Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of different incentive models in free-floating carsharing systems: A case study in Milan

In recent years, carsharing and especially free-floating carsharing (FFCS) concepts have gained popularity, especially in urban areas. In FFCS systems, customers can pick up and drop off vehicles anywhere in a defined business area. While these systems offer a maximum level of flexibility for the customer, the operator is confronted with spatiotemporal demand asymmetries. In order to offer a reliable mobility product, the operator has to assure a certain level of availability by relocating vehicles. As relocations are associated with costs, the FFCS operator has introduced a special incentive system in Milan. The operating area is two-stage, i.e. it consists of a central business area and a peripheral business area. As the peripheral area is less attractive, the operator offers a bonus minute incentive for cars picked up in the peripheral area and returned in the central area. The second incentive (reduced rates) has no restrictions on where to return the vehicle. The goal of this study is to evaluate and compare the effectiveness of the two different incentives regarding the potential to substitute operator-based transfers. To this end, we analyzed more than 600,000 app calls and more than 80,000 rentals. First, we partitioned the operating area into zones to evaluate their attractiveness. In a second step, the success of the different offer types was assessed by analyzing the influence of incentives on the idle times and comparing the vehicle distribution before and after the rental. As a result, we can confirm that the vehicle distribution is improved which means that customers can be influenced to return a car in a predefined area. The idle time after an incentive is placed is significantly lower than the average idle time in the peripheral area. A definite price sensitivity of the customers cannot be stated with the examined data.
GPU-Accelerated Tabu Search Algorithm for Dial-A-Ride Problem

Dial-a-ride problems consist in designing efficient vehicle routes for door-to-door passenger transportation while ensuring time-windows, maximum user ride time limits, and maximum route duration limits. Much progress has been made on algorithms to solve DARP. However, many of these algorithms are computationally complex, and often too slow for real-world scenarios. To address this issue, this paper presents a GPU-accelerated tabu search algorithm (G-TS) for the dial-a-ride problem (DARP), in which three new GPU kernels are introduced: (i) parallel construction heuristic, (ii) parallel neighborhood generation, and (iii) parallel neighborhood evaluation. These kernels are designed carefully to reduce CPU-GPU communication latency while maximizing overall speedup. Numerical experiments have been carried out on various standard DARP instances to investigate the performance and convergence of G-TS on an Nvidia Tesla P100 GPU. It is observed that the proposed G-TS algorithm performs 31 times faster than single-core CPU-based tabu search imnlementation on Iarge-scale DARP instances.
The Effect of Kyolic Aged Garlic Extract on Gut Microbiota, Inflammation, and Cardiovascular Markers in Hypertensives: The GarGIC Trial

Background: Previous research suggests Kyolic-aged-garlic-extract to be effective in reducing blood pressure in a large proportion of hypertensive patients similar to first-line standard antihypertensive medication. High blood pressure has been linked to gut dysbiosis, with a significant decrease in microbial richness and diversity in hypertensives compared to normotensives. Furthermore, gut dysbiosis has been associated with increased inflammatory status and risk of cardiovascular events. Objective: To assess the effect of Kyolic aged GARlic extract on Gut microbiota, Inflammation, and Cardiovascular markers, including blood pressure, pulse wave velocity and arterial stiffness. Methods: A total of 49 participants with uncontrolled hypertension completed a double-blind randomized placebo-controlled trial of 12-weeks, investigating the effect of daily intake of aged-garlic-extract (1.2 g containing 1.2 mg S-allylcysteine) or placebo on blood pressure, pulse wave velocity and arterial stiffness, inflammatory markers, and gut microbiota. Results: Mean blood pressure was significantly reduced by 10 ± 3.6 mmHg systolic and 5.4 ± 2.3 mmHg diastolic compared to placebo. Vitamin B12 status played a role in responsiveness to garlic on blood pressure in 17% of patients. Garlic significantly lowered central blood pressure, pulse pressure and arterial stiffness (p < 0.05). Trends observed in inflammatory markers TNF-α and IL-6 need to be confirmed in larger trials. Furthermore, aged-garlic-extract improved gut microbiota, evident by higher microbial richness and diversity with a marked increase in Lactobacillus and Clostridia species after 3 months of supplementation. Conclusions: Kyolic-aged-garlic-extract is effective in reducing blood pressure in patients with uncontrolled hypertension, and has the potential to improve arterial stiffness, inflammation, and gut microbial profile. Aged-garlic-extract is highly tolerable with a high safety profile as a stand-alone or adjunctive antihypertensive treatment, with multiple benefits for cardiovascular health. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12616000185460 (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx? id=370096)
BEHAVIORAL INTENTION TOWARDS APPLICATION-BASED SHORT-DISTANCE DELIVERY SERVICES ADOPTION IN INDONESIA

