Miracle-dz/UniFAR-FTUs huggingface dataset:
Monolluma quadrangula Protects against Oxidative Stress and Modulates LDL Receptor and Fatty Acid Synthase Gene Expression in Hypercholesterolemic Rats

Hypercholesterolemia is a metabolic disorder associated with oxidative stress. The present study investigated the protective effect of Monolluma quadrangula extract on hypercholesterolemia-induced oxidative stress in the liver and heart of high-cholesterol-diet- (HCD-) fed rats. The experimental animals received HCD for 10 weeks and were concurrently treated with 300 or 600 mg/kg M. quadrangula extract. HCD-fed rats showed a significant increase in serum triglycerides, total cholesterol, LDL-cholesterol, vLDL-cholesterol, and cardiovascular risk indices along with decreased HDL-cholesterol and antiatherogenic index. The M. quadrangula extract significantly improved dyslipidemia and atherogenesis in HCD-fed rats. HCD induced a significant increase in serum transaminases, creatine kinase-MB, and proinflammatory cytokines. In addition, HDC induced a significant increase in hepatic and cardiac lipid peroxidation and decreased antioxidant enzymes. Treatment with the M. quadrangula extract significantly alleviated liver and heart function markers, decreased proinflammatory cytokines and lipid peroxidation, and enhanced the antioxidant defenses. Also, the M. quadrangula extract significantly reduced the expression of fatty acid synthase (FAS) and increased the expression of LDL receptor in the liver of HCD-fed rats. In conclusion, the M. quadrangula extract has a potent antihyperlipidemic and cholesterol-lowering effect on HCD-fed rats. The beneficial effects of the M. quadrangula extract were mediated through the increased antioxidant defenses, decreased inflammation and lipid peroxidation, and modulated hepatic FAS and LDL receptor gene expression.
Micro-Architecture Design for Low Overhead Fault Tolerant Network-on-Chip

Aggressive technology scaling results in reliability decrease of Network-on-Chips (NoCs). Error Correction Codes (ECC) is commonly used to correct error data. It is necessary to balance the reliability of transmissions and the overhead introduced by encoders and decoders. This work utilizes a mechanism reusing decoders in Network Interfaces (NIs), which is named Send-Back ECC. This paper proposes the detailed hardware implementation of Send-Back NoC after hardware overhead optimizing. The design details of the routers and NIs are described. Simulation results prove that the latency of Send-Back ECC is lower than H2H ECC when bit error rate is lower than 0.0002 and always lower than E2E ECC. The hardware overhead of Send-Back ECC is 10.6% lower than H2H ECC, while the energy consumption is also less than both E2E ECC and H2H ECC.
Eliminating Non-linear Raman Shift Displacement Between Spectrometers via Moving Window Fast Fourier Transform Cross-Correlation

Obtaining consistent spectra by using different spectrometers is of critical importance to the fields that rely heavily on Raman spectroscopy. The quality of both qualitative and quantitative analysis depends on the stability of specific Raman peak shifts across instruments. Non-linear drifts in the Raman shifts can, however, introduce additional complexity in model building, potentially even rendering a model impractical. Fortunately, various types of shift correction methods can be applied in data preprocessing in order to address this problem. In this work, a moving window fast Fourier transform cross-correlation is developed to correct non-linear shifts for synchronization of spectra obtained from different Raman instruments. The performance of this method is demonstrated by using a series of Raman spectra of pharmaceuticals as well as comparing with data obtained by using an existing standard Raman shift scattering procedure. The results show that after the removal of shift displacements, the spectral consistency improves significantly, i.e., the spectral correlation coefficient of the two Raman instruments increased from 0.87 to 0.95. The developed standardization method has, to a certain extent, reduced instrumental systematic errors caused by measurement, while enhancing spectral compatibility and consistency through a simple and flexible moving window procedure.
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art methods rely on error backpropagation, which suffers from several well-known issues, such as vanishing and exploding gradients, inability to handle non-differentiable nonlinearities and to parallelize weight-updates across layers, and biological implausibility. These limitations continue to motivate exploration of alternative training algorithms, including several recently proposed auxiliary-variable methods which break the complex nested objective function into local subproblems. However, those techniques are mainly offline (batch), which limits their applicability to extremely large datasets, as well as to online, continual or reinforcement learning. The main contribution of our work is a novel online (stochastic/mini-batch) alternating minimization (AM) approach for training deep neural networks, together with the first theoretical convergence guarantees for AM in stochastic settings and promising empirical results on a variety of architectures and datasets.
Role of synaptic variability in spike-based neuromorphic circuits with unsupervised learning

