Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Affective States via EEG and Deep Learning

Human emotions play a key role in numerous decision-making processes. The ability to correctly identify likes and dislikes as well as excitement and boredom would facilitate novel applications in neuromarketing, affective entertainment, virtual rehabilitation and forensic neuroscience that leverage on sub-conscious human affective states. In this neuroinformatics investigation, we seek to recognize human preferences and excitement passively through the use of electroencephalography (EEG) when a subject is presented with some 3D visual stimuli. Our approach employs the use of machine learning in the form of deep neural networks to classify brain signals acquired using a brain-computer interface (BCI). In the first part of our study, we attempt to improve upon our previous work, which has shown that EEG preference classification is possible although accuracy rates remain relatively low at 61%-67% using conventional deep learning neural architectures, where the challenge mainly lies in the accurate classification of unseen data from a cohort-wide sample that introduces inter-subject variability on top of the existing intra-subject variability. Such an approach is significantly more challenging and is known as subject-independent EEG classification as opposed to the more commonly adopted but more time-consuming and less general approach of subject-dependent EEG classification. In this new study, we employ deep networks that allow dropouts to occur in the architecture of the neural network. The results obtained through this simple feature modification achieved a classification accuracy of up to 79%. Therefore, this study has shown that the use of a deep learning classifier was able to achieve an increase in emotion classification accuracy of between 13% and 18% through the simple adoption of the use of dropouts compared to a conventional deep learner for EEG preference classification. In the second part of our study, users are exposed to a roller-coaster experience as the emotional stimuli which are expected to evoke the emotion of excitement, while simultaneously wearing virtual reality goggles, which delivers the virtual reality experience of excitement, and an EEG headset, acquires the raw brain signals detected when exposed to this excitement stimuli. Here, a deep learning approach is used to improve the excitement detection rate to well above the 90% accuracy level. In a prior similar study, the use of conventional machine learning approaches involving k-Nearest Neighbour (kNN) classifiers and Support Vector Machines (SVM) only achieved prediction accuracy rates of between 65% and 89%. Using a deep learning approach here, rates of 78%-96% were achieved. This demonstrates the superiority of adopting a deep learning approach over other machine learning approaches for detecting human excitement when immersed in an immersive virtual reality environment.
EVALUATING CONTINUOUS-TIME SLAM USING A PREDEFINED TRAJECTORYPROVIDED BY A ROBOTIC ARM

Abstract. Recently published approaches to SLAM algorithms process laser sensor measurements and output a map as a point cloud of the environment. Often the actual precision of the map remains unclear, since SLAMalgorithms apply local improvements to the resulting map. Unfortunately, it is not trivial to compare the performance of SLAMalgorithms objectively, especially without an accurate ground truth. This paper presents a novel benchmarking technique that allows to compare a precise map generated with an accurate ground truth trajectory to a map with a manipulated trajectory which was distorted by different forms of noise. The accurate ground truth is acquired by mounting a laser scanner on an industrial robotic arm. The robotic arm is moved on a predefined path while the position and orientation of the end-effector tool are monitored. During this process the 2D profile measurements of the laser scanner are recorded in six degrees of freedom and afterwards used to generate a precise point cloud of the test environment. For benchmarking, an offline continuous-time SLAM algorithm is subsequently applied to remove the inserted distortions. Finally, it is shown that the manipulated point cloud is reversible to its previous state and is slightly improved compared to the original version, since small errors that came into account by imprecise assumptions, sensor noise and calibration errors are removed as well.
Using Boyer to create a culture of scholarship: Outcomes from a faculty development program

Background: The current nursing faculty shortage has created an urgent need to retain and mentor novice faculty members. Programs to foster faculty development related to scholarship should be part of a school’s overall strategic plan. Methods: This article presents a description of a faculty development program with outcomes regarding scholarship for a five year period. The Boyer model of scholarship was used as a framework to assess resources and also served as a guide for program development. The program was tailored to faculty needs and targeted specific aspects of Boyer’s domains; strategies were developed which promoted faculty growth in these areas. Specific activities were planned and implemented to accomplish the overall outcome of increasing faculty scholarship and create a research culture in the school. Results: The scholarship of discovery was a major area identified for faculty growth. The program goal of increasing faculty scholarship was achieved by an increase in the number of presentations, publications and funded projects for the faculty in the school over a five year period. Conclusions: Using the Boyer model as a framework assisted faculty in achieving a more positive view of their scholarship, facilitated preparation of meaningful dossiers for promotion and tenure decisions, and contributed to knowledge development in the discipline.
eview management-oriented framework for selecting metrics used to ssess habitat-and path-specific quality in spatially structured opulations

