Miracle-dz/UniFAR-FTUs huggingface dataset:
Disentangling the Representation of Identity from Head View Along the Human Face Processing Pathway

Neural models of a distributed system for face perception implicate a network of regions in the ventral visual stream for recognition of identity. Here, we report an fMRI neural decoding study in humans that shows that this pathway culminates in a right inferior frontal cortex face area (rIFFA) with a representation of individual identities that has been disentangled from variable visual features in different images of the same person. At earlier stages in the pathway, processing begins in early visual cortex and the occipital face area (OFA) with representations of head view that are invariant across identities, and proceeds to an intermediate level of representation in the fusiform face area (FFA) in which identity is emerging but still entangled with head view. Three-dimensional, view-invariant representation of identities in the rIFFA may be the critical link to the extended system for face perception, affording activation of person knowledge and emotional responses to familiar faces. Significance Statement In this fMRI decoding experiment, we address how face images are processed in successive stages to disentangle the view-invariant representation of identity from variable visual features. Representations in early visual cortex and the occipital face area distinguish head views, invariant across identities. An intermediate level of representation in the fusiform face area distinguishes identities but still is entangled with head view. The face-processing pathway culminates in the right inferior frontal area with representation of view-independent identity. This paper clarifies the homologies between the human and macaque face processing systems. The findings show further, however, the importance of the inferior frontal cortex in decoding face identity, a result that has not yet been reported in the monkey literature.
Topological Wilson-loop area law manifested using a superposition of loops

We introduce a new topological effect involving interference of two meson loops, manifesting a path-independent topological area dependence. The effect also draws a connection between quark confinement, Wilson-loops and topological interference effects. Although this is only a gedanken experiment in the context of particle physics, such an experiment may be realized and used as a tool to test confinement effects and phase transitions in quantum simulation of dynamic gauge theories.
Toward Unified Control of Networks of Switches and Sensors Through a Network Operating System

Wireless sensor networks (WSNs) are an integral part of an Internet of Things (IoT) ecosystem. In fact, a typical IoT service deployment considers an integrated network of switches and sensors (as well as actuators). In order to manage this heterogeneous network infrastructure, the use of software defined networking (SDN) technologies is anticipated. However, existing SDN-enabling technologies consider only isolated networks and there has been no concrete solution that treats the whole network in a unified way. In this paper, a network operating system is proposed as a way to address the above issue. Starting from the open network operating system, which currently supports only OpenFlow networks, the proposed solution leverages SDN-WISE to integrate WSNs and achieves unified system representation and treatment. The proposed system has been prototyped and is publicly available in open source. Experiments have been executed demonstrating that, by exploiting the proposed solution, it is also possible to improve efficiency of the overall network.
Speaker specific features in vowels

Speech communication is one of the fields of interest of Artificial Intelligence. Part of this process is the ability of the listener to recognize the speaker, a goal automatic speaker recognition (ASR) also tries to achieve. ASR can then be used in, for example, speaker verification systems for security purposes. To accomplish the goal of speaker recognition, information that is characteristic for a particular speaker needs to be recovered from the speech sound. However, speech sounds can differ in several ways: the content of the utterance, the situation in which it is said, and the speaker who expresses it. The last source of variation, the speaker, is the only one of interest to ASR. We were interested in the features in speech sounds that are speaker specific and can therefore be used to automatically discriminate between speakers. Formants are speaker specific and we developed a method to resolve formants from a the cochleogram of a speech sound. For three vowels we investigated whether particular characteristics of their formants are speaker specific and we found this is true for most of the characteristics.
GLP1‐RA Add‐on Therapy in Patients with Type 2 Diabetes Currently on a Bolus Containing Insulin Regimen

