Miracle-dz/UniFAR-FTUs huggingface dataset:
Credence: Augmenting Datacenter Switch Buffer Sharing with ML Predictions

Packet buffers in datacenter switches are shared across all the switch ports in order to improve the overall throughput. The trend of shrinking buffer sizes in datacenter switches makes buffer sharing extremely challenging and a critical performance issue. Literature suggests that push-out buffer sharing algorithms have significantly better performance guarantees compared to drop-tail algorithms. Unfortunately, switches are unable to benefit from these algorithms due to lack of support for push-out operations in hardware. Our key observation is that drop-tail buffers can emulate push-out buffers if the future packet arrivals are known ahead of time. This suggests that augmenting drop-tail algorithms with predictions about the future arrivals has the potential to significantly improve performance. This paper is the first research attempt in this direction. We propose Credence, a drop-tail buffer sharing algorithm augmented with machine-learned predictions. Credence can unlock the performance only attainable by push-out algorithms so far. Its performance hinges on the accuracy of predictions. Specifically, Credence achieves near-optimal performance of the best known push-out algorithm LQD (Longest Queue Drop) with perfect predictions, but gracefully degrades to the performance of the simplest drop-tail algorithm Complete Sharing when the prediction error gets arbitrarily worse. Our evaluations show that Credence improves throughput by $1.5$x compared to traditional approaches. In terms of flow completion times, we show that Credence improves upon the state-of-the-art approaches by up to $95\%$ using off-the-shelf machine learning techniques that are also practical in today's hardware. We believe this work opens several interesting future work opportunities both in systems and theory that we discuss at the end of this paper.
Recent Advancement in 3D Biometrics using Monocular Camera

Recent literature has witnessed significant interest towards 3D biometrics employing monocular vision for robust authentication methods. Motivated by this, in this work we seek to provide insight on recent development in the area of 3D biometrics employing monocular vision. We present the similarity and dissimilarity of 3D monocular biometrics and classical biometrics, listing the strengths and challenges. Further, we provide an overview of recent techniques in 3D biometrics with monocular vision, as well as application systems adopted by the industry. Finally, we discuss open research problems in this area of research.
Among-individual variation in thermal plasticity of fish metabolic rates causes profound variation in temperature-specific trait repeatability, but does not co-vary with behavioural plasticity

Conspecifics of the same age and size differ consistently in the pace with which they expend energy. This among-individual variation in metabolic rate is thought to influence behavioural variation, since differences in energy requirements should motivate behaviours that facilitate energy acquisition, such as being bold or active in foraging. While there is evidence for links between metabolic rate and behaviour in constant environments, we know little about whether metabolic rate and behaviour change together when the environment changes—that is, if metabolic and behavioural plasticity co-vary. We investigated this using a fish that becomes dormant in winter and strongly reduces its activity when the environment cools, the cunner (Tautogolabrus adspersus). We found strong and predictable among-individual variation in thermal plasticity of metabolic rates, from resting to maximum levels, but no evidence for among-individual variation in thermal plasticity of movement activity, meaning that these key physiological and behavioural traits change independently when the environment changes. The strong among-individual variation in metabolic rate plasticity resulted in much higher repeatability (among-individual consistency) of metabolic rates at warm than cold temperatures, indicating that the potential for metabolic rate to evolve under selection is temperature-dependent, as repeatability can set the upper limit to heritability. This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.
Better-than-chance prediction of cooperative behaviour from first and second impressions

Abstract Abstract Could cooperation among strangers be facilitated by adaptations that use sparse information to accurately predict cooperative behaviour? We hypothesise that predictions are influenced by beliefs, descriptions, appearance and behavioural history available for first and second impressions. We also hypothesise that predictions improve when more information is available. We conducted a two-part study. First, we recorded thin-slice videos of university students just before their choices in a repeated Prisoner's Dilemma with matched partners. Second, a worldwide sample of raters evaluated each player using videos, photos, only gender labels or neither images nor labels. Raters guessed players’ first-round Prisoner's Dilemma choices and then their second-round choices after reviewing first-round behavioural histories. Our design allows us to investigate incremental effects of gender, appearance and behavioural history gleaned during first and second impressions. Predictions become more accurate and better-than-chance when gender, appearance or behavioural history is added. However, these effects are not incrementally cumulative. Predictions from treatments showing player appearance were no more accurate than those from treatments revealing gender labels and predictions from videos were no more accurate than those from photos. These results demonstrate how people accurately predict cooperation under sparse information conditions, helping explain why conditional cooperation is common among strangers.
Whither dominance? An enduring evolutionary legacy of primate sociality

