Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical FPGA programming in C

C-based techniques for building high-performance, FPGA-accelerated software applicationsCircuits, Devices, and SystemsC-based Techniques for Optimizing FPGA Performance, Design Flexibility, and Time to MarketForward written by Clive "Max" Maxfield. High-performance FPGA-accelerated software applications are a growing demand in fields ranging from communications and image processing to biomedical and scientific computing. This book introduces powerful, C-based parallel-programming techniques for creating these applications, verifying them, and moving them into FPGA hardware.The authors bridge the chasm between "conventional" software development and the methods and philosophies of FPGA-based digital design. Software engineers will learn to look at FPGAs as "just another programmable computing resource," while achieving phenomenal performance because much of their code is running directly in hardware. Hardware engineers will master techniques that perfectly complement their existing HDL expertise, while allowing them to explore design alternatives and create prototypes far more rapidly. Both groups will learn how to leverage C to support efficient hardware/software co-design and improve compilation, debugging, and testing. Understand when C makes sense in FPGA development and where it fits into your existing processes Leverage C to implement software applications directly onto mixed hardware/software platforms Execute and test the same C algorithms in desktop PC environments and in-system using embedded processors Master new, C-based programming models and techniques optimized for highly parallel FPGA platforms Supercharge performance by optimizing through automated compilation Use multiple-process streaming programming models to deliver truly astonishing performance Preview the future of FPGA computing Study an extensive set of realistic C code examplesAbout the Web SiteVisit http://www.ImpulseC. com/practical to download fully operational, time-limited versions of a C-based FPGA design compiler, as well as additional examples and programming tips. © Copyright Pearson Education. All rights reserved.
Temporal Shift Module for Efficient Video Understanding

The explosive growth in online video streaming gives rise to challenges on efﬁciently extracting the spatial-temporal information to perform video understanding. Conventional 2D CNNs are computationally cheap but cannot capture long-term temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efﬁciency and high performance. Speciﬁcally, it can achieve the performance of 3D CNN but maintain 2D complexity. The central idea of TSM is to shift part of the channels along the temporal dimension, which facilitates information exchange among neighboring frames. TSM can be inserted into 2D CNNs to achieve temporal modeling at the cost of zero FLOPs and zero parameters. On the Something-Something-V1 dataset which focuses on temporal modeling, we achieved better results than I3D family and ECO family using 6 × and 2 . 7 × fewer FLOPs respectively. Measured on P100 GPU, our single model achieved 1.8% higher accuracy at 8 × lower latency and 12 × higher throughput compared to I3D. Remarkably, our framework ranks the ﬁrst on both Something-Something V1 and V2 leaderboards upon this paper’s submission.
Effect of CT Reconstruction Algorithm on the Diagnostic Performance of Radiomics Models: A Task-Based Approach for Pulmonary Subsolid Nodules.

OBJECTIVE We investigated whether the diagnostic performance of machine learning-based radiomics models for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among subsolid nodules (SSNs) was affected by the proportion of images reconstructed with filtered back projection (FBP) and model-based iterative reconstruction (MBIR) in datasets used for feature extraction. MATERIALS AND METHODS This retrospective study included 60 patients (23 men and 37 women; mean age, 61.4 years) with 69 SSNs (54 part-solid and 15 pure ground-glass nodules). Preoperative CT scans were reconstructed with both FBP and MBIR. A total of 860 radiomics features were obtained from the entire nodule volume, and 70 resampled nodule datasets with an increasing proportion of nodules with MBIR-derived features (from 0/69 to 69/69) were prepared. After feature selection using neighborhood component analysis, support vector machines (SVMs) and an ensemble model were used as classifiers for the differentiation of IPAs. The diagnostic performances of all blending proportions of reconstruction algorithms were calculated and analyzed. RESULTS The ROC AUC and the diagnostic accuracy of the radiomics models decreased significantly as the number of nodules with MBIR-derived features increased, and this relationship followed cubic functions (R2 = 0.993 and 0.926 for SVM; R2 = 0.993 and 0.975 for the ensemble model; p < 0.001). The magnitude of variation in AUC due to the reconstruction algorithm heterogeneity was 0.39 for SVM and 0.39 for the ensemble model. CONCLUSION Inclusion of CT scans reconstructed with MBIR for radiomics modeling can significantly decrease diagnostic performance for the identification of IPAs.
Intermediate Band Thermoradiative Cells

