Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Vehicle Collision Avoidance System

With one of the highest motorization growth rate in the world accompanied by rapid expansion in road network and urbanization over the years, our country is faced with serious impacts on road safety levels. Hence, detecting and recognizing moving vehicles in traffic scenes for traffic surveillance, traffic control, and road traffic information systems is an emerging research area for Intelligent Transportation Systems. Due to rapid increase in vehicles, there is exponential increase in accidents. Some costly cars had already implemented some in-built features (varying according to the company) of vehicle collision avoidance system but it's not affordable to every individual. Hence, this work is an attempt to develop a vehicle tracking system with multiple features in one system to deal with the severity of a collision to some extent for the common men. An important aspect of this collision avoidance system is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). It further has the provision of counting the cars, detecting the obstacles coming while driving with the measurement of its distance from the driven vehicle. In addition, tracking of number plate of vehicle is also possible to figure out the vehicle causing accidents.
Reliability Estimations of Large Circuits in Massively-Parallel GPU-SPICE

SPICE simulations for reliability have special requirements. We present GPU-SPICE to serve these special requirements. First, our GPU-SPICE employs the massive parallelism found in GPUs to enable circuit simulations beyond $200K$ transistors. This is necessary to study reliability in micro-architecture components (e.g., multipliers, adders), as reliability estimations require full analogue SPICE simulations (instead of STA or other heuristics). Secondly, our GPU-SPICE can update transistor parameters during the circuit simulation, a feature necessary to model reliability degradation, which constantly reacts to circuit activity (e.g., Bias Temperature Instability reacting to $V_{gs}$ changes by increasing/decreasing $\Delta V_{th}$ in each transistor). Lastly, our GPU-SPICE is open-source software, this ensures that it easily can be employed, adapted and extended by other researchers. Due to the massive parallelism in a GPU and performance optimizations (convergence criteria, CUDA memory management, etc.), our GPU-SPICE is up to 218x faster than its single-threaded baseline NGSPICE.
Deep Extraction of Cropland Parcels from Very High-Resolution Remotely Sensed Imagery

Extracting cropland parcels from high resolution remote sensing images is a basic task for precision agriculture and other fields. Object based image analysis rely heavily on segmentation methods and can't satisfy the parcels' requisition in most situation. Inspired by the recent remarkable improvement on image understanding with deep learning, we propose a deep-edge guided method for cropland parcels extraction. Focus on the boundaries of these parcels, hard edge and soft edge are extracted respectively with U-Net and RCF model. Then all edges with the land type of cropland are constructed into parcels. At last accurate cropland-parcels are achieved.
Compartmental Spatial Multi-Patch Deterministic and Stochastic Models for Dengue

Dengue is a vector-borne viral disease increasing dramatically over the past years due to improvement in human mobility. The movement of host individuals between and within the patches are captured via a residence-time matrix. A system of ordinary differential equations (ODEs) modeling the spatial spread of disease among the multiple patches is used to create a system of stochastic differential equations (SDEs). Numerical solutions of the system of SDEs are compared with the deterministic solutions obtained via ODEs.
The Hidden Burden of Fractures in People Living With HIV

The survival of people living with human immunodeficiency virus (HIV) has increased markedly since the advent of antiretroviral therapy (ART). However, other morbidities have emerged, including osteoporosis. The estimated incidence of fractures at any site in people living with HIV ranges from 0.1 per 1000 person‐years to 8.4 per 1000 person‐years: at least twice that of people without HIV. This increased risk seems to be related to HIV itself and its treatment. Risk factors for bone disease in HIV‐positive (HIV+) subjects include both classical risk factors for osteoporosis and fracture and factors linked to HIV itself, such as inflammation, reconstitution syndrome, low CD4, ART, and co‐infection with hepatitis B and C viruses. The risk of fractures in these individuals can be at least partially assessed by measurement of BMD and the Fracture Risk Assessment Tool (FRAX™). Only alendronate and zoledronic acid have been studied in HIV+ individuals; both show beneficial effects on BMD, although data on fracture reduction are not available. © 2018 The Authors. JBMR Plus Published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
On the Performance of UAV-enabled Multihop V2V FSO systems over generalized α -μ Channels

In this paper, a cooperative multi-hop vehicle-to-vehicle communication over free-space optics (FSO) medium is examined, where unmanned aerial vehicles act as relay nodes. Outage performance of FSO cooperative system is analyzed by deriving the closed-form expressions, where atmospheric turbulence was defined by a generalized α -μ statistical model. Moreover, results on the bit error rate (BER) performance over various distributions are obtained. In addition, it is shown that the BER over Rayleigh fading outperforms the ones over Weibull and Nakagami fading channels when a two-relay FSO system is taken into consideration. Finally, it is presented that the BER with the detect-and-forward mode has better performance in contrast to the BER with amplify-and-forward mode when three and four hop scenarios are considered.
Ranking of Influential users based on User-Tweet bipartite graph

