Miracle-dz/UniFAR-FTUs huggingface dataset:
Antagonistic Effect of a Cytoplasmic Domain on the Basal Activity of Polymodal Potassium Channels

TREK/TRAAK channels are polymodal K+ channels that convert very diverse stimuli, including bioactive lipids, mechanical stretch and temperature, into electrical signals. The nature of the structural changes that regulate their activity remains an open question. Here, we show that a cytoplasmic domain (the proximal C-ter domain, pCt) exerts antagonistic effects in TREK1 and TRAAK. In basal conditions, pCt favors activity in TREK1 whereas it impairs TRAAK activity. Using the conformation-dependent binding of fluoxetine, we show that TREK1 and TRAAK conformations at rest are different, and under the influence of pCt. Finally, we show that depleting PIP2 in live cells has a more pronounced inhibitory effect on TREK1 than on TRAAK. This differential regulation of TREK1 and TRAAK is related to a previously unrecognized PIP2-binding site (R329, R330, and R331) present within TREK1 pCt, but not in TRAAK pCt. Collectively, these new data point out pCt as a major regulatory domain of these channels and suggest that the binding of PIP2 to the pCt of TREK1 results in the stabilization of the conductive conformation in basal conditions.
Implementation of the Sound Classification Module on the Platform with Limited Resources

There is a growing trend of using algorithms based on deep and machine learning in consumer devices, which imposes a challenge to the system's development because of the limited amount of resources in an embedded device. This paper presents integration of the sound classification module based on machine learning into a home audio system. The additional module enables dynamic change of processing controls according to the resulting confidence scores which indicate whether the current audio is speech, music or background noise. Main challenge of this paper is overcoming real-time processing constraints and embedded system's resource limitations. Results show that the sound classification module has been successfully integrated and produces the correct output.
Clickbait Pattern Detection and Classification of News Headlines Using Natural Language Processing

The advent of online news outlets and the recent explosion in number of users who consume news using this medium has led to several websites competing to grab users' attention. This has led to outlets creating creative ways to lure to reader to their website. One of the most widely used techniques is employing “clickbait” headlines. These headlines are specifically designed to pique the reader's interest in what is promised when the article is clicked on. The article usually does not deliver the content the reader is looking for. In this work we aim to create a model to detect such headlines. We analyse what constitutes as clickbait and extract features which may be indicative of clickbait. The features extracted from the article are both syntactic and semantic and encompass the variability that is found in such headlines. Finally we build a classifier that can distinguish between actual news and what is likely to be clickbait. We use multiple classification algorithms and weigh the advantages presented by them.
IoT and Microservices Based Testbed for Connected Car Services

The Internet of Things (IoT) has accelerated the innovations in the connected car domain. Together with Intelligent Transportation Systems (ITS), the IoT technologies envision unleashing efficient and more sustainable transportation systems. A connected car is already considered as an IoT resource whose capabilities can be exposed through an IoT platform, but few testbeds provide full-stack, cloud-based environment for connected car services experimentation. This paper formulates several research and engineering challenges for developing such testbed. Our proposed testbed exploits the IoT and microservices to provide advanced functionalities such as (i) the integration of heterogeneous sensors and vehicular communication technologies in a complete end-to-end stack and (ii) an Edge Server for vehicular data annotation, local processing with actuation. The testbed exposes the vehicular resources securely through a Cloud based framework and open APIs. They are supported by The Web of Things (WoT) based interoperable descriptions of vehicular devices and interactions among relevant actors. Finally, the Cloud system deploys the connected car services using microservices philosophy. In addition to that, we present a functional IoT architecture of the testbed, describe its operational phases, novel aspects, prototyping and experimentation details.
A Novel Monitoring System for Fall Detection in Older People

Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user’s privacy, which constitutes an additional advantage of this alternative.
Sleep Duration and Excessive Daytime Sleepiness Are Associated with Obesity Independent of Diet and Physical Activity

