Miracle-dz/UniFAR-FTUs huggingface dataset:
DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Level Attention for Image Denoising

Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.
Analysis of Hybrid PAPR Reduction Methods of OFDM Signal for HPA Models in Wireless Communications

Orthogonal Frequency Division Multiplexing (OFDM) suffers from a high Peak-to-Average Power Ratio (PAPR). Designing a High Power Amplifier (HPA) with high PAPR is not a wise technique as it roots the amplifier to operate in a non-linear region which is intricate. Linearity and power efficiency are important constraints of HPA which cannot be achieved at the same time. Therefore, perfect linearity is observed when efficiency is low or vice versa and efficiency can be improved by decreasing the PAPR. In this paper, the PAPR is mitigated by using Partial Transmit Sequence (PTS), Selected Mapping (SLM), Hybrid and proposed methods. Analysis of OFDM with high PAPR passing through different HPA models is evaluated in terms of Power Spectral Density (PSD), gain and efficiency for all PAPR reduction methods considered in this work. The results are encouraging by using hybrid PAPR reduction method, HPAs can be operated in a linear region to provide higher efficiency compared to non-hybrid PAPR reduction methods. Hence, hybrid PAPR reduction methods can be used even in future wireless communications systems including 5G and beyond.
Programmability and Performance of New Global-View Programming API for Multi-Node and Multi-Core Processing

Various partitioned global address space (PGAS) languages capable of providing global-view programming environments on multi-node computer systems have been proposed to improve programming productivity in high-performance computing. However, several PGAS languages often require a detailed description of the remote data access, similar to descriptions used in message passing interface one-sided communications. Some PGAS languages have limitations pertaining to remote data access and recommend their local-view programming models, rather than the global-view ones, due to performance-related reasons. In this study, we propose SMint, which is an application programming interface that provides a global-view programming model with a software distributed shared memory mSMS as the runtime. Using stencil computation as a typical processing method, the performance and programmability of SMint have been compared with those of XcalableMP and Unified Parallel C, which are well-known examples of PGAS languages based on the C language. It was found that SMint achieved the best performance under the ideal global-view programming model.
Model-based Dynamic Pose Graph SLAM in Unstructured Dynamic Environments

Navigation in dynamic environments is a challenge for autonomous vehicles operating without prior maps or global position references. This poses high risk to vehicles that perform scientific studies and monitoring missions in marine Arctic environments characterized by slowly moving sea ice with few truly static landmarks. Whereas mature simultaneous localization and mapping (SLAM) approaches assume a static environment, this work extends pose graph SLAM to spatiotemporally evolving environments. A novel model-based dynamic factor is proposed to capture a landmark's state transition model - whether the state be kinematic, appearance or otherwise. The structure of the state transition model is assumed to be known a priori, while the parameters are estimated on-line. Expectation maximization is used to avoid adding variables to the graph. Proof-of-concept results are shown in small- and medium-scale simulation, and small-scale laboratory environments for a small quadrotor. Preliminary laboratory validation results shows the effect of mechanical limitations of the quadrotor platform and increased uncertainties associated with the model-based dynamic factors on the SLAM estimate. Simulation results are encouraging for the application of model-based dynamic factors to dynamic landmarks with a constant-velocity kinematic model.
A hierarchical approach to self-test, fault-tolerance and routing security in a Network-on-Chip

Since the performance of bus interconnects does not scale with the number of processors connected to the bus, chip multiprocessors make use of on-chip networks that implement packet switching and virtual channel flow control to efficiently transport data. In this paper, we consider the test and fault-tolerance aspects of such a network-on-chip (NoC). Past work in this area has addressed the communication efficiency and deadlock-free properties in NoC, but when routing externally received data, aspects of security must be addressed. A malicious denial-of-service attack or a power virus can be launched by a malicious external agent. We propose a two-tier solution to this problem, where a local self-test manager in each processing element runs test algorithms to detect faults in local processing element and its associated physical and virtual channels. At the global level, the health of the NoC is tested using a sorting-based algorithm proposed in this paper. Similarly, we propose to handle fault-tolerance and security concerns in routing at two levels. At the local level, each node is capable of fault-tolerant routing by deflecting packets to an alternate path; when doing so, since a chance of deadlock may be created, the local router must be capable of guestimating a deadlock situation, switch to packet-switching instead of flit-switching and attempt to reroute the packet. At the global level, a routing agent plays the role of gathering fault data and provide the fault-information to nodes that seek this information periodically. Similarly, the agent is capable of detecting malformed packets coming from an external source and prevent injecting such packets into the network, thereby conserving the network bandwidth. The agent also attempts to guess attempts at denial-of-service attacks and power viruses and will reject packets. Use of a two-tier approach helps in keeping the IP modular and reduces their complexity, thereby making them easier to verify.
D2VCB: A Hybrid Deep Neural Network for the Prediction of in-vivo Protein-DNA Binding from Combined DNA Sequence