Although online shop is small in scale, this type of business gives a significant impact for the economy. In order to support online shop to develop, improvement in delivery and logistic system is needed. One of the improvements in these aspects is the emergence of application-based short-distance delivery services. Unfortunately, these applications have not been widely adapted by the online shops in Indonesia. Thus, this study tries to explore what factors can influence online shops’ decision to use these apps by implementing the unified theory of acceptance and use of technology (UTAUT). This study employed structural equation model to test the model and 5 hypotheses proposed, processed by Lisrel 8.8. The result shows that the ease of use aspect of the apps will only be relevant if it is deemed to be beneficial for the user. Moreover, the cognitive aspect also plays the role, as users tend to compare the acquired benefit with the cost incurred in using the app. As a conclusion, effort expectancy, performance expectancy and price value have a significant influence towards use behavior. On the other hand, social influence is not significantly influencing use behavior. Implications and limitations will also be discussed on this paper.
Audio-Visual Spatiotemporal Perceptual Training Enhances the P300 Component in Healthy Older Adults

In older adults, cognitive abilities, such as those associated with vision and hearing, generally decrease with age. According to several studies, audio-visual perceptual training can improve perceived competence regarding visual and auditory stimuli, suggesting that perceptual training is effective and beneficial. However, whether audio-visual perceptual training can induce far-transfer effects in other forms of untrained cognitive processing that are not directly trained in older adults remains unclear. In this study, the classic P300 component, a neurophysiological indicator of cognitive processing of a stimulus, was selected as an evaluation index of the training effect. We trained both young and older adults on the ability to judge the temporal and spatial consistency of visual and auditory stimuli. P300 amplitudes were significantly greater in the posttraining session than in the pretraining session in older adults (P = 0.001). However, perceptual training had no significant effect (P = 0.949) on the P300 component in young adults. Our results illustrate that audio-visual perceptual training can lead to far-transfer effects in healthy older adults. These findings highlight the robust malleability of the aging brain, and further provide evidence to motivate exploration to improve cognitive abilities in older adults.
Comparison of Time Sensitive Networking (TSN) and TTEthernet

Time-Triggered Ethernet (TTEthernet) is a time-critical network for industrial and avionics applications improved from the Avionics Full Duplex Switched Ethernet (AFDX). It is standardized by SAE AS6802 and satisfies strict timing transmission by use of a mixture infrastructure to support Time-Triggered (TT), Rate-Constraint (RC) and Best-Effort (BE) traffics. The key feature is a time-triggered communication paradigm according to off-line schedule table based on global synchronization. Three integration methods (preemption, timely block and shuffling) are used to resolve conflict when a TT message gets ready and a RC message is already in transmission. Besides several real-time Ethernet solutions, Time Sensitive Networking (TSN) shows great interests and attractions to satisfy the increasing and variable transmission requirements for automotive and aeronautical industry. TSN is comprised of several IEEE standards developed under IEEE 802.1 TSN Task Group, such as 802.1AS, 802.1Qav, 802.1Qat, 802.1BA, 802.1Qbv and 802.1Qbu. On top of SPQ (Static Priority Queuing) scheduling mechanism, with best effort traffics in parallel, TSN introduces guaranteed timing behavior with focus on the transportation for Control Data traffic (CDT) under Time Aware Shaper (TAS) and audio video streams under Credit-Based Shaper (CBS). This paper introduces the basic principles of TSN standard in details, compares it with the properties of the TTEthernet standard. In particular, we first investigate their clock synchronization algorithms and discuss their shaping and scheduling strategies for traffic types with individual timing requirements. Then we show the impact the hard real-time traffic has on soft real-time traffic and give the worst case end-to-end latency calculation. Finally, we determine whether TSN offers flexibility for the requirements of avionic.
Modulation of Item and Source Memory by Auditory Beat Stimulation: A Pilot Study With Intracranial EEG