In the European Union, Czech Republic ranks 3rd and 6th for the incidence of obesity and cardiovascular diseases, respectively. Worldwide, short sleep duration and excessive daytime sleepiness (EDS) characterize obese subjects, which in turn exhibit scarce physical activity and unhealthy diet. We aimed to understand the relationship between irregular sleep patterns, obesity and lifestyle factors, such as diet and physical activity, in a vulnerable Czech population. 1482 members of the Kardiovize cohort, a random sample of the Czech urban population, were included in a cross-sectional study. Exposure variables included self-reported sleep duration and EDS, assessed by the Epworth Sleepiness Scale. Primary outcomes were BMI and waist-to-hip ratio or prevalence of obesity and central obesity. Covariates included physical activity and diet. Associations and interactions between variables were evaluated using logistic regression analyses. After adjustment for covariates, short sleep duration (<7 h) was associated with greater odds of overweight (BMI > 25; OR = 1.42; 95%CI = 1.06–1.90; p = 0.020) and obesity (BMI > 30; OR = 1.40; 95%CI = 1.02–1.94; p = 0.047), while EDS was associated with greater odds of central obesity (OR = 1.72; 95%CI = 1.06–2.79; p = 0.030), independent of diet and physical activity. However, due to the cross-sectional nature of our study, further prospective, large-scale studies are needed to evaluate the etiological link and causality between sleep disturbances and obesity.
A Novel Finger Vein Recognition System Based on Enhanced Maximum Curvature Points

Finger vein recognition is a biometric method of authentication that offers high security, efficiency and stability. In this paper we propose a new finger vein recognition system that utilizes the Enhanced Maximum Curvature Points (EMC) technique for finger vein pattern extraction and introduces a new pre-processing stage. In addition, it combines two matching methods, leading to better recognition performance in terms of EER, FAR, FRR and recognition rate than other methods. We present the experimental results obtained by applying our system on the databases SDUMLA-HMT, Tsingua, FV-USM and HKPU and compare them with similar approaches applied on these databases.
Experimental Validation of Design Concepts for Future EV-Traction Inverters

Numerous ideas to improve traction inverters for future electric vehicles were presented in literature. However, fair experimental comparisons of these concepts are rare. In this work, three highly-relevant concepts for traction systems are compared experimentally to a reference 2-level silicon IGBT inverter with a dc-link voltage of 400V. First, the dc-link voltage is increased from 400V to 800V. Secondly, the replacement of silicon IGBTs with silicon carbide MOSFETs. Thirdly, the usage of a three-level inverter instead of two-levels. To ensure a fair comparison, four prototypes with a nominal power of 80kW were constructed with fully use of carry-over parts and tested on two different test benches. The comparison is carried out regarding efficiency, total harmonic distortion, electromagnetic emission and technology readiness.
The Parameterized Complexity of the k-Biclique Problem

Given a graph G and an integer k, the k-Biclique problem asks whether G contains a complete bipartite subgraph with k vertices on each side. Whether there is an f(k) ċ |G|O(1)-time algorithm, solving k-Biclique for some computable function f has been a longstanding open problem. We show that k-Biclique is W[1]-hard, which implies that such an f(k) ċ |G|O(1)-time algorithm does not exist under the hypothesis W[1] ≠ FPT from parameterized complexity theory. To prove this result, we give a reduction which, for every n-vertex graph G and small integer k, constructs a bipartite graph H = (L⊍ R,E) in time polynomial in n such that if G contains a clique with k vertices, then there are k(k − 1)/2 vertices in L with nθ(1/k) common neighbors; otherwise, any k(k − 1)/2 vertices in L have at most (k+1)! common neighbors. An additional feature of this reduction is that it creates a gap on the right side of the biclique. Such a gap might have further applications in proving hardness of approximation results. Assuming a randomized version of Exponential Time Hypothesis, we establish an f(k) ċ |G|o(√k)-time lower bound for k-Biclique for any computable function f. Combining our result with the work of Bulatov and Marx [2014], we obtain a dichotomy classification of the parameterized complexity of cardinality constraint satisfaction problems.
Downstaging and Survival Outcomes Associated With Neoadjuvant Chemotherapy Regimens Among Patients Treated With Cystectomy for Muscle-Invasive Bladder Cancer