A new model of the intermediate band thermoradiative cell (IBTRC) is put forward. Expressions for the power output density (POD) and efficiency of the IBTRC are derived. The current densities and PODs arising from the normal transitions and interband transitions of electrons are compared. It is found that when the IBTRC with a bandgap of 0.2 eV is operated between the heat source at 1000 K and the surroundings at 300 K, the maximum POD and efficiency of the cell are approximately <inline-formula> <tex-math notation="LaTeX">$1.583\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.096\times $ </tex-math></inline-formula> of those of the thermoradiative cell without intermediate bands (IBs) under the same operating temperature and bandgap, respectively. This shows that the performance of the IBTRC can be largely improved by the insert of the IB. Moreover, the optimal selection criteria of some key parameters are provided, and then the optimum operating conditions of the IBTRC are determined. Finally, several issues that will influence the performances of the IBTRC are pointed out. These results may help engineers to fabricate high-performance IBTRCs.
Diffusion phase competition - fundamentals (Review)

Розглядається проблема початкових стадій реакційної дифузії. Аналізуються критерії росту та пригнічення фаз внаслідок дифузійної взаємодії. Пропонуються способи обчислення інкубаційних періодів. Аналізується роль метастабільних фаз на початковій стадії.
Long-term basketball playing enhances bone mass and isokinetic muscle strength

Aim: To evaluate bone mass in female basketball players and the relationship between bone mineral density (BMD) and isokinetic muscle strength. Methods: We investigated 29 female basketball players, age 21.9± 9.1 years, training for about 6–8 hours per week. The reference group consisted of 22 non-active female, age 21.4 ± 1.5 years. BMD was measured using dual-energy-X-ray absorptiometry in total body, lumbar spine, the whole femur and humerus. Isokinetic peak torque of the quadriceps and hamstring muscles were measured at 60 and 180◦/s. Osteocalcin (OC) and cross-linked C-telopeptide (CTX) were measured by radioimmunoassay and electrochimiluminescence, respectively. Results: Compared to the controls, the basketball group had significantly (P < 0.001) a higher BMD in the total body (11.6%), the lumbar spine (9.83%), the dominant femur (16.8%), the non-dominant femur (15.8%) and the dominant humerus (8.47%). The female basketball players had also significantly higher peak torques at both isokinetic velocities (60 and 180◦/s) and for both lower limbs. All muscle peak torque measured for the female basketball players were significantly correlated with correspondent leg BMD and for both velocities tested. However, no significant correlations were noted for the control group. Values of formation (OC) and resorption (CTX) markers were significantly higher in the athletic females (P < 0.01 and P < 0.05, respectively). Conclusion: Our results show clearly that practice of basketball is associated with the improvement of bone mass. In addition, the most important finding of our study is that isokinetic muscle strength seems to be correlated with correspondent BMD in female basketball players.
Correcting the MSU middle tropospheric temperature for diurnal drifts

Channel 2 of the 9 Microwave Sounding Units (MSUs) flown on NOAA polar orbiting platforms provides a 23-year time series of middle-tropospheric temperature. These measurements may be of sufficient quality for climate studies if intersatellite calibration offsets and drifts can be accurately characterized and removed. One of the most important and difficult to characterize sources of long-term drift in the data is due to the evolution of the local observing time due to slow changes in the orbital parameters of each NOAA platform, which can alias diurnal temperature changes into the long-term time series. To account for this effect, we have constructed monthly diurnal climatologies of MSU Channel 2 brightness temperature using the hourly output of a general circulation model as input for a microwave radiative transfer model. We report the results of this calculation, and validate the result by comparing with MSU observations.
Optimization of Links With a Battery-Assisted Time-Switching Wireless Energy Harvesting Relay

In this paper, we analyze the performance of a two-hop network in which a multiantenna source communicates with a single-antenna destination through a decode-and-forward single-antenna energy harvesting (EH) relay. We assume a novel practical system framework in which the EH relay has the option of augmenting the wireless energy harvested by drawing energy from a supplementary battery, thereby improving link performance. Assuming use of the time-switching relaying (TSR) EH protocol, the outage and throughput performance are analyzed. We optimize the fraction of time devoted to EH (the TSR parameter) so as to maximize throughput for fixed supplementary battery energy. We also optimize the TSR parameter to minimize supplementary battery energy consumption for target throughput performance. We further show that exploiting second-hop channel knowledge at the relay can dramatically reduce the energy drawn from the supplementary battery. These insights are of fundamental importance to system designers. Simulation results validate the derived analytical expressions and corroborate our findings.
Social Supports and Breast Cancer

Eleven studies were reviewed to examine the role of social supports for breast cancer patients and their families. Variables examined across the studies included demographic factors, stage of disease and treatment, the types of social support patients received, and how the support was measured. The authors attempted to evaluate the relationship between stages of the disease process and the type of social support best suited to the individual patient and her family. Results from this comparison revealed several areas that should be addressed in future studies to clarify the types and measurement of social support and to address the efficacy of social support on patients' adjustment and survival.
Different characteristics and survival in non‐small cell lung cancer patients with primary and acquired EGFR T790M mutation