Adding glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) to basal insulin regimens has become a guideline‐recommended treatment option for uncontrolled type 2 diabetes. However, limited data exist to support the use of GLP‐1 RAs with insulin regimens, including bolus insulin in patients with type 2 diabetes. The primary objectives of this review were to identify if the combination of a GLP‐1 RA and an insulin regimen containing bolus insulin resulted in improvements in HbA1c, weight loss, reduction in insulin doses, and to evaluate the side effect profile of this combination in terms of nausea and hypoglycemia risk. Eight studies using exenatide twice/day, liraglutide, and dulaglutide were reviewed ranging in average duration of follow‐up from 3 to 15 months. Seven studies showed that addition of a GLP‐1 RA was associated with significant HbA1c reductions ranging from 0.4% to 1.64% from baseline to follow‐up. Patients in all eight studies had significant weight loss in the GLP‐1 RA arm from baseline to follow‐up ranging from 0.87 to 10.2 kg. In all the studies, total daily bolus insulin doses decreased 25–67% from baseline to follow‐up. In some studies, a portion of patients were able to discontinue bolus insulin all together after initiation of a GLP‐1 RA. In addition, in two randomized trials included in the review, the GLP‐1 RA arm showed significant improvement in HbA1c and weight compared with the control group who received basal/bolus regimens. Nausea was identified in 7–42% of participants using GLP‐1 RAs with insulin. Data support the use of GLP‐1 RAs added to insulin regimens already containing bolus insulin for glycemic control, weight loss, and reduction or discontinuation of bolus insulin.
Nonstandard Work Schedules, Family Dynamics, and Mother–Child Interactions During Early Childhood

The rising number of parents who work nonstandard schedules has led to a growing body of research concerned with what this trend means for children. The negative outcomes for children of parents who work nonstandard schedules are thought to arise from the disruptions these schedules place on family life, and thus, the types of parenting that support their children’s development, particularly when children are young. Using a nationally representative sample of two-parent families (Early Childhood Longitudinal Study–Birth cohort, n = 3,650), this study examined whether mothers’ and their partners’ nonstandard work schedules were associated with mothers’ parenting when children were 2 and 4 years old. Structural equation models revealed that mothers’ and their partners’ nonstandard work schedules were associated with mothers’ lower scores on measures of positive and involved parenting. These associations were mediated by fathers’ lower levels of participation in cognitively supportive parenting and greater imbalance in cognitively supportive tasks conducted by mothers versus fathers.
Purchase prediction using Tmall‐specific features

Historical user activity, such as online shopping recommendations, content personalization, and advertising clicking rates, is a critical component of building user profiles to predict purchases and preferences. Alongside the rapid development of e‐commerce, purchase prediction has become an increasingly important consideration for a wide variety of retail platforms. This paper proposes a framework which combines machine learning methods with a threshold‐moving approach to predict sets of pairs (user id and brand id,) in terms of whether a certain brand is purchased by a specified user according to his or her historical activity records. Three specific feature groups are extracted: click features, purchase features, and collect‐and‐cart features using a dataset from Tmall, a Chinese business‐to‐consumer online retail platform. Next, seven user purchase prediction experiments with different combinations of the three feature groups are conducted, and the purchase prediction performance is observed. Results showed that a combination of all three feature groups, with 27 features in total, provided valuable purchase prediction contributions and performed favorably. Also, three feature groups are proved to be relative independent, and our prediction model identified the most important feature, from the collect‐and‐cart feature group. Detailed result analysis validated the effectiveness of both the extracted features and proposed machine learning methods. Copyright © 2016 John Wiley & Sons, Ltd.
Improvements in health-related quality of life, cardio-metabolic health, and fitness in postmenopausal women after a supervised, multicomponent, adapted exercise program in a suited health promotion intervention: a multigroup study

Objectives: This study explored the multidimensional outcomes that resulted from the adherence to regular exercise among previously sedentary postmenopausal women. The exercise was managed through a supervised, multicomponent, adapted approximately 20-week program in a suited health promotion intervention. Methods: A multigroup, mixed-design study with between-group (intervention, sedentary, and active women) and within-subject measures (baseline, postintervention, and 3- and 12-month follow-ups) was conducted using intention-to-treat methodology. The Cervantes Scale assessed health-related quality of life (HRQoL), and several indicators of cardio-metabolic status and fitness were also assessed. Results: After the intervention, the participants experienced positive changes in short and long-term physical and mental health, with significant enhancements in several HRQoL dimensions, particularly mental well-being and menopause-related health and subdomains. Improvements were maintained or continued (eg, mental well-being) throughout the period, leading up to the 12-month follow-up. These outcomes were accompanied by significant improvements in cardio-metabolic status and fitness, including weight, body mass index, cardio-respiratory fitness, and flexibility. Conclusions: Our findings parallel previous empirical evidence showing the benefits associated with regular exercise, and add evidence to the association of positive outcomes in HRQoL with improvements in cardio-metabolic health and fitness status after the adoption of an active lifestyle.
Towards Pertinent Characteristics of Agility and Agile Practices for Software Processes