Abstract This article discusses dominance personality dimensions found in primates, particularly in the great apes, and how they compare to dominance in humans. Dominance traits are seen in virtually all primate species, and these dimensions reflect how adept an individual is at ascending within a social hierarchy. Among great apes, dominance is one of the most prominent personality factors but, in humans, dominance is usually modeled as a facet of extraversion. Social, cultural, and cognitive differences between humans and our closest ape relatives are explored, alongside humanity’s hierarchical and egalitarian heritage. The basic characteristics of dominance in humans and nonhuman great apes are then described, alongside the similarities and differences between great apes. African apes live in societies each with its own hierarchical organization. Humans were a possible exception for some of our history, but more recently, hierarchies have dominated. The general characteristics of high-dominance humans, particularly those living in industrialized nations, are described. Dominance itself can be subdivided into correlated subfactors: domineering, prestige, and leadership. Various explanations have been posed for why dominance has declined in prominence within human personality factor structures, and several possibilities are evaluated. The value of dominance in personality research is discussed: dominance has links to, for instance, age, sex, aggression, self-esteem, locus of control, stress, health, and multiple socioeconomic status indicators. The piece concludes with recommendations for researchers who wish to assess dominance in personality.
Optimize Efficiency of Utilizing Systems by Dynamic Core Binding

Load balancing at both the process and thread levels is imperative for minimizing application computation time in the context of MPI/OpenMP hybrid parallelization. This necessity arises from the constraint that, within a typical hybrid parallel environment, an identical number of cores is bound to each process. Dynamic Core Binding, however, adjusts the core binding based on the process’s workload, thereby realizing load balancing at the core level. In prior research, we have implemented the DCB library, which has two policies for computation time reduction or power reduction. In this paper, we show that the two policies provided by the DCB library can be used together to achieve both computation time reduction and power consumption reduction.
Bivariate distributions of significant wave height and peak period of sea states in deep and shallow waters offshore Portugal

A bivariate description of the wave parameters significant wave height (Hs) and peak period (Tp) is presented in three areas offshore mainland Portugal. In each area a location in deep water and one in shallow water are considered to provide information of the changes associated with approaching coast. Three different methods of fitting bivariate models are compared using a 12-year hindcast dataset in order to conclude on their accuracy and applicability. The Conditional Modelling Approach defines the joint distribution from a marginal distribution of Hs and a set of distributions of Tp conditional on Hs. Other model fits the Plackett model to the data, and the last one applies the Box-Cox transformations to the data with the aim of making it approximately normal to fit a bivariate normal distribution to the transformed data. The methods provide a good fit despite the differences in each one. The results of this study provide long term information on the wave conditions that can be used in the assessment of the operability and safety of shipping in different water depths.
Efficient value encoding through convergence of tactile and visual value information in the primate putamen

The processing of diverse sensory values by a limited number of basal ganglia neurons raises the question of whether each value is processed independently or combined as the number of neurons decreases from the cortex to downstream structures. Here, we discovered that tactile and visual values were partially converged in the primate putamen, enhancing its efficiency in encoding values while preserving modality information. Humans and monkeys performed tactile and visual value discrimination tasks. Notably, the human putamen selectively represented both tactile and visual values in fMRI scans. Single-unit electrophysiology further revealed that half of the individual neurons in the macaque putamen encoded both tactile and visual values, and the other half encoded each value separately. The bimodal value neurons enable more efficient value encoding using fewer neurons than the modality-selective value neurons. Our data suggest that the basal ganglia system uses modality convergence to efficiently encode values with limited resources.
An ecological investigation of the effect of background noise on speech processing in a Virtual Classroom

Many real-life situations can be extremely noisy. Psychoacoustic studies have shown that background noise can have a detrimental effect on the ability to process and understand speech. However, most studies use stimuli and task designs that are highly artificial, limiting their generalization to more realistic contexts. Moreover, to date, we do not fully understand the neurophysiological consequences of trying to pay attention to speech in a noisy place. To address this lab to real-life gap and increase the ecological validity of speech in noise research, here we introduce a novel audiovisual Virtual Reality (VR) experimental platform. Combined with neurophysiological measurements of neural activity (EEG), eye-gaze and skin conductance (GSR) we studied the effects of background noise in a realistic context where the ability to process and understand continuous speech is especially important: A VR Classroom. Participants (n=32) sat in a VR Classroom and were told to pay attention to mini-lecture segments by a virtual teacher. Trials were either Quiet or contained background construction noise, emitted from outside the classroom window, which was either Continuous (drilling) or Intermittent (air hammers). Result show that background noise had a detrimental effect on learning outcomes, which was also accompanied by reduced neural tracking of the teacher’s speech. Comparison of the two noise types showed that the intermittent construction noise was more disruptive than continuous noise, as index by both behavioral and neural measures, and it also elicited higher skin-conductance levels, reflecting heightened arousal. Interesting, eye-gaze dynamics were not affected by the presence of noise. This study advances our understanding of the neurophysiological effects of background noise and extends it to more ecologically relevant contexts. It also emphasizes the role that temporal dynamics play for processing speech in noise, highlighting the need to consider the features of realistic noises, as we expand speech in noise research to increasingly realistic circumstances.
HAPTIC INTERFACES