Today, even if we are still geographically situated, but real-time news and trends are able to reach us no matter how far we are or how huge is the time difference. This is due to Social Networks Services and the strong involvement of people to them through posting, sharing and interacting online with information. Therefore, we propose a study on patterns of information diffusion on Twitter during global scale events. We also introduce a method for identifying influencers and quantify popularity of Tweets on the Network based on a user-tweet bipartite graph.
Optimal Bidding Strategy for a DER Aggregator in the Day-Ahead Market in the Presence of Demand Flexibility

The penetration of distributed energy resources (DER), including distributed generators, storage devices, and demand response (DR) is growing worldwide, encouraged by environmental policies and decreasing costs. To enable DER local integration, new energy players as aggregators appeared in the electricity markets. This player, acting toward the grid as one entity, can offer new services to the electricity market and the system operator by aggregating flexible DER involving both DR and generation resources. In this paper, an optimization model is provided for participation of a DER aggregator in the day-ahead market in the presence of demand flexibility. This player behaves as an energy aggregator, which manages energy and financial interactions between the market and DER organized in local energy systems (LES), which are in charge to satisfy the multienergy demand of a set of building clusters with flexible demand. A stochastic mixed-integer linear programming problem is formulated by considering uncertainties of intermittent DER facilities and day-ahead market price, to find the optimal bidding strategies while maximizing the expected aggregator's profit. Numerical results show that the method is efficient in finding the bidding curves in the day-ahead market through the optimal management of flexibility requests sent to clusters, as well as of DER in LES and interactions among LES.
Releasing The Requirement for Analog Cancellation in Wireless Full-Duplex Communications

In wireless full-duplex communications, analog cancellation is used mainly to reduce the self-interference signal to satisfy the dynamic range of the analog-to-digital converter (ADC). This is a critical requirement for hardware design, especially for the broadband self-interference signals, due to the lack of sufficiently precise analog devices and high implementation complexity. To release the requirement for analog cancellation, this paper proposes a novel method, called in-band delay and cancellation digitization (IBDCD), to break the bottleneck of the digital cancellation capacity by improving the signal-to-noise ratio of the ADC samples. The IBDCD method is composed of two loops, one suppresses the target signal in the analog domain and the other recovers the target signal in the digital domain. Experiments are performed and analyzed to verify the feasibility of IBDCD. The performance budget indicates that IBDCD can release the analog cancellation requirements in full-duplex applications.
Grid-like hexadirectional modulation of human entorhinal theta oscillations

Significance The entorhinal cortex plays a critical role in allowing organisms to navigate and represent spatial memories using grid-like representations. Given the importance of spatial processing for everyday life, we examined the patterns of neuronal oscillations in the entorhinal cortex, which are thought to underlie grid cells, in humans using intracranial recordings from neurosurgical patients as they navigated in virtual reality. We show that the power of entorhinal theta oscillations shows a distinctive six-way symmetric directional modulation, suggesting they correlate with grid representations, and that this signal correlates with spatial memory performance. Our findings thus show that the human entorhinal grid network is associated with theta oscillations and indicate that it is possible to measure properties of grid-like neural representations by recording oscillations. The entorhinal cortex contains a network of grid cells that play a fundamental part in the brain’s spatial system, supporting tasks such as path integration and spatial memory. In rodents, grid cells are thought to rely on network theta oscillations, but such signals are not evident in all species, challenging our understanding of the physiological basis of the grid network. We analyzed intracranial recordings from neurosurgical patients during virtual navigation to identify oscillatory characteristics of the human entorhinal grid network. The power of entorhinal theta oscillations showed six-fold modulation according to the virtual heading during navigation, which is a hypothesized signature of grid representations. Furthermore, modulation strength correlated with spatial memory performance. These results demonstrate the connection between theta oscillations and the human entorhinal grid network and show that features of grid-like neuronal representations can be identified from population electrophysiological recordings.
Parkinson’s Disease Motor Subtypes Show Different Responses to Long-Term Subthalamic Nucleus Stimulation

