemic has a huge impact and influence on translation companies in many aspects, and this paper will give countermeasures and relevant solutions from the perspectives of governance structure of translation companies and the establishment of translation companies' own risk management system.
Towards Greener Logistics: a Multi-Body Simulation to Test Pallet Stability

Pallets are critical components in the logistics of food and beverage products transportation, and their stability is essential to ensure reliability of the handling system as well as safety during road and rail freight. This work aims to develop an innovative solution for evaluating pallet stability very early, ideally during the design phase of the pallet schema and the wrapping format. Differently from other investigations, the goal of our work is to analyse the dynamics of pallets wrapped in an envelope of paper material instead of plastic. By collecting raw video data from an acceleration test bench, and using computer vision and machine learning techniques, we develop a physically realistic multi-body simulation. The simulation is completely virtual and capable to evaluate the stability of the pallet under different configurations and loading conditions.
LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems

Bayesian optimization (BO) has recently been demonstrated as a powerful tool for efficient derivative-free optimization of expensive black-box functions, such as those prevalent in performance optimization of complex energy systems. Classical BO algorithms ignore the relationship between consecutive optimizer candidates, resulting in jumps in the admissible search space which can lead to fail-safe mechanisms being triggered, or undesired transient dynamics that violate operational constraints. In this paper, we propose LSR-BO, a novel global optimization methodology that enforces local search region (LSR) constraints by design, which restricts how much the optimizer candidate can be changed at every iteration. We demonstrate that naively incorporating LSR constraints into BO causes the algorithm to get stuck in local suboptimal solutions, and overcome this challenge through the development a novel exploration strategy that can gracefully navigate the trade-off between short-term "local", and long-term "global", performance improvement. Furthermore, we provide theoretical guarantees on the convergence of LSR-BO. Finally, we verify the effectiveness of our proposed LSR-BO method on an illustrative benchmark and a real-world energy minimization problem for a commercial vapor compression system.
Modelling emission spectra of the gas-discharge plasma for the aims of its analysis and parameters estimation

In this paper modelling of the emission spectra of gas-discharge plasma using the data about the electronic transitions in substances under the assumption of local thermodynamic equilibrium is discussed. The possibility of using these spectra to simplify the analysis of the plasma composition during the coating deposition process and determine the parameters of the gas-discharge plasma are shown.
Strategic Influencers and the Shaping of Beliefs

Influencers, from propagandists to sellers, expend vast resources targeting agents who amplify their message through word-of-mouth communication. While agents differ in network position, they also differ in their bias: agents may naturally read articles with a particular slant or buy products from a certain seller. Absent competition, an influencer prefers targeting central agents and those biased against it. If agents are unbiased, competition leads to influencers targeting more central agents. However, when agents have heterogeneous biases and competition is intense, the incentive to deter one’s rival dominates. Influencers protect their base, targeting those with similar beliefs in equilibrium. JEL Codes: D83, D85, L10
Why So Few Women in Math and Science?

Mathematics, a computer program, syntax Social A political election, a legal system, a business Organizable A taxonomy, a collection, a library Motoric A sports technique, a performance, a musical technique • Test scores. The SAT Reasoning Test (formerly the Scholastic Aptitude Test and Scholastic Assessment Test), which is administered nationally to college applicants in the United States, is, in part, a test of math skills. Males on average score 50 points higher than females on this portion of the test.5 Among individuals who score above 700 (out of a possible 800) points, the sex ratio is thirteen to one (men to women). 6 • Constructional abilities. On average, men score higher than women in an assembly task in which people are asked to put together a three-dimensional (3-D) mechanical apparatus. Boys are also better at constructing block buildings from two-dimensional blueprints, and they show more interest than girls in playing with LEGO bricks, which can be combined and recombined into an infinite number of systems. Boys as young as three years of age are also faster at copying 3-D models of outsized LEGO pieces. Older boys, from the age of nine years, are better than girls at imagining what a 3-D object will look like if it is laid out flat, and at constructing a 3-D structure from just an aerial and frontal view in a picture.7 • The water-level task. Originally devised by the Swiss child psychologist Jean Piaget, the water-level task involves a bottle that is tipped at an angle. Individuals are asked to predict the water level. Women more often draw the water level aligned with the tilt of the bottle and not horizontally, as is correct.8 • The rod-and-frame test. The rod-and-frame test features a movable rod inside a movable frame. As the frame is moved, the subject is asked to adjust the rod to keep it in a vertical position. A person whose judgment of vertical orientation is influenced by the tilt of the frame is said to be “field-dependent”—that is, his or her judgment is easily swayed by extraneous input in the surrounding context. One who is not influenced by the tilt of the frame is said to be “field-independent. ” Most studies indicate that females are more field-dependent—that is, women are relatively more distracted by contextual cues, and they tend not to consider each variable within a system separately. They are more likely than WHY SO FEW WOMEN IN MATH AND SCIENCE? 13
Retraction Retracted: The Effect of Oxytocin Plus Carboprost Methylate in Preventing PostpartumHemorrhage in High-Risk Pregnancy and Its Effect on Blood Pressure

Te presence of these indicators undermines our confdence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. Te corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.
Modeling social interaction in human-robot joint action from a bio-inspired approach

Social robotics is a multidisciplinary research field interested in studying the interaction of robots at the social level. In recent decades, research on human-robot interactions (HRI) has been accompanied by the design of robots that are increasingly more capable at engaging in joint actions with humans. However, these advances have also encountered significant challenges in ensuring smooth interactions and maintaining human engagement (Belhassein et al. (2022)).
Hybrid MOFs Supercapacitor: A Mini Review