Prediction of in-vivo protein-DNA binding is an important, but challenging task in the broad field of computational biology. Although some methods based on deep learning have succeed in modeling in-vivo protein-DNA binding, they often simply extract the sequence features from the original DNA sequence without consideration of other sequence features, such as their reverse, complementary and reverse complementary sequences. Also, one-hot encoding of DNA sequence is vulnerable to the curse of dimensionality, which leads to unwanted equidistance of pairwise sequences. To address these problems, we propose D2VCB (dna2vec, convolution, bi-LSTM), a novel hybrid deep neural network framework using dna2vec to predict in-vivo protein-DNA binding events. We extract input features from DNA original sequences, reverse sequences, complementary and complementary reverse sequences, and then use dna2vec to compute a distributed representation of k-mer. In our D2VCB model, the convolution layer captures motif features, while the recurrent layer captures long-term dependencies among motif features so as to improve prediction accuracy. Our performance comparison experiments show that D2VCB outperforms significantly other existing methods in terms of multiple performance metrics.
GPS emulation via visual-inertial odometry for inspection drones

This work tested the applicability of a navigation system based on cameras and inertial sensors for a quadrotor UAV, motivated by the desire to expand its operational envelope to regions with low GPS signal reception. A standalone navigation module composed of a stereo camera pair, an IMU, synchronization electronics and a SoC computational board was developed and provided with OKVIS, a recent open-source VIO (Visual-Inertial Odometry) algorithm. In order to quantitatively assess its performance, an indoors dataset was recorded in a controlled environment, with precise ground-truth from a motion capture system. The system was integrated with a quadrotor UAV, functioning as an additional GPS sensor from the perspective of the onboard autopilot. For this end, the VIO trajectory data was georeferenced using information from the onboard GPS receiver, as well as from the orientation estimator embedded in the IMU. To the best of our knowledge, our system is the first to follow this integration approach, this being one of the main contributions of this work. Having validated the system with handheld testing, flight tests were performed. We show, qualitatively, that our system effectively yields improved trajectory estimates under low GPS signal reception.
FPGA-based Implementation of HOG Algorithm: Techniques and Challenges

Histogram of Oriented Gradients (HOG) is a method for extracting features from an image, which has many applications in Computer Vision. Due to the complexity and high amount of computations of this algorithm, software-based implementations of HOG cannot meet the real-time criterion. Therefore, many researchers have implemented HOG algorithm on hardware platforms such as FPGAs. This paper presents an extensive review of FPGA-based implementations of the HOG algorithm, that have been published from 2010 to 2019. Different techniques for hardware implementation of HOG are classified into three groups: methods which improve a certain stage of the algorithm, methods which optimize the whole algorithm, and methods which make minor simplification on the algorithm. In this paper, these three classes of techniques are reviewed. Finally, the speed and resource utilization of the surveyed papers are compared to each other in order to present a comprehensive conclusion on FPGA-based HOG implementation.
Unexpected Diversity: Quantitative Memory Analysis for Zynq UltraScale+ Systems

Memory throughput is one of the major bottlenecks for accelerator performance. Now that Zynq UltraScale+ systems are being deployed at exascale to edge, it is important to understand their characteristics of the memory subsystem and optimizations possible for developers. In this paper, we extensively evaluate the memory performance and behaviour for various AXI port combinations, burst sizes, access patterns, and the number of accelerators per AXI port. Our results on ZCU102 and Ultra 96 boards show that 1) effective throughput of these systems is reaching only 75% and 92.5% of theoretical maximum respectively, 2) 128 and 192 Byte burst size is often optimal, 3) AXI ports of the same type may not always exhibit similar behaviour, 4) multiplexing accelerators in PL can provide better throughput distribution compared to multiplexing in PS, and 5) using all AXI ports does not lead to the highest performance.
Image sensor based Diffuse Optical Tomographic System