Background and purpose: Subthalamic nucleus deep brain stimulation (STN DBS) is well established for the treatment of advanced Parkinson’s disease (PD), substantially improving motor symptoms, quality of life, and reducing the long-term need for dopaminergic medication. However, whether chronic STN DBS produces different effects on PD motor subtypes is unknown. This retrospective study aimed to evaluate the long-term effects of STN DBS on the PD motor subtypes. Methods: Eighty patients undergoing STN DBS were included. The Unified Parkinson’s Disease Rating Scale (UPDRS) analysis was performed in “On” and “Off” medication/“On” and “Off” stimulation conditions. The patients were classified as akinetic-rigid type (ART), tremor-dominant type (TDT), and mixed type (MT) based on the preoperative UPDRS III subscores in the “Off” medication state. Preoperative and postoperative comparisons were performed. Results: After 4.9 years, STN DBS produced significant improvement in the UPDRS III total scores and subscores of tremor, rigidity, and bradykinesia in the “Off” medication state in the ART group, less improvement in the MT group, and the least improvement in the TDT group. The UPDRS II and III total scores and other subscores failed to improve during the “On” medication state. However, all groups improved substantially, and the improvement in tremor was sustained for both the “On” and “Off” medication states after years. Long-term STN DBS failed to improve swallowing and speech in all the subtypes. Conclusion: The data confirms that PD is heterogeneous. Long-term STN DBS produced the best effects on bradykinesia/rigidity in the “Off” medication state and on tremor in the “On” and “Off” medication states. There were differences in the response by each group, but some of the differences could be explained by the fact that more severe symptoms at baseline tend to have greater improvement. The findings support the idea that ART mainly involves the basal ganglia-thalamo-cortical pathway, whereas TDT involves a different circuit, likely the cerebellar-thalamo-cortical pathway.
Channel AoA estimation for massive MIMO systems using one-bit ADCs

Although massive multiple-input multiple-output (MIMO) can enhance the overall system performance significantly, it could suffer from high cost and power consumption issues due to using a large number of radio frequency (RF) chains. Two different approaches are commonly exploited to overcome these issues. The first approach is using hybrid beamforming, which consists of analog and digital beamforming, to reduce the total number of RF chains. The second approach is adopting low-resolution analog-todigital converters (ADCs) for each RF chain. For both approaches, channel estimation becomes a difficult task. This paper addresses the problem of channel angle of arrival (AoA) estimation in massive MIMO using both hybrid beamforming and one-bit magnitudeaided (OMA) ADCs. An iterative algorithm is developed to estimate the channel AoA, and the appropriate threshold per iteration is analyzed. Numerical results show that the proposed technique can achieve sufficient AoA estimation performance with practical values of the signal-to-noise ratio (SNR).
Depressive Symptoms as Predictors of Alcohol Problem Domains and Reinforcement Among Heavy Drinking College Students

Heavy drinking among college students in the United States is common and results in a wide range of problems. Symptoms of depression are also common among college students and may exacerbate problems associated with heavy drinking, but to date most studies have been cross sectional and relied on an aggregate measure of alcohol problems. Further, depressive symptoms may also predict other elements of risk among heavy drinkers, including greater experience of substance-related reinforcement, and diminished experience of substance-free reinforcement. The current study examines depressive symptoms as a prospective predictor of changes in alcohol problem domains and reward variables in a sample of heavy drinking college students. Heavy drinking college students (N = 138) completed a survey assessing depressive symptoms, alcohol problems, and reinforcement at baseline and after a 12-month follow-up period. Multiple regressions examined the utility of depressive symptoms (DASS-21) in predicting alcohol problems, substance-related reinforcement, and substance-free reinforcement at the 12-month follow-up after controlling for baseline drinking level and the baseline level of the relevant outcome variable. Baseline depressive symptoms predicted 12-month alcohol problems related to impaired control (i.e., drinking more than planned), self-perception, and self-care. Depressive symptoms also predicted lower 12-month substance-free, but not substance-related, reinforcement. Finally, change in depressive symptoms was associated with total alcohol problems, impaired control, self-perception, self-care, academic/occupational, and physiological dependence problems at 12-month follow-up. Heavy drinkers with depressive symptoms may benefit from interventions targeting alcohol problems that also increase access to and engagement in rewarding alternative activities.
Facial Expression Analysis for Distress Detection

Emotions are an incredibly important aspect of human life and basic research on emotions of the past few decades has produced several discoveries that have led to important real world applications. Facial expressions project our true emotions to others and add the real intent to the words we say. The interpretation of such facial expressions exhibited by the subject in response to a situation is really useful for many applications in fields of medicine, E-learning, entertainment, monitoring, marketing, law and many more. This project focuses on determining the distress level of a person by analyzing his facial expressions. The reaction of a person to a particular communication scenario is recorded using a video or still camera under predefined lighting conditions and this input is taken and processed further to detect his emotion. The face and facial landmarks detection are done using Viola Jones algorithm. Facial patches active during an emotion elicitation are then extracted for texture analysis. The feature extraction method used here is Gray Level Difference Method (GLDM) in which texture features are derived from the GLDM probability density functions. The next step, classification is done using Naïve Bayes Classifier. With reference to the trained information, the emotion of the person is recognized and is used for determining his distress level. The proposed system is tested using Extended Cohn Kanade(CK+) and Japanese female facial expression (JAFFE) datasets.
Interaction between environmental and familial affective risk impacts psychosis admixture in states of affective dysregulation