Auditory beat stimulation is an upcoming technique for non-invasive brain stimulation. Its influence on mediotemporal regions and memory processes has not yet been thoroughly investigated. A recent study suggests that auditory beats are able to alter intracranial EEG (iEEG) power and phase synchronization. 5 Hz binaural beat stimulation increased temporo-lateral phase synchronization, while 5 Hz monaural beat stimulation decreased mediotemporal synchronization. Based on the relevance of phase synchronization for memory operations, we hypothesized that 5 Hz binaural beat stimulation enhances, while 5 Hz monaural beat stimulation decreases long-term memory performance. We analyzed data from presurgical epilepsy patients with implanted depth electrodes in the hippocampus and rhinal cortex. 5 Hz monaural and binaural beat vs. control stimulation was applied while patients performed an associative learning task involving item and source recognition. We evaluated behavioral effects for item (hits minus false alarms) and source memory (correct minus incorrect) and the impact of auditory beats on iEEG power, rhinal-hippocampal phase synchronization and inter-trial phase locking. A three-way repeated measures ANOVA (encoding/retrieval, item/source, monaural/binaural/control) revealed a main effect of stimulation (p = 0.03) and a linear effect in the expected direction: binaural > control > monaural (p = 0.036). Both monaural and binaural stimulation were associated with increased phase locking of 5 Hz oscillations within rhinal cortex. These phase locking increases, however, corresponded to reverse phase shifts. Our data suggest that binaural vs. monaural 5 Hz stimulation increases vs. decreases long-term memory performance. These behavioral effects appear to be related to reverse phase shifts within rhinal cortex.
Multi-Fleet Platoon Matching: A Game-Theoretic Approach

We consider the platoon matching problem for a set of trucks with the same origin, but different destinations. It is assumed that the vehicles benefit from traveling in a platoon for instance through reduced fuel consumption. The vehicles belong to different fleet owners and their strategic interaction is modeled as a non-cooperative game where the vehicle actions are their departure times. Each truck has a preferred departure time and its utility function is defined as the difference between its benefit from platooning and the cost of deviating from its preferred departure time. We show that the platoon matching game is an exact potential game. An algorithm based on best response dynamics is proposed for finding a Nash equilibrium of the game. At a Nash equilibrium, vehicles with the same departure time are matched to form a platoon. Finally, the total fuel reduction at the Nash equilibrium is studied and compared with that of a cooperative matching solution where a common utility function for all vehicles is optimized.
Scaffolding for a Robot That Learns Reactions to Dialogue Acts

A dialogue act (DA) represents the meaning of an utterance at the illocutionary force level (Austin 1962) such as questions, requests, and greetings. Since DAs take charge of the most fundamental part of communication, we believe that the elucidation of DA learning mechanism is important for cognitive science and artificial intelligence. The purpose of this study is to let a robot learn to estimate DAs and to make a response based on them and to verify that scaffolding takes place when people teach the robot. The experimental results demonstrated that participants who continued interaction for a sufficiently long time gave scaffolding for the robot.
Initial-Dip Existence and Estimation in Relation to DPF and Data Drift