Diffuse optical tomography (DOT) is a promising biomedical imaging modality that can provide the metabolic state of tissues sitting deep inside body, non-invasively. This health state is mapped from the recovered three-dimensional distribution of optical parameters, estimated from boundary photon-flux measurements. In a conventional DOT imaging system, delivery of probing beam to tissue and photon flux measurements are carried out with the help of dedicated optical fiber arrangements. A modified experimental setup is proposed that replaces the measuring optical fibers by a camera unit, and the experimental results obtained from a tissue mimicking phantom are reported. The camera measures the boundary photon-flux over the region of interest (ROI) space-continuously in a denser grid. The increase of measurements per projection improves the rank of the system matrix and hence reduces the ill-posedness associated with the DOT imaging. Experimental results obtained from a tissue mimicking cylindrical shape phantom are presented that show a good agreement with the known values.
Congenital absence of a lumbosacral facet joint: A case report

Background: Congenital absence of the lumbosacral facet joint is extremely rare, with only 26 cases reported in the literature. Here, we present a patient with the unilateral absence of the left fifth lumbar inferior articular process and reviewed the relevant literature. Case Description: A 32-year-old gentleman, who had undergone right L4-5 lumbar microdiscectomy 3 months ago now presented with acute low back and left leg pain following a fall. He is now presented with acute low back and left leg pain following a fall. Plain radiographs of the L-S spine revealed an absent left L5–S1 zygapophyseal joint. The magnetic resonance imaging and computed tomography studies additionally confirmed an absent unilateral left L5 lumbar inferior articular process. Conclusion: Patients presenting for lumbar surgery may have unilaterally absent lumbosacral zygapophyseal joints, which may impact the outcome of surgical treatment.
Evolution, developmental expression and function of odorant receptors in insects

ABSTRACT Animals rely on their chemosensory system to discriminate among a very large number of attractive or repulsive chemical cues in the environment, which is essential to respond with proper action. The olfactory sensory systems in insects share significant similarities with those of vertebrates, although they also exhibit dramatic differences, such as the molecular nature of the odorant receptors (ORs): insect ORs function as heteromeric ion channels with a common Orco subunit, unlike the G-protein-coupled olfactory receptors found in vertebrates. Remarkable progress has recently been made in understanding the evolution, development and function of insect odorant receptor neurons (ORNs). These studies have uncovered the diversity of olfactory sensory systems among insect species, including in eusocial insects that rely extensively on olfactory sensing of pheromones for social communication. However, further studies, notably functional analyses, are needed to improve our understanding of the origins of the Orco–OR system, the mechanisms of ORN fate determination, and the extraordinary diversity of behavioral responses to chemical cues. Summary: A review of our understanding of the origins of the olfactory system in insects, the mechanisms of olfactory receptor fate determination, and the extraordinary diversity of behavioral responses to chemical cues.
Dexamethasone Inhibits Spheroid Formation of Thyroid Cancer Cells Exposed to Simulated Microgravity

Detachment and the formation of spheroids under microgravity conditions can be observed with various types of intrinsically adherent human cells. In particular, for cancer cells this process mimics metastasis and may provide insights into cancer biology and progression that can be used to identify new drug/target combinations for future therapies. By using the synthetic glucocorticoid dexamethasone (DEX), we were able to suppress spheroid formation in a culture of follicular thyroid cancer (FTC)-133 cells that were exposed to altered gravity conditions on a random positioning machine. DEX inhibited the growth of three-dimensional cell aggregates in a dose-dependent manner. In the first approach, we analyzed the expression of several factors that are known to be involved in key processes of cancer progression such as autocrine signaling, proliferation, epithelial–mesenchymal transition, and anoikis. Wnt/β-catenin signaling and expression patterns of important genes in cancer cell growth and survival, which were further suggested to play a role in three-dimensional aggregation, such as NFKB2, VEGFA, CTGF, CAV1, BCL2(L1), or SNAI1, were clearly affected by DEX. Our data suggest the presence of a more complex regulation network of tumor spheroid formation involving additional signal pathways or individual key players that are also influenced by DEX.
Left‐handed musicians show a higher probability of atypical cerebral dominance for language