Primary epidermal growth factor receptor (EGFR) T790M mutation can be occasionally identified in previous untreated nonsmall cell lung cancer (NSCLC) patients. To compare clinical characteristics and outcomes in patients with primary and acquired EGFR T790M mutation, we collected the data of patients diagnosed with EGFR mutation from 2012 to 2017 in Shanghai Chest Hospital. Primary EGFR T790M mutation was identified in 61 patients (1.1%; 95% confidence interval (CI): 0.8%–1.3%) of 5685 TKI‐naive EGFR mutant patients. Acquired T790M mutation was detected in 98 patients (50.3%; 95%CI: 43.2%–57.3%) of 195 TKI‐treated patients. T790M mutation always coexisted with sensitizing EGFR mutations. Primary EGFR T790M always coexisted with 21L858R (46/61) whereas acquired T790M coexisted with 19del (68/98), (p < 0.001). Among them, 18 patients with primary T790M mutation received osimertinib and 72 patients with acquired T790M mutation received osimertinib. The median progression‐free survival (PFS) of osimertinib was significantly longer in primary T790M group (17.0 months, 95%CI:14.0–20.0 months) compared to acquired T790M group (10.0 months, 95%CI:8.6–11.4 months, p = 0.022). However, the median overall survival (OS) of acquired T790M mutation patients was significantly longer compared to that of primary T790M mutation patients who received osimertinib (50.4 months vs. 29.9 months, p = 0.016). Our findings suggest that primary T790M mutation likely coexists with 21L858R while acquired mutation likely coexists with 19del. Both mutations showed good response to osimertinib. Patients with primary T790M mutation experienced greater benefits from osimertinib. However, patients with acquired T790M mutation had a better overall survival during the entire clinical treatment.
Bayesian graph convolutional neural networks for semi-supervised classification

Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spurious edges may be included; other edges may be missing between nodes that have very strong relationships. In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs. We then target inference of the joint posterior of the random graph parameters and the node (or graph) labels. We present the Bayesian GCNN framework and develop an iterative learning procedure for the case of assortative mixed-membership stochastic block models. We present the results of experiments that demonstrate that the Bayesian formulation can provide better performance when there are very few labels available during the training process.
Wavelet–Based Face Recognition Schemes

The growth in direct threats to people’s safety in recent years and the rapid increase in fraud and identity theft has increased the awareness of security requirements in society and added urgency to the task of developing biometric-based person identification as a reliable alternative to conventional authentication methods. In this Chapter we describe various approaches to face recognition with focus on wavelet-based schemes and present their performance using a number of benchmark databases of face images and videos. These schemes include single-stream (i.e. those using single-subband representations of face) as well as multi-stream schemes (i.e. those based on fusing a number of wavelet subband representations of face). We shall also discuss the various factors and quality measures that influence the performance of face recognition schemes including extreme variation in lighting conditions and facial expressions together with measures to reduce the adverse impact of such variations. These discussions will lead to the introduction of new innovative adaptive face recognition schemes. We shall present arguments in support of the suitability of such schemes for implementation on mobile phones and PDA’s.
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

The performance and efficiency of distributed training of Deep Neural Networks highly depend on the performance of gradient averaging among all participating nodes, which is bounded by the communication between nodes. There are two major strategies to reduce communication overhead: one is to hide communication by overlapping it with computation, and the other is to reduce message sizes. The first solution works well for linear neural architectures, but latest networks such as ResNet and Inception offer limited opportunity for this overlapping. Therefore, researchers have paid more attention to minimizing communication. In this paper, we present a novel gradient compression framework derived from insights of real gradient distributions, and which strikes a balance between compression ratio, accuracy, and computational overhead. Our framework has two major novel components: sparsification of gradients in the frequency domain, and a range-based floating point representation to quantize and further compress gradients frequencies. Both components are dynamic, with tunable parameters that achieve different compression ratio based on the accuracy requirement and systems' platforms, and achieve very high throughput on GPUs. We prove that our techniques guarantee the convergence with a diminishing compression ratio. Our experiments show that the proposed compression framework effectively improves the scalability of most popular neural networks on a 32 GPU cluster to the baseline of no compression, without compromising the accuracy and convergence speed.
Image Processing Techniques to Identify Red Blood Cells

