Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-Driven Convolutional Recurrent Models of the Visual System

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models of temporally-averaged responses of neurons in the primate brain's visual system. However, biological visual systems have two ubiquitous architectural features not shared with typical CNNs: local recurrence within cortical areas, and long-range feedback from downstream areas to upstream areas. Here we explored the role of recurrence in improving classification performance. We found that standard forms of recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the ImageNet task. In contrast, novel cells that incorporated two structural features, bypassing and gating, were able to boost task accuracy substantially. We extended these design principles in an automated search over thousands of model architectures, which identified novel local recurrent cells and long-range feedback connections useful for object recognition. Moreover, these task-optimized ConvRNNs matched the dynamics of neural activity in the primate visual system better than feedforward networks, suggesting a role for the brain's recurrent connections in performing difficult visual behaviors.
Interferer Rejection in Cognitive Radio Receiver Using Heterodyne Conversion and Active Feedback

Limited linearity and local oscillator (LO) harmonics mixing are two major issues that ultra-wideband receivers, and a cognitive radio (CR) in particular, are dealing with. Direct-conversion schemes, based on current-driven passive mixers, have shown to improve the linearity but unable to resolve the LO harmonic mixing problem. They are usually limited to third and fifth harmonics rejection or require a very complex and power hungry circuitry for a higher number of harmonics. This paper presents a heterodyne conversion scheme in the 180-nm CMOS technology for CR applications (54–862 MHz band) that robustly mitigates the harmonic mixing issue for all the harmonics, while by employing an active feedback loop, a comparable state-of-the-art $\mathrm {IIP_{3}}$ of better than +10 dBm is achieved. Measurements show an average noise figure of 7.5 dB when the active feedback loop is OFF (i.e., in the absence of destructive interference) and of 15.5 dB when the feedback loop is active and a 0-dBm interferer is applied, respectively.
No Differential Effects of Two Different Alpha-Band Electrical Stimulation Protocols Over Fronto-Parietal Regions on Spatial Attention

In a previous study using transcranial alternating current stimulation (tACS), we found preliminary evidence that phase coherence in the alpha band (8–12 Hz) within the fronto-parietal network may critically support top-down control of spatial attention (van Schouwenburg et al., 2017). Specifically, synchronous alpha-band stimulation over the right frontal and parietal cortex (0° relative phase) was associated with changes in performance and fronto-parietal coherence during a spatial attention task as compared to sham stimulation. In the current study, we firstly aimed to replicate these findings with synchronous tACS. Second, we extended our previous protocol by adding a second tACS condition in which the right frontal and parietal cortex were stimulated in a desynchronous fashion (180° relative phase), to test the specificity of the changes observed in our previous study. Participants (n = 23) were tested in three different sessions in which they received either synchronous, desynchronous, or sham stimulation over the right frontal and parietal cortex. In contrast to our previous study, we found no spatially selective effects of stimulation on behavior or coherence in either stimulation protocol compared to sham. We highlight some of the differences in study design that may have contributed to this discrepancy in findings and more generally may determine the effectiveness of tACS.
Normal Electrical Activity of the Brain in Obsessive-Compulsive Patients After Anodal Stimulation of the Left Dorsolateral Prefrontal Cortex

Introduction: Transcranial Direct Current Stimulation (tDCS) has been used as a non-invasive method to increase the plasticity of brain. Growing evidence has shown several brain disorders such as depression, anxiety disorders, and chronic pain syndrome are improved following tDCS. In patients with Obsessive-Compulsive Disorder (OCD), increased brain rhythm activity particularly in the frontal lobe has been reported in several studies using Eectroencephalogram (EEG). To our knowledge, no research has been done on the effects of electrical stimulation on brain signals of patients with OCD. We measured the electrical activity of the brain using EEG in patients with OCD before and after tDCS and compared it to normal participants. Methods: Eight patients with OCD (3 males) and 8 matched healthy controls were recruited. A 64-channel EEG was used to record a 5-min resting state before and after application of tDCS in both groups. The intervention of tDCS was applied for 15 minutes with 2 mA amplitude where anode was placed on the left Dorsolateral Prefrontal Cortex (DLPFC) and cathode on the right DLPFC. Results: In line with previous studies, the results showed that the power of Delta frequency band in OCD patients are significantly higher than the normal group. Following anodal tDCS, hyperactivity in Delta and Theta bands declined in most channels, particularly in DLPFC (F3, F4) and became similar to normal signals pattern. The reduction in Delta band was significantly more than the other bands. Conclusion: Anodal tDCS over the left DLPFC significantly decreased the power of frequency bands of Delta and Theta in Patients with OCD. The pattern of EEG activity after tDCS became particularly similar to normal, so tDCS may have potential clinical application in these patients.
On the Development of an Autonomous Agent for a 3D First-Person Shooter Game Using Deep Reinforcement Learning