Early de-oxygenation (initial dip) is an indicator of the primal cortical activity source in functional neuro-imaging. In this study, initial dip's existence and its estimation in relation to the differential pathlength factor (DPF) and data drift were investigated in detail. An efficient algorithm for estimation of drift in fNIRS data is proposed. The results favor the shifting of the fNIRS signal to a transformed coordinate system to infer correct information. Additionally, in this study, the effect of the DPF on initial dip was comprehensively analyzed. Four different cases of initial dip existence were treated, and the resultant characteristics of the hemodynamic response function (HRF) for DPF variation corresponding to particular near-infrared (NIR) wavelengths were summarized. A unique neuro-activation model and its iterative optimization solution that can estimate drift in fNIRS data and determine the best possible fit of HRF with free parameters were developed and herein proposed. The results were verified on simulated data sets. The algorithm is applied to free available datasets in addition to six healthy subjects those were experimented using fNIRS and observations and analysis regarding shape of HRF were summarized as well. A comparison with standard GLM is also discussed and effects of activity strength parameters have also been analyzed.
The Epidemiology and Pathogenesis of Kawasaki Disease

Epidemiologic and clinical features of Kawasaki Disease (KD) strongly support an infectious etiology. KD is worldwide, most prominently in Japan, Korea, and Taiwan, reflecting increased genetic susceptibility among Asian populations. In Hawaii, KD rates are 20-fold higher in Japanese ethnics than in Caucasians, intermediate in other ethnicities. The age distribution of KD, highest in children < 2 yo, lower in those < 6 months, is compatible with infection by a ubiquitous agent resulting in increasing immunity with age and with transplacental immunity, as with some classic viruses. The primarily winter-spring KD seasonality and well-documented Japanese epidemics with wave-like spread also support an infectious trigger. We hypothesize KD pathogenesis involves an RNA virus that usually causes asymptomatic infection but KD in a subset of genetically predisposed children. CD8 T cells, oligoclonal IgA, and upregulation of cytotoxic T cell and interferon pathway genes in the coronaries in fatal KD also support a viral etiology. Cytoplasmic inclusion bodies in ciliated bronchial epithelium identified by monoclonal antibodies made from oligoclonal IgA heavy chains also supports a viral etiology. Recent availability of “second generation” antibodies from KD peripheral blood plasmablasts may identify a specific viral antigen. Thus, we propose an unidentified (“new”) RNA virus infects bronchial epithelium usually causing asymptomatic infection but KD in a subset of genetically predisposed children. The agent persists in inclusion bodies, with intermittent respiratory shedding, entering the bloodstream via macrophages targeting coronaries. Antigen-specific IgA plasma cells and CD8 T cells respond but coronaries can be damaged. IVIG may include antibody against the agent. Post infection, 97–99% of KD patients are immune to the agent, protected against recurrence. The agent can spread either from those with asymptomatic primary infection in winter-spring or from a previously infected contact who intermittently sheds the agent.
Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization

We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags. Compared to appearance features, the semantic layout of a scene is generally more invariant to appearance variations. We use this intuition and propose a novel end-to-end deep attention-based framework that utilizes multimodal cues to generate robust embeddings for 2D-VL. The proposed attention module predicts a shared channel attention and modality-specific spatial attentions to guide the embeddings to focus on more reliable image regions. We evaluate our model against state-of-the-art (SOTA) methods on three challenging localization datasets. We report an average (absolute) improvement of $19\%$ over current SOTA for 2D-VL. Furthermore, we present an extensive study demonstrating the contribution of each component of our model, showing $8$--$15\%$ and $4\%$ improvement from adding semantic information and our proposed attention module. We finally show the predicted attention maps to offer useful insights into our model.
Optimization of Multilayer Earth Structure by using Steepest Descent Method and Estimation of Transient Ground Potential Rise in Substation

The purpose of this paper is to analyse the impact of ground potential rise in the design of substation. Investigation of soil structure in the substation site is the pre-requirement to design the substation. Number of layers, resistivity of each layer and thickness are the parameters to be estimated with the measured site data. By using RESAP module of CDEGS software optimizing the parameters of soil structure. Ground potential rise of substation to be computed when the fault current is injected in to the grounding grid in power frequency using MALZ module and HIFREQ module of CDEGS. Further discussed about the safest ground potential rise and the ways to improve the safest GPR.
Bayesian Layers: A Module for Neural Network Uncertainty