Resistive Random Access Memory (RRAM)-based artificial Neural Networks (NNs) have been shown to be intrinsically robust to RRAM variability but no study has been done to clearly explain and quantify this robustness. In this paper, we fully characterize a 4kbit RRAM array under different programming conditions. The impact of the electrical characteristics of RRAM (resistance variability, memory window, endurance performance) on the detection rate of a NN designed for object tracking and trained with a stochastic Spike-Timing Dependent Plasticity (STDP) rule is studied. We introduce a new parameter called the Synaptic Window (SW), defined as the ratio between the arithmetic mean conductance values of the low and high resistance distributions. The network performance was found only to be sensitive to the value of the SW (a SW>100 is required to achieve the maximum NN performance). Moreover, we demonstrate that a high resistance variability increases the SW for a given window margin.
Genetic and morphometric divergence in the Garnet-Throated Hummingbird Lamprolaima rhami (Aves: Trochilidae)

Cloud forests are one of the most endangered ecosystems in the Americas, as well as one of the richest in biological diversity in the world. The species inhabiting these forests are susceptible to environmental changes and characterized by high levels of geographic structure. The Garnet-Throated Hummingbird, Lamprolaima rhami, mainly inhabits cloud forests, but can also be found in other habitats. This species has a highly restricted distribution in Mesoamerica, and five disjunct regions have been delimited within the current geographic distribution of the species from Mexico to Honduras. According to variation in size and color, three subspecies have been described: L. r. rhami restricted to the Mexican highlands and Guatemala, L. r. occidentalis distributed in Guerrero (Mexico), and L. r. saturatior, distributed in the highlands from Honduras and El Salvador. We analyzed the levels of geographic structure in L. rhami and its taxonomic implications. We used mitochondrial and nuclear DNA to analyze genetic variation, demographic history, divergence times, reconstructed a multilocus phylogeny, and performed a species delimitation analyses. We also evaluated morphological variation in 208 specimens. We found high levels of genetic differentiation in three groups, and significant variation in morphological traits corresponding with the disjunct geographic populations. L. rhami presents population stability with the highest genetic variation explained by differences between populations. Divergence time estimates suggest that L. rhami split from its sister group around 10.55 million years ago, and the diversification of the complex was dated ca. 0.207 Mya. The hypotheses tested in the species delimitation analyses validated three independent lineages corresponding to three disjunct populations. This study provides evidence of genetic and/or morphometric differentiation between populations in the L. rhami complex where four separate evolutionary lineages are supported: (1) populations from the Sierra Madre Oriental and the highlands of Oaxaca (rhami), (2) populations from the highlands of Guerrero (occidentalis), (3) populations from the highlands of Chiapas and Guatemala (this is a non-previously proposed potential taxon: tacanensis), and (4) populations from the highlands of Honduras and El Salvador (saturatior). The main promoters of the geographic structure found in the L. rhami complex are likely the Isthmus of Tehuantepec as a geographic barrier, isolation by distance resulting from habitat fragmentation, and climatic conditions during the Pleistocene.
Strongly Participatory Science and Knowledge Justice in an Environmentally Contested Region

This article draws insights from a case study examining unanswered health questions of residents in two polluted towns in an industrial region in southern France. A participatory health study, as conducted by the author, is presented as a way to address undone science by providing the residents with relevant data supporting their illness claims. Local residents were included in the health survey process, from the formulation of the questions to the final data analysis. Through this strongly participatory science (SPS) process, the townspeople offered many creative ideas in the final report for how the data could be used to assist in improving their health and environment and policy work is already in evidence, resulting from the study. Drawing from the literature on participatory science and expertise as well as from the initial outcomes of the local health study, I propose that SPS produces a form of knowledge justice. Understanding knowledge and its making as part of a social justice agenda aligns well with environmental justice frames. Through SPS, local residents have a hermeneutical resource to make sense of their embodied lives and augment their claims with strong data supporting actions for improving their health and environment.
Threshold-Free Interference Cancellation Method for Automotive FMCW Radar Systems