For many years, the robotic community sought to develop robots that can eventually operate autonomously and eliminate the need for human operators. However, there is an increasing realization that there are some tasks that humans can perform significantly better but, due to associated hazards, distance, physical limitations and other causes, only robots can be employed to perform these tasks. Remotely performing these types of tasks requires operating robots as human surrogates. Such a capability has been the goal of many studies and significant success has been reported in recent years. NASA Johnson Space Center is currently developing a robotic astronaut, called Robonaut (see Figures 1 and 2), which is capable of performing various tasks at remote sites. Robonaut was designed such that a human operator who is wearing gloves and/or suit with sensors can control the robot. Unfortunately, due to unavailability of force and tactile feedback capability in the control suit/glove, the operator determines the required action by visual feedback, i.e. looking at the Robonaut action at the remote site. This approach is ineffective and is limiting the potential tasks that Robonaut can perform. As human activity in space increases there is an increasing need for robots to perform dexterous Extra Vehicular Activity (EVA). Existing space robots are inadequate substitutes for an astronaut because they: (1) require additional special alignment targets and grapple fixtures. (2) are too large to fit through tight EVA access corridors. (3) do not possess adequate speed and dexterity to handle small and complex items, soft and flexible materials, as well as most common EVA interfaces. (4) are controlled by operators that have only visual feedback limiting incredibly their "perception" of the work-environment.
DeepAction: A MATLAB toolbox for automated classification of animal behavior in video

The identification of behavior in video is a critical but time-consuming component in many areas of animal behavior research. Here, we introduce DeepAction, a deep learning-based toolbox for automatically annotating animal behavior in video. Our approach uses features extracted from raw video frames by a pretrained convolutional neural network to train a recurrent neural network classifier. We evaluate the classifier on two benchmark rodent datasets and show that it achieves high accuracy, requires little training data, and surpasses both human agreement and similar existing methods. We also create a confidence score for classifier output, and show our method provides an accurate estimate of classifier performance and reduces the time required by human annotators to review and correct automatically-produced annotations. We release our system and accompanying annotation interface as an adaptable, non-technical, and open-source MATLAB toolbox.
Most patients with COPD are unaware of their health threats and are not diagnosed: national-level study using pulmonary function test

Background: There is public health risk due to COPD underdiagnosis. To decrease the rate of underdiagnosed COPD and to plan strategies for efficient screening for disease management, we investigated the national-level prevalence of COPD by airflow obstruction.Methods: We performed pulmonary function test (PFT) in 24,454 adults aged >40 years, based on 8 years (2010-2017) of data. FEV1/FVC ratio of <0.70 was defined as COPD. We performed multivariate logistic regression analysis by adjusting dependent variable to determine if each factor was related to COPD.Results: The annual COPD prevalence increased from 13.1% in 2010 to 14.6% in 2012 followed by 13.3% in 2017. The proportion of potential high-risk group that was not diagnosed with COPD and other respiratory diseases was 80.8% in 2010 and 78.1% in 2017 ranging between 73.0% and 86.2%. Multivariate logistic regression showed that ever smokers in the COPD diagnosed patient group had a 2.38 times higher rate of diagnosis than never smokers. Furthermore, the group with PY of 4Q had a 2.81 times higher diagnosis rate than that of 1Q. Regarding the potential high-risk group, the ever smokers, current smokers, and heavy smokers had a higher prevalence than never smokers.Conclusion: Although COPD prevalence was high in the ever, current, and heavy smokers, only the diagnosis rate of COPD in ever smokers was 2.38 times higher than never smokers, indicating that smoking is the most important cause of COPD.
Inﬂuence of Ovine Milk in Mixture with Bovine Milk on the Quality of Reduced Fat Muenster-Type Cheese