Context: It is believed that agility in software processes can bring benefits to software development and lead to an economy of efforts when accommodating changes is needed. Objective: Assess pertinence and relevance of agility characteristics and agile practices for software processes. Method: From 18 agility characteristics and 17 agile practices applicable to software processes revealed through systematic literature reviews performed in 2010, a survey was conducted to assess their pertinence and relevance. Results: 16 agility characteristics and 15 agile practices were considered pertinent to insert agility in software processes. Conclusion: Results should be used sparingly. It would be interesting to replicate the study in other contexts, with different subjects, and compare them, to increase the generalization of their results.
Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning

Learning-based color enhancement approaches typically learn to map from input images to retouched images. Most of existing methods require expensive pairs of input-retouched images or produce results in a non-interpretable way. In this paper, we present a deep reinforcement learning (DRL) based method for color enhancement to explicitly model the step-wise nature of human retouching process. We cast a color enhancement process as a Markov Decision Process where actions are defined as global color adjustment operations. Then we train our agent to learn the optimal global enhancement sequence of the actions. In addition, we present a 'distort-and-recover' training scheme which only requires high-quality reference images for training instead of input and retouched image pairs. Given high-quality reference images, we distort the images' color distribution and form distorted-reference image pairs for training. Through extensive experiments, we show that our method produces decent enhancement results and our DRL approach is more suitable for the 'distort-and-recover' training scheme than previous supervised approaches. Supplementary material and code are available at https://sites.google. com/view/distort-and-recover/
A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination

DNA melting analysis provides a rapid method for genotyping a target amplicon directly after PCR amplification. To transform melt genotyping into a broad-based profiling approach for heterogeneous samples, we previously proposed the integration of universal PCR and melt analysis with digital PCR. Here, we advanced this concept by developing a high-resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. Using synthetic DNA oligos with defined melting temperatures, we characterized sources of melting variability and minimized run-to-run variations. Within-run comparisons throughout a 20,000-reaction chip revealed that high-melting-temperature sequences were significantly less prone to melt variation. Further optimization using bacterial 16S amplicons revealed a strong dependence of the number of melting transitions on the heating rate during curve generation. These studies show that reliable high-resolution melt curve genotyping can be achieved in digital, picoliter-scale reactions and demonstrate that rate-dependent melt signatures may be useful for enhancing automated melt genotyping.
Current Status and Prospects for Cannabidiol Preparations as New Therapeutic Agents

States and the federal government are under growing pressure to legalize the use of cannabis products for medical purposes in the United States. Sixteen states have legalized (or decriminalized possession of) products high in cannabidiol (CBD) and with restricted ∆9‐tetrahydrocannabinol (∆9‐THC) content. In most of these states, the intent is for use in refractory epileptic seizures in children, but in a few states, the indications are broader. This review provides an overview of the pharmacology and toxicology of CBD; summarizes some of the regulatory, safety, and cultural issues relevant to the further exploitation of its antiepileptic or other pharmacologic activities; and assesses the current status and prospects for clinical development of CBD and CBD‐rich preparations for medical use in the United States. Unlike Δ9‐THC, CBD elicits its pharmacologic effects without exerting any significant intrinsic activity on the cannabinoid receptors, whose activation results in the psychotropic effects characteristic of Δ9‐THC, and CBD possesses several pharmacologic activities that give it a high potential for therapeutic use. CBD exhibits neuroprotective, antiepileptic, anxiolytic, antipsychotic, and antiinflammatory properties. In combination with Δ9‐THC, CBD has received regulatory approvals in several European countries and is currently under study in trials registered by the U.S. Food and Drug Administration in the United States. A number of states have passed legislation to allow for the use of CBD‐rich, limited Δ9‐THC–content preparations of cannabis for certain pathologic conditions. CBD is currently being studied in several clinical trials and is at different stages of clinical development for various medical indications. Judging from clinical findings reported so far, CBD and CBD‐enriched preparations have great potential utility, but uncertainties regarding sourcing, long‐term safety, abuse potential, and regulatory dilemmas remain.
Using MODIS cloud regimes to sort diagnostic signals of aerosol‐cloud‐precipitation interactions