Music processing and right hemispheric language lateralization share a common network in the right auditory cortex and its frontal connections. Given that the development of hemispheric language dominance takes place over several years, this study tested whether musicianship could increase the probability of observing right language dominance in left‐handers. Using a classic fMRI language paradigm, results showed that atypical lateralization was more predominant in musicians (40%) than in nonmusicians (5%). Comparison of left‐handers with typical left and atypical right lateralization revealed that: (a) atypical cases presented a thicker right pars triangularis and more gyrified left Heschl's gyrus; and (b) the right pars triangularis of atypical cases showed a stronger intra‐hemispheric functional connectivity with the right angular gyrus, but a weaker interhemispheric functional connectivity with part of the left Broca's area. Thus, musicianship is the first known factor related to a higher prevalence of atypical language dominance in healthy left‐handed individuals. We suggest that differences in the frontal and temporal cortex might act as shared predisposing factors to both musicianship and atypical language lateralization.
The Dysbiosis of Gut Microbiota Caused by Low-Dose Cadmium Aggravate the Injury of Mice Liver through Increasing Intestinal Permeability

Cadmium (Cd), widely present in food and drinking water at low doses, can cause health risks. However, the mechanistic effects of long-term Cd exposure at low dose through dietary intake is poorly studied. The aim of this study is to elucidate whether the dysbiosis of gut microbiota caused by Cd at an environmental low dose can aggravate the injury of mice liver, and the possible mechanism is investigated. In order to explore the potential underlying mechanism, the analyses of the variation of gut microbiota composition, intestinal permeability, and hepatic transcriptome were conducted. Our results showed that gut microbiota was disturbed. The rise of intestinal permeability induced by the dysbiosis of gut microbiota resulted in more Cd ions accumulating in mice liver, but it could be restored partly through depleting gut microbiota by antibiotics cocktail. Transcriptomic analyses indicated that 162 genes were significantly differentially expressed including 59 up-regulated and 103 down-regulated in Cd treatment. These genes were involved in several important pathways. Our findings provide a better understanding about the health risks of cadmium in the environment.
Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan. This network is tunable to accommodate natural variations in device parameters and to avoid false triggering of Trojan alerts. The tolerance is tuned using bio-inspired principles for various security requirements, such as forming high-alert systems for safety-critical missions. The Trojan detection circuit is resilient to a range of faults and attacks, both intentional and unintentional. Also, we devise a design-for-trust architecture by developing a bio-inspired device-locking mechanism. The proposed architecture is implemented on a Xilinx Artix-7 Field Programmable Gate Array (FPGA) device. Results demonstrate the suitability of the proposal for resource-constrained environments with minimal hardware and power dissipation profiles. The design is tested with a wide range of device parameters to demonstrate the effectiveness of Trojan detection. This work serves as a new approach to enable secure CPSs and to employ bio-inspired unsupervised machine intelligence.
A Small Number of HER2 Redirected CAR T Cells Significantly Improves Immune Response of Adoptively Transferred Mouse Lymphocytes against Human Breast Cancer Xenografts

HER2 positive JIMT-1 breast tumors are resistant to trastuzumab treatment in vitro and develop resistance to trastuzumab in vivo in SCID mice. We explored whether these resistant tumors could still be eliminated by T cells redirected by a second-generation chimeric antigen receptor (CAR) containing a CD28 costimulatory domain and targeting HER2 with a trastuzumab-derived scFv. In vitro, T cells engineered with this HER2 specific CAR recognized HER2 positive target cells as judged by cytokine production and cytolytic activity. In vivo, the administration of trastuzumab twice weekly had no effect on the growth of JIMT-1 xenografts in SCID mice. At the same time, a single dose of 2.5 million T cells from congenic mice exhibited a moderate xenoimmune response and even stable disease in some cases. In contrast, when the same dose contained 7% (175,000) CAR T cells, complete remission was achieved in 57 days. Even a reduced dose of 250,000 T cells, including only 17,500 CAR T cells, yielded complete remission, although it needed nearly twice the time. We conclude that even a small number of CAR T lymphocytes can evoke a robust anti-tumor response against an antibody resistant xenograft by focusing the activity of xenogenic T cells. This observation may have significance for optimizing the dose of CAR T cells in the therapy of solid tumors.