Radar systems are key components for today's advanced driver assistance systems such as adaptive cruise control or emergency brake assistants. Along with the rising utilization of radars in modern cars, the issue of interference amongst themselves arises. It has been shown in previous work that interference between different frequency modulated continuous wave (FMCW) radar systems leads to an increased noise floor. This may severely impact the detectability of objects, especially those with a small radar cross section like pedestrians. In this work we propose a novel concept to mitigate interference in FMCW radar transceivers using digital signal processing. The actual interference cancellation is carried out in frequency domain taking into account a sequence of FMCW chirps. Therewith noise suppression is performed, the interference is cancelled, and the object information is retained in the radar image. In contrast to existing interference cancellation concepts, no threshold needs to be chosen in advance. We prove our method with simulation results, and compare it to existing work.
Time to abandon amateurism and volunteerism: addressing tensions between the Alma-Ata principle of community participation and the effectiveness of community-based health insurance in Africa

### Summary box The 1978 Alma-Ata declaration asserted that primary healthcare ‘requires and promotes maximum community and individual self-reliance and participation in the planning, organisation, operation and control of primary healthcare. ’1 It enshrined community participation in health management. Thirty years on, however, WHO’s 2008 report on primary healthcare2 noted the weak progress in this area and reaffirmed the need to mobilise people’s participation. The declaration formulated in anticipation of the second international conference on primary healthcare, slated for October 2018 (Kazakhstan), reiterates these principles by promoting community participation in healthcare governance, management and funding and by considering populations to be coproducers of health. In Africa, the development of community-based health insurance (CBHI: autonomous, not-for-profit, voluntary member-based organisations …
Hardware Implementation of a Smart Camera with Keypoint Detection and Description

Feature detection and description constitute important steps of many computer vision applications such as object detection and panorama stitching. Since those steps are computationally heavy, they might occupy significant portion of the full operation. Although fast feature detection algorithms and resource-efficient binary description methods have been proposed and implemented, resource limited embedded devices and distributed camera systems still require more effective solutions. In this paper, we propose a novel smart camera architecture which finds the FAST keypoints and computes their FREAK descriptions by processing pixel stream. Thus, this smart camera system provides useful metadata associated with the pixel stream at the same time with no latency. Moreover, performance of this hardware reaches very high frame rates with power and area efficiency. With this approach, this costly operation is locally solved in the smart camera node, and this leads to meet timing and power constraints of the large camera networks.
Statistical mechanics of low-rank tensor decomposition

Often, large, high-dimensional datasets collected across multiple modalities can be organized as a higher-order tensor. Low-rank tensor decomposition then arises as a powerful and widely used tool to discover simple low-dimensional structures underlying such data. However, we currently lack a theoretical understanding of the algorithmic behavior of low-rank tensor decompositions. We derive Bayesian approximate message passing (AMP) algorithms for recovering arbitrarily shaped low-rank tensors buried within noise, and we employ dynamic mean field theory to precisely characterize their performance. Our theory reveals the existence of phase transitions between easy, hard and impossible inference regimes, and displays an excellent match with simulations. Moreover it reveals several qualitative surprises compared to the behavior of symmetric, cubic tensor decomposition. Finally, we compare our AMP algorithm to the most commonly used algorithm, alternating least squares (ALS), and demonstrate that AMP significantly outperforms ALS in the presence of noise.
Proportional Fairness in Wireless Powered CSMA&#x002F;CA Based IoT Networks

This paper considers the deployment of a hybrid wireless data&#x002F;power access point in an 802.11- based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA&#x002F;CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.
First-hand experience of why gamification projects fail and what could be done about it

A plethora of services, applications and scholarly research has emerged related to gamification. Regardless of the optimistic onset of this hype around the technology trend, designing gamification has proved to be a challenging endeavor; requiring multidisciplinary work that is often hindered by multiple theoretical and practical challenges. Problem-driven, theory-advancing approaches to gamification research could assist in the addressment of gamification design challenges and accelerate the growth of the gamification field however not all such approaches have been equally utilized or understood. This paper presents the case of MANGO: a project to design a gamified e-participation tool through Action Design Research (ADR). The paper reflects on the challenges of gamification design and development and possible strategies to address them. It additionally reflects on the ADR process; an under-utilized and hence possibly a superficially understood approach to gamification research. The paper is hence a guide for researchers and practitioners as to possible challenges they can face with gamification research and design and how to counteract them.
A Wide-Tunning-Range Low-Phase-Noise Colpitts Oscillator with Variable Capacitive Feedback