This paper presents a method for the automatic identification and classification of red cells in different classes of interest for diagnosis using microscopic images of blood smear. The whole system uses different image processing techniques such as binarization, contrast enhancement, noise elimination, morphological operations (dilatation, erosion), labeling and extraction of some features of interest (area, perimeter, diameter). Using this information, some factors (form factor, circularity factor, and deviation factor) involved in the classification of red cells are calculated. The classification process has two phases: the first separates red cells in normal and abnormal type and the second classifies the abnormal in three subclasses. This system does not aim to replace the pathologist, but to assist him / her and to improve the execution time of these types of analyzes.
Identification of a novel 43-bp insertion in the heparan sulfate 6-O-sulfotransferase 3 (HS6ST3) gene and its associations with growth and carcass traits in chickens

Abstract Previous studies have revealed a significant association between SNPs found within the heparan sulfate 6-O-sulfotransferase 3 (HS6ST3) gene and obesity. This study identified a novel 43-bp indel polymorphism in intron 1 of HS6ST3 in 1963 chickens from nine different breeds, and three genotypes, designated II, ID and DD, were observed. The frequency of the ‘I’ (0.62–0.87) allele was higher than that of the ‘D’ (0.13–0.38) allele. A total of 777 individuals of the Gushi-Anka F2 resource population were used for the analysis of associations according to growth traits, carcass traits, serum variables and meat quality traits. The results showed that the 43-bp indel polymorphism was significantly associated with the body weight at 4 and 6 weeks of age, chest depth at 4 and 12 weeks of age and shank girth at 12 weeks of age (P < 0.05). In terms of the carcass traits, the indel polymorphism was significantly associated with breast muscle weight, heart weight and leg weight (P < 0.05). These findings suggested that this indel polymorphism has the potential to become a new target for the marker-assisted selection of chicken growth and carcass traits.
Emotional Responses to Pleasant Sounds Are Related to Social Disconnectedness and Loneliness Independent of Hearing Loss

The purpose of this study was to evaluate the relationship between emotional responses to sounds, hearing acuity, and isolation, specifically objective isolation (social disconnectedness) and subjective isolation (loneliness). It was predicted that ratings of valence in response to pleasant and unpleasant stimuli would influence the relationship between hearing loss and isolation. Participants included 83 adults, without depression, who were categorized into three groups (young with normal hearing, older with normal hearing, and adults with mild-to-moderately severe hearing loss). Participants made ratings of valence in response to pleasant and unpleasant nonspeech sounds, presented at a moderate overall level in the laboratory. Participants also completed questionnaires related to social disconnectedness and loneliness. Data were analyzed using multiple regression with questionnaire scores as dependent variables. Independent variables were age, gender, degree of hearing loss, perceived hearing handicap, number of depressive symptoms, mean valence rating in response to unpleasant sounds, and mean valence rating in response to pleasant sounds. Emotional responses to pleasant sounds explained significant variability in scores of both social disconnectedness and loneliness. Depressive symptoms also explained variability in loneliness scores. Hearing loss was not significantly related to social disconnectedness or loneliness, although it was the only variable significantly related to ratings of valence in response to pleasant sounds. Emotional responses to pleasant sounds are related to disconnectedness and loneliness. Although not related to isolation in this study, hearing loss was related to emotional responses. Thus, emotional responses should be considered in future models of isolation and hearing loss.
Involvement of the glutamine RF-amide peptide and its cognate receptor GPR103 in prostate cancer

Glutamine RF-amide peptide (QRFP) belongs to the RFamide neuropeptide family, which is involved in a wide spectrum of biological activities, ranging from food intake and cardiovascular functioning to analgesia, aldosterone secretion, locomotor activity and reproduction. Recently, QRFP has been demonstrated to exert its effects by activating the G protein-coupled receptor GPR103. QRFP is expressed in the brain and peripherally in the adipose tissue, bladder, colon, testis, parathyroid and thyroid gland, as well as in the prostate gland. Following lung cancer, prostate cancer constitutes the second most frequently diagnosed cancer among men, whilst obesity appears to be a contributing factor for aggressive prostate cancer. In the present study, we sought to investigate the role of QRFP in prostate cancer, using two androgen-independent human prostate cancer cell lines (PC3 and DU145) as in vitro experimental models and clinical human prostate cancer samples. The expression of both QRFP and GPR103 at the gene and protein level was higher in human prostate cancer tissue samples compared to control and benign prostatic hyperplasia (BHP) samples. Furthermore, in both prostate cancer cell lines used in the present study, QRFP treatment induced the phosphorylation of ERK1/2, p38, JNK and Akt. In addition, QRFP increased cell migration and invasion in these in vitro models, with the increased expression of MMP2. Furthermore, we demonstrated that the pleiotropic adipokine, leptin, increased the expression of QRFP and GPR103 in PC3 prostate cancer cells via a PI3K- and MAPK-dependent mechanism, indicating a novel potential link between adiposity and prostate cancer. Our findings expand the existing evidence and provide novel insight into the implication of QRFP in prostate cancer.