Coincident multiyear measurements of aerosol, cloud, precipitation, and radiation at near‐global scales are analyzed to diagnose their apparent relationships as suggestive of interactions previously proposed based on theoretical, observational, and model constructs. Specifically, we examine whether differences in aerosol loading in separate observations go along with consistently different precipitation, cloud properties, and cloud radiative effects. Our analysis uses a cloud regime (CR) framework to dissect and sort the results. The CRs come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are defined as distinct groups of cloud systems with similar covariations of cloud top pressure and cloud optical thickness. Aerosol optical depth used as proxy for aerosol loading comes from two sources, MODIS observations and the MERRA‐2 reanalysis, and its variability is defined with respect to local seasonal climatologies. The choice of aerosol data set impacts our results substantially. We also find that the responses of the marine and continental component of a CR are frequently quite disparate. Overall, CRs dominated by warm clouds tend to exhibit less ambiguous signals but also have more uncertainty with regard to precipitation changes. Finally, we find weak, but occasionally systematic covariations of select meteorological indicators and aerosol, which serve as a sober reminder that ascribing changes in cloud and cloud‐affected variables solely to aerosol variations is precarious.
Carrying My Environment with Me: A Participatory-sensing Approach to Enhance Thermal Comfort

Commercial building is one of the major energy consumers that has drawn worldwide concerns. Heating, ventilating and air-conditioning (HVAC) system constitutes 40% of the total energy consumption in a typical commercial building. While the main objective of HVAC is to provide occupants with a comfort and safe environment, it currently lacks channels to recognize occupants' favourite temperatures as well as reflect their levels of comfort, e.g., too-cold or too-hot. Hence, it is hard to justify the energy consumption without considering end-user needs. Models of thermal comfort and predicted mean vote have been used to estimate such index, however, they are not widely adopted due to their complexity and inaccuracy. In this paper, we design the innovative system CarryEn, which first captures user's favourite temperature non-intrusively from their daily environment. We connect our system with the building management system (BMS), and optimize the setpoint temperature to occupants with our model. When the user moves into other rooms or buildings, his favourite setting will also be carried with him. Based on our experiments, CarryEn is able to achieve an improvement of 28.2% thermal satisfaction from occupants and save 13% of energy consumption.
The Role of Common Ontology in Achieving Sharable, Reusable Knowledge Bases

A solar cell has a heat collector bar with a heat absorbing material in contact therewith. The heat absorbing material includes a transparent web member having a plurality of capsule uniformly distributed therein with each capsules having a suspension of highly reflective, flake-like, field responsive particles therein. The particles may be preoriented with respect to incident electromagnetic radiation.
5‐Lipoxygenase gene disruption reduces amyloid‐β pathology in a mouse model of Alzheimer's disease

5‐Lipoxygenase (5LO), by producing leukotrienes, is a proinflammatory enzyme, and there is evidence suggesting that it is up‐regulated with aging and may be involved in Alzheimer's disease (AD). In this paper, we studied the effect of 5LO‐targeted gene disruption on the amyloid phenotype of a transgenic mouse model of AD, the Tg2576. Amyloid‐β (Aβ) deposition in the brains of Tg2576 mice lacking 5LO was reduced by 64–80% compared with Tg2576 controls. This reduction was associated with a similar significant decrease in Aβ levels measured by sandwich ELISA. Absence of 5LO did not induce any significant change in amyloid‐β precursor protein (APP) levels and processing, or Aβ catabolic pathways. Furthermore, in vitro studies showed that 5LO activation or 5LO metabolites increase, whereas 5LO inhibition decreases, Aβ formation, secondary to correspondent changes in γ‐secretase activity. These data establish for the first time a novel functional role for 5LO in the pathogen‐esis of AD‐like amyloidosis, thereby modulating γ‐secretase activity. Our work suggests that pharmacological inhibition of 5LO could provide a novel therapeutic tool for AD. Firuzi, O. , Zhuo, J., Chinnici, C. M. , Wisniewski, T. , Pratico, D. 5‐Lipoxygenase gene disruption reduces amyloid‐β pathology in a mouse model of Alzheimer's disease. FASEB J. 22, 1169–1178 (2008)
Analysis and Transformation of Textual Energy Distribution

In this paper we revisit the Textual Energy model. We deal with the two major disadvantages of the Textual Energy: the asymmetry of the distribution and the unbounded ness of the maximum value. Although this model has been successfully used in several NLP tasks like summarization, clustering and sentence compression, no correction of these problems has been proposed until now. Concerning the maximum value, we analyze the computation of Textual Energy matrix and we conclude that energy values are dominated by the lexical richness in quadratic growth of the vocabulary size. Using the Box-Cox transformation, we show empirical evidence that a log transformation could correct both problems.