We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It extends neural network libraries with drop-in replacements for common layers. This enables composition via a unified abstraction over deterministic and stochastic functions and allows for scalability via the underlying system. These layers capture uncertainty over weights (Bayesian neural nets), pre-activation units (dropout), activations ("stochastic output layers"), or the function itself (Gaussian processes). They can also be reversible to propagate uncertainty from input to output. We include code examples for common architectures such as Bayesian LSTMs, deep GPs, and flow-based models. As demonstration, we fit a 5-billion parameter "Bayesian Transformer" on 512 TPUv2 cores for uncertainty in machine translation and a Bayesian dynamics model for model-based planning. Finally, we show how Bayesian Layers can be used within the Edward2 probabilistic programming language for probabilistic programs with stochastic processes.
SIKAP PEKERJA SOSIAL TERHADAP PEREMPUAN KORBAN PERKOSAAN

Penelitian bertujuan menggambarkan sikap pekerja sosial terhadap perempuan korban perkosaan dan menguji faktor-faktor yang berasosiasi dengan sikap tersebut. Responden adalah 73 pekerja sosial yang disampel secara non-random. Data diperoleh melalui kuesioner yang didistribusikan pada akhir tahun 2016 dan kemudian dianalisa menggunakan teknik statistik simple regressions. Penelitian menemukan bahwa sikap pekerja sosial terhadap perempuan korban perkosaan belum dapat dikatakan konsisten, dibuktikan dengan beragamnya respon baik yang bersifat positif, negatif dan atau ambigu terhadap korban perkosaan. Jenis kelamin dan sikap terhadap peran jender merupakan 2 (dua) predictor yang signifikan. Pekerja sosial perempuan dan pekerja sosial yang menunjukkan dukungan terhadap kesetaraan peran laki-laki dan perempuan dalam masyarakat lebih cenderung melaporkan sikap yang positif terhadap korban. Studi ini memberikan kontribusi untuk mempersempit kesenjangan pengetahuan tentang sikap pekerja sosial terhadap kekerasan seksual terhadap perempuan.Kata kunci: Indonesia; pekerja sosial; sikap terhadap korban perkosaan
Potential Mechanisms for the Ketamine-Induced Reduction of P3b Amplitudes

In specific dosages, the N-methyl-D-aspartate receptor (NMDA) antagonist ketamine can be used to model transient psychotic symptoms in healthy individuals that resemble those of schizophrenia. Ketamine administration also temporarily impairs cognitive functions, which can be studied by event-related potentials (ERPs). ERPs also allow dissecting what stages of information processing are affected by ketamine and what stages remain functional. For tasks requiring the differentiation of targets and non-targets, it has repeatedly been shown that ketamine administration in healthy individuals leads to decreased amplitudes of the ERP component P3b in response to target stimuli. However, it could be argued that this ketamine-induced P3b reduction is the consequence of an increased difficulty to differentiate targets from non-targets, primarily mediated by ketamine's psychotomimetic rather than pharmacological effects. The current review of ERP studies seeks to clarify the issue whether P3b effects of ketamine may indeed be explained as the consequence of an experienced increase in task difficulty or whether alternative mechanisms are perhaps more plausible. The review first summarizes the effects of task difficulty on ERP components related to intentional stimulus categorization (P3b), involuntary attention switches to distractors (P3a), as well as sensory processing (P1, N1). Secondly, the ERP effects of task difficulty are contrasted with those observed in ketamine studies in healthy individuals. Findings show that P3b amplitudes are consistently diminished by an increased task difficulty, as well as after ketamine administration. In contrast and as most important difference, increased task difficulty leads to increased P3a amplitudes to distractors presented in same modality as targets, whereas ketamine leads to reduced P3a amplitudes for such distractors. This dissociation indicates that the decreased P3b amplitudes after ketamine cannot be explained by a drug-induced increase in task difficulty. The conjoint reductions of P3a and P3b amplitudes instead suggest that working memory operations, in particular working memory updating are impaired after ketamine, which is in line with previous behavioral findings.