In this paper, we propose a technique to extend the tuning range (TR) of Colpitts complementary metal-oxide semiconductor (CMOS) voltage-controlled oscillators (VCOs). To achieve a wide tuning range, we use variable capacitive feedback to extend the tuning range while achieving lower phase noise (PN) than that of the conventional Colpitts VCOs. In addition, the structure benefits from the dynamic forward-body self-biased technique to reduce the power consumption and facilitate the start-up condition. As a proof-of-concept, a 28-GHz Colpitts oscillator, which is suitable for 5G wireless applications, is designed and laid out in a 65-nm CMOS process. Post-layout simulations in conjunction with electromagnetic (EM) simulations confirm the improved performance of the proposed technique. Based on the simulation results, the VC O achieves a PN of −115.7 dBc/Hz at 10-MHz offset, and TR of ∼34.5% resulting in a figure of merit incorporating the tuning range (FOMt) of −189 dBc/Hz. The circuit consumes 4.3 mW from a 1-V supply and excluding the pads occupies a 0.12 mm2 of silicon area.
LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on Color

Designing a logo is a long, complicated, and expensive process for any designer. However, recent advancements in generative algorithms provide models that could offer a possible solution. Logos are multi-modal, have very few categorical properties, and do not have a continuous latent space. Yet, conditional generative adversarial networks can be used to generate logos that could help designers in their creative process. We propose LoGAN: an improved auxiliary classifier Wasserstein generative adversarial neural network (with gradient penalty) that is able to generate logos conditioned on twelve different colors. In 768 generated instances (12 classes and 64 logos per class), when looking at the most prominent color, the conditional generation part of the model has an overall precision and recall of 0.8 and 0.7 respectively. LoGAN's results offer a first glance at how artificial intelligence can be used to assist designers in their creative process and open promising future directions, such as including more descriptive labels which will provide a more exhaustive and easy-to-use system.
Brain Responses to Emotional Faces in Natural Settings: A Wireless Mobile EEG Recording Study

The detection of a human face in a visual field and correct reading of emotional expression of faces are important elements in everyday social interactions, decision making and emotional responses. Although brain correlates of face processing have been established in previous fMRI and electroencephalography (EEG)/MEG studies, little is known about how the brain representation of faces and emotional expressions of faces in freely moving humans. The present study aimed to detect brain electrical potentials that occur during the viewing of human faces in natural settings. 64-channel wireless EEG and eye-tracking data were recorded in 19 participants while they moved in a mock art gallery and stopped at times to evaluate pictures hung on the walls. Positive, negative and neutral valence pictures of objects and human faces were displayed. The time instants in which pictures first occurred in the visual field were identified in eye-tracking data and used to reconstruct the triggers in continuous EEG data after synchronizing the time axes of the EEG and eye-tracking device. EEG data showed a clear face-related event-related potential (ERP) in the latency interval ranging from 165 to 210 ms (N170); this component was not seen whilst participants were viewing non-living objects. The face ERP component was stronger during viewing disgusted compared to neutral faces. Source dipole analysis revealed an equivalent current dipole in the right fusiform gyrus (BA37) accounting for N170 potential. Our study demonstrates for the first time the possibility of recording brain responses to human faces and emotional expressions in natural settings. This finding opens new possibilities for clinical, developmental, social, forensic, or marketing research in which information about face processing is of importance.
CORDIC Framework for Quaternion-based Joint Angle Computation to Classify Arm Movements

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in ‘making-a-cup-of-tea’ an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration.
High-speed 8/16/32-point DCT Architecture Using Fixed-rotation Adaptive CORDIC

In this paper, the high-speed Discrete Cosine Transform (DCT) architecture is presented using the Adaptive CORDIC (ACor) algorithm built with a fixed-rotation angle. The proposed method is implemented in six different versions corresponding to the number of DCT point, i.e., 8-point (8p), 16-point (16p), and 32-point (32p), and the number of ACor stages, i.e., 2-Stage (2S) and 3-Stage (3S). The implementations are built and verified on an Altera Stratix IV FPGA. The 2S designs of 8p-DCT, 16p-DCT, and 32p-DCT achieve the maximum operating frequencies of 179.86 MHz, 162.60 MHz, and 136.97 MHz, respectively. Moreover, the 2S-32p-DCT module is implemented in ASIC with the 65nm-SOTB CMOS technology. The synthesis shows that the core costs 47.2K gates and consumes about 0.68 mW while operating at 100 MHz clock rate. The 2S implementations of 8p-DCT, 16p-DCT, and 32p-DCT achieve four, five, and six adder-delay, mean-square-error of 1.403e-4, 2.029e-2, and 7.663e-2, and coding gain of 8.8108 dB, 9.0984 dB, and 9.2170 dB, respectively. In comparison with recent works, the proposed method achieves the best timing performances, good accuracy results, and adequate resources cost.