Importance Neoadjuvant chemotherapy (NAC) followed by radical cystectomy improves survival compared with cystectomy alone for patients with bladder cancer. Although gemcitabine with cisplatin has become a standard NAC regimen, a dose-dense combination of methotrexate, vinblastine, doxorubicin, and cisplatin (ddMVAC) is being adopted at some institutions. Objective To assess the association of neoadjuvant ddMVAC vs standard regimens with downstaging and overall survival among patients treated with radical cystectomy for bladder cancer. Design, Setting, and Participants Cross-sectional analysis of data extracted from the medical records of a consecutive sample, after exclusions, of 1113 patients with bladder cancer of whom 824 had disease stage T2 or greater, who were treated with cystectomy at the Moffitt Cancer Center in Tampa, Florida, a tertiary care cancer center, between January 1, 2007, and May 31, 2017. Data were collected between November 14, 2016, and July 21, 2017, and analyzed between August 21, 2017, and December 8, 2017. Patients were compared based on type of NAC. Those who did not receive NAC were included as controls. Main Outcomes and Measures Comparative rates and the association of any downstaging, complete response, and overall survival with ddMVAC and other NAC regimens and surgery alone. Outcomes were examined using Kaplan-Meier, adjusted logistic, Cox regression, and propensity-weighted models. Results Of the 1113 patients who underwent cystectomy for bladder cancer, 861 (77.4%) were male, the median (interquartile range) age was 67 (60-74) years, 1051 (94.4%) were white, 27 (2.4%) black, 37 (3.3%) Hispanic/Latino, and 35 (3.1%) other race/ethnicity. Of 824 patients with muscle-invasive bladder cancer, 332 (40%) received NAC. Downstaging rates were 52.2% for ddMVAC, 41.3% for gemcitabine-cisplatin, and 27.0% for gemcitabine with carboplatin, and complete response (pT0N0) rates were 41.3% for ddMVAC, 24.5% for gemcitabine-cisplatin, and 9.4% for gemcitabine-carboplatin (2-sided P < .001). Adjusted analysis comparing ddMVAC with gemcitabine-cisplatin demonstrated a higher likelihood of downstaging (odds ratio [OR], 1.84; 95% CI, 1.10-3.09) and complete response (OR, 2.67; 95% CI, 1.50-4.77) with ddMVAC. Similar results were achieved with propensity score matching (OR, 1.52; 95% CI, 0.99-2.35). Patients who received ddMVAC had better overall survival than those treated with other chemotherapy regimens, although the observed survival benefit did not reach statistical significance in adjusted or propensity-matched models (hazard ratio, 0.44; 95% CI, 0.14-1.38; P = .16). Conclusions and Relevance This study suggest that neoadjuvant ddMVAC followed by cystectomy is associated with a higher complete response (ypT0N0) rate than standard NAC. These data highlight and suggest the need to further investigate ddMVAC vs standard NAC in a prospective, randomized fashion.
A Self-Calibration Scheme for TDD Massive MIMO with Imperfect Channel Estimation

Exploiting channel reciprocity, time-division-duplexing (TDD) operated massive multiple-input multiple-output (MIMO) systems are able to acquire the channel state information with a reasonable overhead of channel estimation. However, in practical scenarios, the imperfections in channel reciprocity can significantly degrade the system performance. In this work, we propose a novel self calibration scheme for the maximum ratio transmission in TDD multi-user massive MIMO systems to compensate for the imperfect channel reciprocity, with considerations of imperfect channel estimation. The proposed scheme shows the greater robustness to a compound effect of channel reciprocity error and channel estimation error, compared with the traditional self calibration scheme that is widely used in massive MIMO systems.
Provably and Roubst Blind Source Seperation of Ill-Conditioned Hyperspectral Mixtures

Given a hyperspectral image of a scene, unsupervised linear hyperspectral unmixing (HU) is a blind source separation problem aimed at identifying the spectral signatures of the materials present in the scene, termed endmembers, and, for each pixel, the abundances of each material. HU is often very challenging, namely owing to the high correlation between the spectral signatures that is commonly observed in real hyperspectral scenes. However, such ill-conditioning is often ignored in the design of existing HU methods. To the best of our knowledge, existing preconditioning techniques rely on the pure-pixel assumption, which is, however, often violated in practical scenarios. In this paper, we propose a new theoretical framework wherein a provable preconditioning method is developed without requiring the presence of pure pixels. Our approach is to identify John’s maximum-volume data-inscribed ellipsoid, followed by a specific affine transformation that maps the John’s ellipsoid into an Euclidean ball. Provably, such transformation converts the original hyperspectral vectors into a preconditioned dataset whose endmember matrix is semi-unitary, which is exploited as regularization information. Furthermore, this transformation is robust to noise and largely simplifies the ensuing unmixing/optimization procedure. Our numerical experiments provide evidence of the superior unmixing performance of the proposed method.
Saccade Landing Point Prediction: A Novel Approach based on Recurrent Neural Networks

A saccade is a fast eye movement that allows the change of visual fixation from one object of interest to another. These movements are characterized by very high angular velocity peaks that can reach up to 1,000º/s, making them as one of the fastest neuromotor activities in the human body. Modeling such a complex movement remains a challenge. Saccadic eye movements can be defined by initial and landing points, duration, amplitude, and velocity profile. The landing point is important as it defines the new fixation region and, therefore, the region of interest of the viewer. Its prediction may reduce problems caused by display-update latency in gaze-contingent systems that make real-time changes in the display based on eye tracking. The main contribution of this work is to propose the use of state-of-the-art machine learning techniques (i.e., Recurrent Neural Networks) for saccade landing point prediction in real-world scenarios. Our method was evaluated using 220,000 saccades from 75 subjects acquired during viewing video from "Hollywood" movies. The results obtained using our proposed methods outperform existing approaches with improvements of up to 40% error reduction. Our results show that dynamic temporal relationships exploited by Recurrent Neural Networks can improve the performance of traditional Feed Forward Neural Networks.
Sparse Data-Based Urban Road Travel Speed Prediction Using Probabilistic Principal Component Analysis