First-Person Shooter games have always been very popular. One of the challenges in the development of First-Person Shooter games is the use of game agents controlled by Artificial Intelligence because they can learn how to handle very distinct situations presented to them. In this work, we construct an autonomous agent to play different scenarios in a 3D First-Person Shooter game using a Deep Neural Network model. The agent receives as input only the pixels of the screen and should learn how to interact with the environments by itself. To achieve this goal, the agent is trained using a Deep Reinforcement Learning model through an adaptation of the Q-Learning technique for Deep Networks. We evaluate our agent in three distinct scenarios: a basic environment against one static enemy, a more complex environment against multiple different enemies and a custom medikit gathering scenario. We show that the agent achieves good results and learns complex behaviors in all tested environments. The results show that the presented model is suitable for creating 3D First-Person Shooter autonomous agents capable of playing different scenarios.
On the Tradeoff Between Accuracy and Complexity in Blind Detection of Polar Codes

Polar codes are a recent family of error-correcting codes with a number of desirable characteristics. Their disruptive nature is illustrated by their rapid adoption in the 5th-generation mobile-communication standard, where they are used to protect control messages. In this work, we describe a two-stage system tasked with identifying the location of control messages that consists of a detection and selection stage followed by a decoding one. The first stage spurs the need for polar-code detection algorithms with variable effort to balance complexity between the two stages. We illustrate this idea of variable effort for multiple detection algorithms aimed at the first stage. We propose three novel blind detection methods based on belief-propagation decoding inspired by early-stopping criteria. Then we show how their reliability improves with the number of decoding iterations to highlight the possible tradeoffs between accuracy and complexity. Additionally, we show similar tradeoffs for a detection method from previous work. In a setup where only one block encoded with the polar code of interest is present among many other blocks, our results notably show that, depending on the complexity budget, a variable number of undesirable blocks can be dismissed while achieving a missed-detection rate in line with the block-error rate of a complex decoding algorithm.
Solving the face growth problem in the biometrie face recognition using Photo-Anthropometric ratios by iris normalization

Over the last years, facial landmarks techniques were the first and main approach to solve biometric facial recognition and they are still capable of achieving great results in controlled environments. However, there are still open problems to be solved, such as how to deal with twins, scale variation and the face growth. In this work, we propose a new method based on measured values (ratios) from facial cephalometric landmarks, which uses an iris size as a normalization factor to solve the influence of face scale (face growth) effect and improving Equal Error Rates (EER) scores for a facial recognition system in specifics scenarios under 5%.
Compact Sequential Feeding Network With Quadruple Output Ports and Its Application for Wideband Circularly Polarized Antenna

A compact single-layer series-type feeding network is proposed by utilizing two dual-band frequency-independent couplers in this paper. The overall size of the feeding network is further reduced by meandering the transmission lines. The proposed feeding network is quite suitable in the design of wideband circularly polarized antenna due to its wideband performance, identical amplitude and flat 90° successive shifts between two arbitrary adjacent ports. Based this, a wideband circularly polarized antenna for satellite application (L band and S band) is presented, fabricated, and measured employing the proposed feeding network. Four feeding strips are introduced to enhance the axial ratio (AR) bandwidth of the proposed antenna. The simulated AR bandwidth extends from 14.7% to 34.5% by loading the four feeding strips. The measured results indicate that the proposed antenna has numerous advantages, such as low-cost, low-profile ( $0.07\lambda _{0}$ ), wideband, high gain, wide AR bandwidth, and excellent circularly polarized feature. Note that the measurements agree reasonably well with the simulations.
Dynamic Modeling and Adaptive Sliding Mode Control for a Maglev Train System Based on a Magnetic Flux Observer

The control method and dynamic performance of a magnetic suspension system, which is the core component of maglev trains, have a significant influence on the performance of the maglev train. Currently, a control strategy based on the current feedback is widely used. However, the stable range of control parameters is relatively small, making it difficult to identify stable regions for the control parameters. In addition, the strong interactions between the control parameters are cause issues in the control. In this paper, a control strategy based on the flux density observer is proposed using an analysis of the working principle and the structure of the suspension system. A nonlinear dynamic model of a maglev system is established by utilizing the state equation of the flux feedback. Based on the current and voltage feedback, a hybrid magnetic flux density observer is presented. According to the mathematical model, an adaptive sliding mode controller is designed to reduce the upper bound of both uncertainty and interference of the sliding mode controller. Finally, a theoretical analysis and the effectiveness of the proposed control strategy are verified using both simulations and experiments.
Det-LB: A Load Balancing Approach in 802.11 Wireless Networks for Industrial Soft Real-Time Applications