In the world of energy storage devices, Supercapacitors occupy a very unique and pivotal position. Their rapid rate of discharge gives them high power density. They have high reversibility and are robust to a large number of charging and discharging cycles. Sustained research has revealed a certain set of properties and behaviour, that every prospective candidate supercapacitor material must possess. Metal organic frameworks (MOFs) with unique textural properties, excellent specific surface area, tuneable porous structure and distinctively advantageous electrochemical behaviour are prominent candidates for the use in energy storage applications. However pristine MOF based materials are handicapped due to their low conductivity and poor mechanical stability. These inherent deficiencies can be overcome by hybridizing pristine MOFs with other materials like carbon materials (Activated Carbon, Graphene and Carbon Nano Tubes), conducting polymers, metals, and small molecules through variety of methods. This review puts the spotlight on the utilization, growth and various forms of hybrid materials based on MOFs for supercapacitor applications. It also highlights the various surface engineering techniques on the materials for high potential applications.
Porphyrin-Based Carbon Dots for Efficient Photocatalytic Reduction of CO2 to Methanol

Photocatalytic CO2 reduction to solar fuel is a potential way to overcome the problem of high carbon dioxide concentrations; however, the process still faces enormous challenges, such as the low light absorption efficiency and high carrier recombination rates. This work synthesized tetraphenylporphyrin-based carbon dots (TPP-CDs) for the photocatalytic CO2 reduction to CH3OH under simulated sunlight to extend the absorption region of the CDs. The conduction band of the TPP-CDs has a sufficiently negative reduction potential to reduce CO2. TPP-CDs containing 1 wt. % TPP achieved an optimum CH3OH production rate of 173.35 μmol·gcat-1·h-1, with a remarkable 2.06-fold increase compared to pristine CDs without TPP. In this study, TPP-CDs were synthesized by changing the CD precursors to improve the utilization of visible light and apply them to the photocatalytic reduction of CO2.
Discovery of the mountain glass snail, Hessemilimax kotulae (Westerlund, 1883) (Mollusca, Gastropoda, vitrinidae), in the High vosges Mountains (northeast France) and its conservation

ABSTRACT We report here four new localities for the mountain glass snail Hessemilimax kotulae (Westerlund, 1883) in the northeast part of France. These new records extend the range of this species considerably within the Vosges Mountains, where it was previously known from only a single site. Specimens were found at the base of or in scree slopes facing mainly north or northeast or along streams in fir or mixed forests, at elevations between 863 and 1145 m. Because H. kotulae is difficult to find and identify, we provide the description of habitats where the species has been found and the associated gastropod assemblages as well as morphological and anatomical characters in order to facilitate its identification by non-malacological conservationists. Finally, we revised the species conservation status on the French Red List. We further discuss the conservation of land snails that reach the upper limit of their distribution at the highest elevations of the low mountain ranges in north-eastern France, in the context of global warming. RÉSUMÉ Découverte de la semilimace alpine, Hessemilimax kotulae (Westerlund, 1883) (Mollusca, Gastropoda, Vitrinidae), dans les Hautes-Vosges (nord-est de la France) et sa conservation. Nous signalons ici quatre nouvelles localités pour la semilimace alpine Hessemilimax kotulae (Westerlund, 1883) dans le nord-est de la France. Ces nouvelles mentions étendent considérablement l'aire de répartition de cette espèce dans le massif des Vosges, où elle n'était auparavant connue que d'un seul site. Les spécimens ont été trouvés à la base ou dans des éboulis principalement exposés au nord ou au nord-est, ou le long de petits ruissellements dans des forêts de sapins ou mixtes, à des altitudes comprises entre 863 et 1145 m. Dans la mesure où H. kotulae est difficile à trouver et à identifier, nous fournissons dans cet article la description des habitats où l'espèce a été trouvée et des cortèges de gastéropodes associés ainsi que les caractères morphologiques et anatomiques, et ce, afin de faciliter son identification par les gestionnaires des espaces naturels non-malacologues. Enfin, nous avons réévalué le statut de conservation de l'espèce dans la Liste Rouge française. Nous discutons également de la conservation des gastéropodes terrestres qui atteignent la limite supérieure de leur distribution aux plus hautes altitudes des chaînes de basse montagne du nord-est de la France, dans le contexte du réchauffement climatique.
Tiling Framework for Heterogeneous Computing of Matrix based Tiled Algorithms

Tiling matrix operations can improve the load balancing and performance of applications on heterogeneous computing resources. Writing a tile-based algorithm for each operation with a traditional, hand-tuned tiling approach that uses for loops in C/C++ is cumbersome and error prone. Moreover, it must enable and support the heterogeneous memory management of data objects and also explore architecture-supported, native, tiled-data transfer APIs instead of copying the tiled data to continuous memory before the data transfer. The tiling framework provides a tiled data structure for heterogeneous memory mapping and parameterization to a heterogeneous task specification API. We have integrated our tiled framework into MatRIS (Math kernels library using IRIS). IRIS is a heterogeneous run-time framework with a heterogeneous programming model, memory model, and task execution model. Experiments reveal that the tiled framework for BLAS operations has improved the programmability of tiled BLAS and improved performance by ~20% when compared against the traditional method that copies the data to continuous memory locations for heterogeneous computing.