Mobile species with complex spatial dynamics can be difficult to manage because their population distributions vary across space and time, and because the consequences of managing particular habitats are uncertain when evaluated at the level of the entire population. Metrics to assess the importance of habitats and pathways connecting habitats in a network are necessary to guide a variety of management decisions. Given the many metrics developed for spatially structured models, it can be challenging to select the most appropriate one for a particular decision. To guide the management of spatially structured populations, we define three classes of metrics describing habitat and pathway quality based on their data requirements (graph-based, occupancy-based, and demographic-based metrics) and synopsize the ecological literature relating to these classes. Applying the first steps of a formal decision-making
Thyroid Status in Diabetes Mellitus

The study was carried out to estimate thyroid hormones (T3, T4 and TSH) level in diabetic patients and to compare it with normal controls in an attempt to find out the importance of thyroid hormone estimation in diabetic cases. Sixty cases of diabetes mellitus who attended Diabetic Clinic, RIMS during the period from October 2008 to March 2010 were taken as the cases and 30 healthy individuals were selected as control group. Serum total Tri-iodothyronine (T3), thyroxine (T4), thyroid stimulating hormone and blood sugar were estimated in the cases and controls. The study showed that diabetes was more prevalent in the age group of 51-65 years, and more in males (52%). The mean fasting blood sugar (126.17 ± 37.92 mg%) and serum TSH level (4.58 ± 2.90 mIU/L)were increased significantly (r=0.884, p>0.05) whereas serumT4 level (5.79 ± 4.39 μg/dl) was decreased significantly in diabetic cases when compared with controls. Mean T3 level of diabetic cases was higher than controls but it was insignificant. Diabetes mellitus cases with statistically significant higher TSH value have complications like hypertension, retinopathy, nephropathy etc. A statistically significant, negative correlation (r=-0.942, p<0.05) was seen between Serum T4 and blood sugar in the cases. Therefore, routine assessment of thyroid hormone level in addition to other biochemical variables in the early stage of diabetes will help in the management of these patients.
Asymmetric E ↵ ects of Non-Pecuniary Signals on Search and Purchase Behavior for Energy-E � cient Durable Goods

We report the results of a field experiment where we exogenously vary the use of social comparison “nudges” and subsidies for participation in an in-home energy audit program, and follow subjects through to subsequent purchase of durable goods. We therefore can compare the causal e↵ect of financial incentives and nudges along two margins: audits, which we liken to search, and purchase of durables. Using data on nearly 100,000 households, we document an asymmetry: nudges increase audits, but lead to lower rates of purchase. We find no evidence of a di↵erential response for those o↵ered a financial incentive. These di↵erences suggest heterogeneity in the motives of the marginal consumer induced by nudges versus prices. JEL Codes: D01; Q41; D83
Significant Underestimation in the Optically Based Estimation of the Aerosol First Indirect Effect Induced by the Aerosol Swelling Effect

Aerosol optical quantities have been widely employed as the proxy variables of cloud condensation nuclei concentration to study aerosol indirect effects (AIE). Due to the aerosol swelling effect, cloud condensation nuclei and aerosol optical quantities do not vary in harmony, leading to a bias in the estimation of the AIE. To identify and quantify this artifact, we employ extensive measurements of aerosol and cloud properties made at four sites in different continents that have distinct aerosol properties in terms of size and composition. One‐unit enhancement in aerosol scattering coefficient by swelling effect is found to lead to a systematic underestimation of the first indirect effect (FIE) by about 23% that can result in an underestimation in the FIE‐related radiative forcing by several W/m2 depending on aerosol properties and relative humidity. This likely contributes significantly to the systematic difference between satellite‐based estimates of the FIE and those simulated by general circulation models. Recommendations are made to make more sound comparisons of the AIE estimated from observations and model simulations.
Detecting Metagame Shifts in League of Legends Using Unsupervised Learning

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Complete chloroplast genome sequences contribute to plant species delimitation: A case study of the Anemopaegma species complex.