Wireless control systems for industrial automation have been gaining popularity in recent years owing to the low cost of their components and their ease of deployment. Wireless local area network (WLAN) is a good candidate for industrial wireless applications, especially for soft real-time control systems. However, unbalanced traffic loads in areas that are covered by several access points (APs) could cause significant performance degradation, and the problem would be more serious in industrial contexts that have high requirements for timeliness and reliability. In this paper, we propose a deterministic load balancing algorithm (Det-LB) for WLAN, which is based on game theory, mainly intended for soft real-time control systems. The procedure of the proposed algorithm is described and analysed in detail. To validate the goodness of the proposed approach, we compare Det-LB with three other load balancing strategies (RLF, MMF, and DLBA) in a simulation. The results show that Det-LB can achieve better load balancing performance and improve network performance significantly. For instance, in a common scenario in which five APs are deployed, the RLF scheme has terrible performance and cannot be applied to industrial applications at all; Det-LB has 35% and 26% performance improvements over MMF in 500- and 250-byte payload size conditions, respectively, and has a 5% deadline miss performance boost and better packet loss performance compared with DLBA when the APs tend to be overloaded.
potted fever group rickettsiae detected in immature stages of ticks arasitizing on Iberian endemic lizard Lacerta schreiberi Bedriaga

Spotted fever rickettsioses are tick-borne diseases of growing public health concern. The prevalence of rickettsia-infected ticks and their ability to parasitize humans significantly influence the risk of human infection. Altogether 466 Ixodes ricinus ticks (428 nymphs and 38 larvae) collected from 73 Lacerta schreiberi lizards were examined by PCR targeting the citrate synthetase gene gltA for the presence of Rickettsia spp. Rickettsial DNA was detected in 47% of nymphs and 31.6% of larvae. They were subsequently subjected to a second PCR reaction using primers derived from the outer membrane protein rOmpA encoding gene (ompA) to detect spotted fever group rickettsiae (SFG). This analysis shows that 41.4% of nymphs and 7.9% of larvae collected from the lizards contain DNA of SFG rickettsiae. Sequencing
Benefits of Adjuvant Mitotane after Resection of Adrenocortical Carcinoma: A Systematic Review and Meta-Analysis

Background The adjuvant use of mitotane on adrenocortical carcinoma (ACC) has always been in controversy. We aimed to assess the prognostic benefits of adjuvant mitotane after resection of ACC in patients without distant metastasis. Methods The PubMed, WoS, Embase, and Cochrane Library databases were systematically searched. Recurrence-free survival (RFS) and overall survival (OS) were adopted as measurements. A meta-analysis was conducted based on hazard ratio (HR) with 95% confidence interval (CI). A study was included only if the enrolled patients underwent resection of ACC without adjuvant chemotherapy except mitotane. Results A total of 5 retrospective studies reporting on 1249 patients were included for this meta-analysis. The meta-analysis showed that adjuvant mitotane was significantly associated with prolonged RFS (HR = 0.62; 95%CI, 0.42-0.94; P < 0.05) and prolonged OS (HR = 0.69; 95%CI, 0.55-0.88, P < 0.05). Conclusion After comprehensive review, current evidence suggests that adjuvant mitotane significantly decreases the recurrence rate and mortality after resection of ACC in patients without distant metastasis, but these findings need further demonstration from prospective controlled trials.
Leveraging Smartphones for Vehicle Lane-Level Localization on Highways

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing works of tracking vehicles on lane-level mostly depend on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphones to sense driving conditions for vehicle lane-level localization on highways. By analyzing driving traces collected from real driving environments, we find that each type of lane-change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a <italic>Lane-Level Localization</italic> (<inline-formula><tex-math notation="LaTeX">$L^3$</tex-math><alternatives> <inline-graphic xlink:href="yu-ieq1-2776286.gif"/></alternatives></inline-formula>) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane-change behaviors. Then, a <italic>Finite State Machine </italic> is employed to track vehicles on lane-level leveraging the patterns. Extensive experiments demonstrate that <inline-formula><tex-math notation="LaTeX">$L^3$</tex-math><alternatives> <inline-graphic xlink:href="yu-ieq2-2776286.gif"/></alternatives></inline-formula> is accurate and robust in real driving environments. The experimental results show that, on average, <inline-formula><tex-math notation="LaTeX">$L^3$ </tex-math><alternatives><inline-graphic xlink:href="yu-ieq3-2776286.gif"/></alternatives></inline-formula> achieves the accuracy of 91.49 percent on lane change detection and 90.31 percent on lane-level localization.
PRNU-based detection of morphed face images