Reduced fat Muenster-type cheeses were manufactured from a mixture of bovine skim milk and ovine whole milk and from bovine milk only (control). Cheeses were evaluated at 15, 30, 60, 90, 120, and 180 d of age for numbers and type of microﬂora, casein hydrolysis, and amounts of free fatty acids. α s1 -Casein degradation was similar for both cheeses during the aging period, but β -casein degradation proceeded at a faster rate in the control cheese. The total amounts of free fatty acids remained constant throughout the ripening time; however, the cheeses produced with bovine/ovine milk yielded a signiﬁcantly larger amount of caprylic (C 8:0 ) and capric (C 10:0 ) acids compared with the bovine milk cheeses. Lactobacilli increased during the aging period, while the populations of lactic acid bacteria, yeast and molds, and lipolytic organisms did not increase. Both cheeses had comparable cheese ﬂavor intensity, but the bovine/ovine milk cheese had a greater occurrence of off ﬂavors. The bovine/ovine milk cheeses were ﬁrmer than the bovine cheeses throughout the aging period.
Missing the forest for the trees: when monitoring quantitative measures distorts task prioritization

Managers’ use of remote monitoring software increased following the transition to working from home during the Covid-19 pandemic to compensate for reduced observability. Higher observability entails quantitative measures not directly related to productivity, potentially incentivizing workers to prioritize quantity over quality. For example, office workers may increase observable work hours by directing effort inefficiently. Observing the number of completed tasks incentivizes workers to perform many meaningless tasks rather than prioritize productive ones. We design an experiment where workers can allocate effort based on perceived task difficulty and manipulate the structure of the signal to the manager. We show theoretically that quantitative information in the signal distorts incentives. In equilibrium, workers prioritize productive tasks less, reducing overall productivity. Our results confirm that removing quantitative information from the signal increases productivity by shifting workers’ strategies. Enriching the signal with quantitative information, however, does not have the opposite effect.
Controlled evaluation of a transition clinic for Dutch young people with cystic fibrosis

Transition clinics (TCs) are advocated as best practice to support young people with cystic fibrosis (CF) during transition to adulthood and adult care. We aimed to research the functioning of a TC for young people with CF compared with direct hand‐over care and to evaluate whether those treated at the TC have better transfer experiences and outcomes compared with the control group.
The effect of confinement on thermally induced fluctuations in nanomagnets

We study the thermally excited magnetization fluctuations of mesoscale magnetic structures. In the micromagnetic framework we utilize a computational method for implementing temperature in simulations, by means of a stochastic magnetic field. Using this approach, we investigate the confinement effects related to the size of the structures and characterize the emerging magnonic modes, with respect to their spatial distribution of amplitudes and frequencies. We also analyze the temperature dependence of low frequency edge mode fluctuations for various structure sizes, recovering temporal dynamics associated with intrinsic energy barriers arising from their magnetization texture and finite size.
Load-Eﬀiciency-Balance Cell Selection Policy for IAB Networks

— The 3rd Generation Partnership Project (3GPP) has proposed recently the Integrated Access and Backhaul (IAB) to simplify the deployment of new base stations, where the connection between the User Equipment (UE) and the Core Network (CN) is provided by a multi-hop 5G wireless connection between the IAB nodes. The main contribution of this paper is to propose a cell selection policy specific to IAB, that aims to guarantee the lowest possible total transmission bandwidth cost (including the backhaul cost which is a significant factor in an IAB context) while respecting the base stations capacities, in order to boost IAB network capacity. In this context, a comparative performance study is performed between our proposed policy and state-of-the-art policies. System level simulation results show that our solution provides the best system capacity via a trade-off between spectral efficiency and load balancing.
Seismicity in the Hokuriku District

A telemetering observation of microearthquakes started in May 1976, at the Hokuriku Microearthquake Observatory, Disaster Prevention Research Institute, Kyoto University. In this article, seismicity in the Hokuriku area was investigated, using the data by the telemetering observation for 14 months since May 1967, and historical data of large earthquakes. The microseismicity is high in the areas from Fukui plain to the north-eastern side of Lake Biwa and from west coast of Lake Biwa to Wakasa Bay. On the contrary, microseismicity is relatively low in the areas of Lake Biwa, off-shore of Japan Sea, and the northern part of Gif u and Ishikawa prefectures. The microseismicity in this area seems to be closely related to large tectonic structures, such as Fukui earthquake fault, Neodani fault, and Yanagase fault. These active faults, in general, form boundaries between seismically active and aseismic areas. The active areas of microearthquakes at present are almost coincident with those of disastrous earthquakes in the past. In regard to the activities of the past disastrous earthquakes, there seems to be some regularities of occurrence spacially and temporally.