PREMISE OF THE STUDY Bignoniaceae is an important component of neotropical forests and a model for evolutionary and biogeographical studies. A previous combination of molecular markers and morphological traits improved the phylogeny of the group. Here we demonstrate the value of next-generation sequencing (NGS) to assemble the chloroplast genome of eight Anemopaegma species and solve taxonomic problems. METHODS Three NGS platforms were used to sequence total DNA of Anemopaegma species. After genome assembly and annotation, we compared chloroplast genomes within Anemopaegma, with other Lamiales species, and the evolutionary rates of protein-coding genes using Tanaecium tetragonolobum as the outgroup. Phylogenetic analyses of Anemopaegma with different data sets were performed. KEY RESULTS Chloroplast genomes of Anemopaegma species ranged from 167,413 bp in A. foetidum to 168,987 bp in A. acutifolium ("typical" form). They exhibited a characteristic quadripartite structure with a large single-copy region (75,070-75,761 bp), a small single-copy region (12,766-12,817 bp) and a pair of inverted repeat regions (IRs) (39,480-40,481) encoding an identical set of 112 genes. An inversion of a fragment with ca. 8 kb, located in the IRs and containing the genes trnI-AAU, ycf2, and trnL-CAA, was observed in these chloroplast genomes when compared with those of other Lamiales. CONCLUSIONS Anemopaegma species have the largest genomes within the Lamiales possibly due to the large amount of repetitive sequences and IR expansion. Variation was higher in coding regions than in noncoding regions, and some genes were identified as markers for differentiation between species. The use of the entire chloroplast genome gave better phylogenetic resolution of the taxonomic groups. We found that two forms of A. acutifolium result from different maternal lineages.
Does Marijuana Contribute to Intimate Partner Aggression? Temporal Effects in a Community Sample of Marijuana-Using Couples.

OBJECTIVE Although patterns of marijuana use are positively associated with intimate partner aggression, there is little evidence that episodes of marijuana use contribute to the occurrence of episodes of relationship conflict and aggression. The present ecological momentary assessment study considered the temporal relationship between marijuana use episodes and the occurrence of conflict, verbal aggression, and physical aggression between intimate partners in the next 2 hours. METHOD A sample of 183 cohabiting marijuana-using couples (ages 18-30) were recruited from the community. For 30 consecutive days, each partner independently reported episodes of marijuana use and partner conflict, including verbal and physical aggression perpetration and victimization within conflicts. Temporal associations between each partner's marijuana use and subsequent conflict and aggression were examined using the Actor-Partner Interdependence Model. Analyses accounted for between-person effects of marijuana use frequency and total conflicts. RESULTS We observed temporal effects of actor (but not partner) marijuana use on men's and women's reports of conflict and verbal aggression perpetration and victimization within 2 hours of use. Marijuana use episodes did not alter the likelihood of physical aggression in the next 2 hours. Partner concordance in marijuana use had no effect on verbal or physical aggression or victimization. The positive temporal effects of marijuana on conflict and verbal aggression remained significant after accounting for the effect of drinking episodes. CONCLUSIONS Within generally concordant, marijuana-using young couples, marijuana use episodes contribute to the occurrence of relationship conflict and verbal aggression.
Theoretical estimation of Photons flow rate Production in quark gluon interaction at high energies

photons emitted from higher energetic collisions in quark-gluon system have been theoretical studied depending on color quantum theory. A simple model for photons emission at quark-gluon system have been investigated. In this model, we use a quantum consideration which enhances to describing the quark system. The photons current rate are estimation for two system at different fugacity coefficient. We discussion the behavior of photons rate and quark gluon system properties in different photons energies with Boltzmann model. The photons rate depending on anisotropic coefficient : strong constant, photons energy, color number, fugacity parameter, thermal energy and critical energy of system are also discussed.
A Robust Longitudinal Control Strategy of Platoons Under Model Uncertainties and Time Delays

Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
State Abstractions for Lifelong Reinforcement Learning