Abstract Background Evidence suggests that cannabis use, childhood adversity, and urbanicity, in interaction with proxy measures of genetic risk, may facilitate onset of psychosis in the sense of early affective dysregulation becoming ‘complicated’ by, first, attenuated psychosis and, eventually, full-blown psychotic symptoms. Methods Data were derived from three waves of the second Netherlands Mental Health Survey and Incidence Study (NEMESIS-2). The impact of environmental risk factors (cannabis use, childhood adversity, and urbanicity) was analyzed across severity levels of psychopathology defined by the degree to which affective dysregulation was ‘complicated’ by low-grade psychotic experiences (‘attenuated psychosis’ – moderately severe) and, overt psychotic symptoms leading to help-seeking (‘clinical psychosis’ – most severe). Familial and non-familial strata were defined based on family history of (mostly) affective disorder and used as a proxy for genetic risk in models of family history × environmental risk interaction. Results In proxy gene–environment interaction analysis, childhood adversity and cannabis use, and to a lesser extent urbanicity, displayed greater-than-additive risk if there was also evidence of familial affective liability. In addition, the interaction contrast ratio grew progressively greater across severity levels of psychosis admixture (none, attenuated psychosis, clinical psychosis) complicating affective dysregulation. Conclusion Known environmental risks interact with familial evidence of affective liability in driving the level of psychosis admixture in states of early affective dysregulation in the general population, constituting an affective pathway to psychosis. There is interest in decomposing family history of affective liability into the environmental and genetic components that underlie the interactions as shown here.
Current and Emerging Reconstituted HDL-apoA-I and HDL-apoE Approaches to Treat Atherosclerosis

Atherosclerosis affects millions of people worldwide. However, the wide variety of limitations in the current therapeutic options leaves much to be desired in future lipid-lowering therapies. For example, although statins, which are the first-line treatment for coronary heart disease (CHD), reduce the risk of cardiovascular events in a large percentage of patients, they lead to optimal levels of low density lipoprotein-cholesterol (LDL-C) in only about one-third of patients. A new promising research direction against atherosclerosis aims to improve lipoprotein metabolism. Novel therapeutic approaches are being developed to increase the levels of functional high density lipoprotein (HDL) particles. This review aims to highlight the atheroprotective potential of the in vitro synthesized reconstituted HDL particles containing apolipoprotein E (apoE) as their sole apolipoprotein component (rHDL-apoE). For this purpose, we provide: (1) a summary of the atheroprotective properties of native plasma HDL and its apolipoprotein components, apolipoprotein A-I (apoA-I) and apoE; (2) an overview of the anti-atherogenic functions of rHDL-apoA-I and apoA-I-containing HDL, i.e., natural HDL isolated from transgenic Apoa1−/− × Apoe−/− mice overexpressing human apoA-I (HDL-apoA-I); and (3) the latest developments and therapeutic potential of HDL-apoE and rHDL-apoE. Novel rHDL formulations containing apoE could possibly present enhanced biological functions, leading to improved therapeutic efficacy against atherosclerosis.
Acquisition of analgesic properties by the cholecystokinin (CCK)/CCK2 receptor system within the amygdala in a persistent inflammatory pain condition

Abstract Pain is associated with negative emotions such as anxiety, but the underlying neurocircuitry and modulators of the association of pain and anxiety remain unclear. The neuropeptide cholecystokinin (CCK) has both pronociceptive and anxiogenic properties, so we explored the role of CCK in anxiety and nociception in the central amygdala (CeA), a key area in control of emotions and descending pain pathways. Local infusion of CCK into the CeA of control rats increased anxiety, as measured in the light–dark box test, but had no effect on mechanical sensitivity. By contrast, intra-CeA CCK infusion 4 days after Complete Freund's Adjuvant (CFA) injection into the hindpaw resulted in analgesia, but also in loss of its anxiogenic capacity. Inflammatory conditions induced changes in the CeA CCK signaling system with an increase of CCK immunoreactivity and a decrease in CCK1, but not CCK2, receptor mRNA. In CFA rats, patch-clamp experiments revealed that CCK infusion increased CeA neuron excitability. It also partially blocked the discharge of wide dynamic range neurons in the dorsal spinal cord. These effects of CCK on CeA and spinal neurons in CFA rats were mimicked by the specific CCK2 receptor agonist, gastrin. This analgesic effect was likely mediated by identified CeA neurons projecting to the periaqueductal gray matter that express CCK receptors. Together, our data demonstrate that intra-CeA CCK infusion activated a descending CCK2 receptor-dependent pathway that inhibited spinal neuron discharge. Thus, persistent pain induces a functional switch to a newly identified analgesic capacity of CCK in the amygdala, indicating central emotion-related circuit controls pain transmission in spinal cord.