In this paper, we propose a data-driven model for predicting the travel speed of urban roads, based on GPS trajectories of vehicles. Though this is a strategically important task in many traffic monitoring systems, the problem has not yet been well-solved given the following two challenges. The first is the effective modeling approach that can predict the urban road travel speed faced with the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in some time slots. Second, the traffic condition influences the travel speed on a road but it is hard to capture the pattern as it fluctuates irregularly. To address these problems, we propose to utilize the probabilistic principal component analysis-based model to predict the urban road travel speed, which can handle the problem of data sparsity. In addition, to improve the prediction performance of the probabilistic generative model, we incorporate the traffic condition to partition roads into clusters using a spectral clustering method. Implementing prediction on each cluster brings smaller traffic condition variability within clusters and make it capable of parallel computing. We evaluate our proposed method in a case study for the citywide road network of Shanghai, using GPS trajectories generated by over 13000 taxis over a period of one month. Empirical results demonstrate that the model outperforms the competing methods in terms of both effectiveness and robustness.
Transferability of Spatial Maps: Augmented Versus Virtual Reality Training

Work space simulations help trainees acquire skills necessary to perform their tasks efficiently without disrupting the workflow, forgetting important steps during a procedure, or the location of important information. This training can be conducted in Augmented and Virtual Reality (AR, VR) to enhance its effectiveness and speed. When the skills are transferred to the actual application, it is referred to as positive training transfer. However, thus far, it is unclear which training, AR or VR, achieves better results in terms of positive training transfer. We compare the effectiveness of AR and VR for spatial memory training in a control-room scenario, where users have to memorize the location of buttons and information displays in their surroundings. We conducted a within-subject study with 16 participants and evaluated the impact the training had on short-term and long-term memory. Results of our study show that VR outperformed AR when tested in the same medium after the training. In a memory transfer test conducted two days later AR outperformed VR. Our findings have implications on the design of future training scenarios and applications.
Biasing for Zero Distortion: Using the EKV\/BSIM6 Expressions

The EKV/BSIM6 model is well suited to find the optimum biasing point of a metal-oxide-semiconductor transistor (MOST) for zero third-order intermodulation IM<sub>3</sub> distortion. This article provides a derivation of a simple expression for this biasing point, for all possible regions of operation, i.e., the weak inversion (wi), the strong inversion (si), the velocity saturation (vsat), and all their crossover regions. Because the expressions are relatively simple, this overview provides, at the same time, a set of EKV/BSIM6 model expressions to be used for design and educational purposes.
Advanced RF Interferometry Structure for Improving Operation Range

In this letter, a novel structure combining low coherence interferometry (LCI) and microwave photonics (MWP) is proposed. In this case, the electro-optic modulator is strategically placed inside the interferometric structure in order to modify the behavior of the whole MWP-LCI system. Specifically, limiting effects which are inherently generated as the DC term, the self-reflection term and the carrier suppression effect are avoided. This fact enables the improvement of the MWP-LCI operating range in comparison to previous proposals. Moreover, a detailed description of the MWP-LCI structure is addressed in this letter to support the corresponding experimental demonstration and theoretical development. In addition, typical LCI capabilities such as penetration depth, resolution, and sensitivity are also measured. For the MWP-LCI structure proposed here, a 1 cm penetration depth with a resolution of $120~\mu \text{m}$ and a sensitivity beyond 50 dB are achieved.
Customized Instructions for Protection Against Memory Integrity Attacks

Customized instructions have typically been used for enhancing the performance of embedded systems. However, the use of finding dedicated instructions for security has been rather limited. On the contrary, modern processors are crippled by the threats of memory integrity attacks, which typically target the control flow of a program and are mitigated at the software level. In this letter, we analyze the memory exploitation codes being developed as a part of the Cyber Security Awareness Week-2016 competition, which are based on unsecured memcpy and return address modification by buffer overflow on OpenRISC and RISC-V architectures, and implement protections at the hardware level. We added eight new instructions to handle the four exploits by designing dedicated hardware stack and a module for checking against buffer overflow. We have also performed a validation on RISC-V platform and introduced two new custom instructions to ensure security from unbounded memcpy. The proposed countermeasures and the new instructions are validated on field programmable gate array platform.