In lifelong reinforcement learning, agents must effectively transfer knowledge across tasks while simultaneously addressing exploration, credit assignment, and generalization. State abstraction can help overcome these hurdles by compressing the representation used by an agent, thereby reducing the computational and statistical burdens of learning. To this end, we here develop theory to compute and use state abstractions in lifelong reinforcement learning. We introduce two new classes of abstractions: (1) transitive state abstractions, whose optimal form can be computed efﬁ-ciently, and (2) PAC state abstractions, which are guaranteed to hold with respect to a distribution of tasks. We show that the joint family of transitive PAC abstractions can be acquired efﬁciently, preserve near optimal-behavior, and experimentally reduce sample complexity in simple domains, thereby yielding a family of desirable abstractions for use in lifelong reinforcement learning. Along with these positive results, we show that there are pathological cases where state abstractions can negatively impact performance.
Lost Photons Won’t Derail Quantum Sampling

A race is on to build a quantum computer that solves difficult problems much faster than a classical computer—a milestone dubbed quantum supremacy [1]. Runners in this race, however, are faced with a hazy finish line, which can move closer as quantum machines and algorithms improve or further away as their classical counterparts catch up. An experiment led by Jian-Wei Pan of the University of Science and Technology in China [2] nudges the racers forward for now. Inspired by a theoretical proposal, the researchers confirmed that a promising method for demonstrating quantum supremacy, known as boson sampling with photons (Fig. 1), produces useful output even as photons leak from the system. This means that researchers don’t have to “toss away” the output of a sampling experiment when photons are lost, as was previously assumed [3], allowing for faster computations and bringing a demonstration of quantum supremacy closer to reality.
Arterial tourniquets

A tourniquet is a device which is used to control the flow of blood to and/or from an extremity. The word tourniquet itself derives from the French verb tourner (to turn) and was first used by the eighteenth-century French surgeon Louis Petit describing the screw-like device he strapped to the thighs of patients undergoing leg amputations, to reduce blood loss. This article focuses on the use of arterial tourniquets. The arterial tourniquet is usually a pneumatic device consisting of an inflatable cuff connected to a compressed gas supply. The measurable and high pressures that such tourniquets can generate allow controlled arterial compression and distal circulatory stasis. The most common use for such tourniquets is in surgical procedures on the extremities, where the tourniquet is of great value in optimizing operating conditions by creating a bloodless surgical field. Examples include joint replacements and the repair of tendons, nerves, and blood vessels.
TAPP Adaptors Control B Cell Metabolism by Modulating the Phosphatidylinositol 3-Kinase Signaling Pathway: A Novel Regulatory Circuit Preventing Autoimmunity

Class I PI3K enzymes play critical roles in B cell activation by phosphorylating plasma membrane lipids to generate two distinct phosphoinositide (PI) products, PI(3,4,5)P3 and PI(3,4)P2. These PIs each bind distinct but overlapping sets of intracellular proteins that control cell survival, cytoskeletal reorganization, and metabolic activity. The tandem PH domain containing proteins (TAPPs) bind with high specificity to PI(3,4)P2, and their genetic uncoupling from PI(3,4)P2 in TAPP knock in (KI) mice was previously found to cause chronic B cell activation, abnormal germinal centers (GCs), and autoimmunity. In this article, we find that TAPPs provide feedback regulation affecting PI3K signaling and metabolic activation of B cells. Upon activation, TAPP KI B cells show enhanced metabolic activity associated with increased extracellular acidification rate, increased expression of glucose transporter GLUT1, and increased glucose uptake. TAPP KI B cells show markedly increased activation of the PI3K-regulated kinases Akt, GSK3β, and p70-S6K. Conversely, overexpression of the C-terminal TAPP PH domains in B cells can inhibit Akt phosphorylation by a mechanism requiring the TAPP PI(3,4)P2-binding pocket. Inhibition of the PI3K pathway in TAPP KI B cells reduced GLUT1 expression and glucose uptake, whereas inhibition of Akt alone was not sufficient to normalize these responses. TAPP KI GC B cells also show increased GLUT1 and glucose uptake, and treatment with the inhibitor of glycolysis 2-deoxy-D-glucose reduced chronic GC responses and autoantibody production within these mice. Our findings show that TAPP–PI(3,4)P2 interaction controls activation of glycolysis and highlights the significance of this pathway for B cell activation, GC responses, and autoimmunity.