In the recent past, face recognition systems have been found to be highly vulnerable to attacks based on morphed biometrie samples. Such attacks pose a severe security threat to biometric recognition systems across various applications. Apart from some algorithms, which have been reported to reveal practical detection performance on small in-house datasets, approaches to effectively detect morphed face images of high quality have remained elusive. In this paper, we propose a morph detection algorithm based on an analysis of photo response non-uniformity (PRNU). It is based on a spectral analysis of the variations within the PRNU caused by the morphing process. On a comprehensive database of 961 bona fide and 2,414 morphed face images practical performance in terms of detection equal error rate (D-EER) is achieved. Additionally, the robustness of the proposed morph detection algorithm towards different post-processing procedures, e.g. histogram equalization or sharpening, is assessed.
Event-driven cooperative-based Internet-of-Things (IoT) system

This paper presents the development of an Internet-of-Things (IoT) Cooperative System (IoT-CS) based on local Event-Driven response. After introducing IoT basic concepts and most common computational schemes (with particular attention on the differences between Cloud, Fog, and Cooperative computing paradigms), an innovative scheme for IoT will be presented. Thanks to the introduction and development of several intermediate layers between the sensor network and the Cloud, the hereby proposed system allows overcoming most of the problems of the state-of-the-art paradigms. In order to validate the proposed solution the most relevant aspects of this implementations will be presented.
Fractional Wavelet Scattering Network and Applications

Objective: This study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network. Methods: In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators. The feature vectors constructed by fractional scattering coefficients are usually used for signal classification. In this paper, an application example of the FrScatNet is provided in order to assess its performance on pathological images. First, the FrScatNet extracts feature vectors from patches of the original histological images under different orders. Then we classify those patches into target (benign or malignant) and background groups. And the FrScatNet property is analyzed by comparing error rates computed from different fractional orders, respectively. Based on the above pathological image classification, a gland segmentation algorithm is proposed by combining the boundary information and the gland location. Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is improved in fractional scattering domain. We also compare the FrScatNet-based gland segmentation method with those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method achieves comparable results. Conclusion: The FrScatNet is shown to achieve accurate and robust results. More stable and discriminative fractional scattering coefficients are obtained by the FrScatNet in this paper. Significance: The added fractional order parameter is able to analyze the image in the fractional scattering domain.
Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network

Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The aim of the present study was to screen critical differentially expressed genes (DEGs) involved in asthma in children. Gene expression in different tissues was compared between asthmatic children and healthy control subjects in order to identify DEGs associated with asthma. Protein-protein interaction (PPI) networks were constructed for the DEGs and weighted gene co-expression network analysis methods were used to further determine the functional modules associated with DEGs in different tissue samples. In addition, the gene co-expression network was constructed. Gene Ontology function analysis and pathway analysis were conducted to identify critical DEGs. The results identified numerous DEGs from the different tissue samples, including 1,662 DEGs from nasal-epithelium tissue samples, 572 DEGs from peripheral blood (PB) samples and 146 DEGs from PB mononuclear cells samples. In the PPI network, F-box only protein 6 (FBXO6), histone deacetylase 1 (HDAC1) and amyloid β precursor protein (APP) were hub genes and served an important role in the process of asthma. In addition, proliferating cell nuclear antigen (PCNA), integrin α-4 (ITGA4), catenin α-1 (CTNNA1), nuclear factor-κB1 (NF-κB1) and mechanistic target of rapamycin (MTOR) may be critical DEGs involved in the progression of asthma in children. These results suggested that FBXO6, HDAC1 and APP may interact with PCNA, ITGA4, CTNNA1, NF-κB1 and mTOR in the progression of asthma in children.
Four-pole frequency agile bandpass filter with fully canonical response and constant ABW

This paper reports on the first four-order frequency-agile bandpass filter (FA-BPF) with four transmission zeros (TZs) and constant absolute bandwidth. The proposed filter employs four varactor-loaded resonators and can be controlled by only two bias. Therefore, very simple design and control procedures are achieved while the good insertion loss (IL) performance are obtained when comparing to other cross-coupling high-order tunable filter. The design starts with the element-variable coupling matrix. By synchronously tuning the element-variable coupling matrix, the center frequency (CF) of the resultant passband is tuned and the bandpass bandwidth is kept unchanged. According to the matrix, the 1.24–1.72 GHz FA-BPF with a constant ABW of 61.5±1.5 MHz is realized and measured. The measurement results validate the prediction.