FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Deep learning and Convolutional Neural Network (CNN) have becoming increasingly more popular and important in both academic and industrial areas in recent years cause they are able to provide better accuracy and result in classification, detection and recognition areas, compared to traditional approaches. Currently, there are many popular frameworks in the market for deep learning development, such as Caffe, TensorFlow, Pytorch, and most of frameworks natively support CPU and consider GPU as the mainline accelerator by default. FPGA device, viewed as a potential heterogeneous platform, still cannot provide a comprehensive support for CNN development in popular frameworks, in particular to the training phase. In this paper, we firstly propose the FeCaffe, i.e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe to enable FPGA to support mainline deep learning development features, e.g. training and inference with Caffe. Furthermore, we provide some benchmarks with FeCaffe by taking some classical CNN networks as examples, and further analysis of kernel execution time in details accordingly. Finally, some optimization directions including FPGA kernel design, system pipeline, network architecture, user case application and heterogeneous platform levels, have been proposed gradually to improve FeCaffe performance and efficiency. The result demonstrates the proposed FeCaffe is capable of supporting almost full features during CNN network training and inference respectively with high degree of design flexibility, expansibility and reusability for deep learning development. Compared to prior studies, our architecture can support more network and training settings, and current configuration can achieve 6.4x and 8.4x average execution time improvement for forward and backward respectively for LeNet.Comment: 11 pages, 7 figures and 4 table

Production of dibaryon dNΩd_{N\Omega} in kaon induced reactions

In this work, we propose to investigate the $d_{N\Omega}$ dibaryon production in the process $K^- p \rightarrow d_{N\Omega} \bar{\Xi}^0$ by utilizing the kaon beam with the typical momentum to be around 10 GeV, which may be available at COMPASS, OKA@U-70 and SPS@CERN. The cross sections for $K^- p \rightarrow d_{N\Omega} \bar{\Xi}^0$ are estimated and in particular, the cross sections can reach up to $577.20\ \mathrm{\mu b}$ at $P_{K}=20$ GeV. Considering that $d_{N\Omega}$ dominantly decay into $\Xi \Lambda$ and $\Xi \Sigma$, we also estimate the cross sections for $K^- p \to \Xi^0 \Lambda \bar{\Xi}^0$ and $K^- p \to \Xi^+ \Sigma^- \bar{\Xi}^0$, which can reach up to $134.89$ and $5.93 \ \mathrm{\mu b}$, respectively, at$P_K=20$ GeV.Comment: 7 pages, 4 figure

hidden charm decays of X(4014)X(4014) in a D∗Dˉ∗D^{*}\bar{D}^{*} molecule scenario

Inspired by the recent observation of a new structure, $X(4014)$, in the process $\gamma\gamma\to \gamma\psi(2S)$, we evaluate the possibility of assigning $X(4014)$ as a $D^\ast \bar{D}^\ast$ molecular state with $I(J^{PC})=0(0^{++})$ by investigating the hidden charm decays of $X(4014)$. The partial widths of $J/\psi\omega$, $\eta_{c}\eta$ and $\eta_{c}\eta^{\prime}$ channels are evaluated to be about $(0.41\sim 5.00)$, $(2.05\sim7.49)$ and $(0.11\sim0.51)\ \mathrm{MeV}$, respectively. Considering the experimental observation and the present estimations, we proposed to search $X(4014)$ in the $\gamma \gamma \to J/\psi \omega$ process in Belle II.Comment: 7 pages, 5 figures, accepted for publication in Phys. Rev.

Expressions of Wingless and Int1 (Wnt)-induced secreted protein 1 in paraquat-poisoned patients

Purpose: To study the expression of Wingless & Int1 (Wnt)-induced secreted protein 1 (WISP1) in paraquat (PQ)-poisoned patients.Methods: A total of 37 PQ-poisoned patients were enrolled in the study, and divided into non-survivor group (NS) and survival group (S) based on the final therapeutic outcome. Besides, another normal control group (NC) comprised of normal healthy people. Serum PQ concentration was determined by high performance liquid chromatography (HPLC), while reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) were used to evaluate WISP1 in the serum of PQ poisoned patients.Results: PQ intake in NS and S groups were 23.58 ± 26.23 and 143.18 ± 263.04 mL, respectively, while serum PQ concentration was 2.07 ± 0.67 and 4.12 ± 1.74 mg/L, respectively. Significant correlation was found between the outcome of patients and serum PQ concentration (OR = 1.434, p < 0.01). Serum PQ concentration was closely correlated with WISP1 gene expression levels (OR = 0.621, p < 0.01) and serum WISP1 protein levels (OR = 0.596, p < 0.01) on the first day after poisoning. Furthermore, a correlation between serum PQ concentration and WISP1 levels was found on the third after poisoning (OR = 0.447, p < 0.01).Conclusion: WISP1 is over-expressed in PQ-poisoned patients, and serum PQ concentration may be a useful index for the prognosis of PQ poisoned patients.Keywords: Wingless & Int1 (Wnt)-induced secreted protein 1, Poison, Paraquat, Prognosis, Correlatio

Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978

Extracellular Matrix Peptides of Artemia Cyst Shell Participate in Protecting Encysted Embryos from Extreme Environments

BACKGROUND: Many species of the brine shrimp Artemia are found in various severe environments in many parts of the world where extreme salinity, high UV radiation levels, high pH, anoxia, large temperature fluctuations, and intermittent dry conditions are often recorded. To withstand adverse environments, Artemia undergoes an oviparous developmental pathway to release cysts whereas, under favorable conditions, swimming nauplius larvae are formed directly via an ovoviviparous pathway. In the former case these cysts have an extraordinary ability to keep the embryos protected from the harsh environment for long periods. This is achieved through the protection by a complex out-wrapping cyst shell. However, the formation and function of the cyst shell is complex; the details remain largely unclear. PRINCIPAL FINDING: A shell gland-specific gene (SGEG2) was cloned and identified from a suppression subtractive hybridization library. Western blot analysis showed that SGEG2 presumably requires post-translational proteolysis in order to be processed into two mature peptides (SGEG2a and 2b). The three matrix peptides (SGEG1 reported previously, 2a, and 2b) were found to distribute throughout the cyst shell. The results of gene knockdown by RNAi and subsequent resistance to environmental stresses assays indicated that these matrix peptides are required for cyst shell formation and are involved in protecting the encysted embryos from environmental stress. CONCLUSIONS/SIGNIFICANCE: This study revealed that extracellular matrix peptides participate in protecting embryos from extreme salinity, UV radiation, large temperature fluctuations and dry environments, thereby facilitating their survival. The cyst shell provides an excellent opportunity to link the ecological setting of an organism to the underlying physiological and biochemical processes enabling its survival. The cyst shell material has also a high potential to become an excellent new biomaterial with a high number of prospective uses due, specifically, to such biological characteristics

Long-distant contribution and χc1\chi_{c1} radiative decays to light vector meson

The discrepancy between the PQCD calculation and the CLEO data for $\chi_{c1}\to \gamma V$ ($V=\rho^0,\,\omega,\,\phi$) stimulates our interest in exploring extra mechanism of $\chi_{c1}$ decay. In this work, we apply an important non-perturbative QCD effect, i.e., hadronic loop mechanism, to study $\chi_{c1}\to \gamma V$ radiative decay. Our numerical result shows that the theoretical results including the hadronic loop contribution and the PQCD calculation of $\chi_{c1}\to \gamma V$ are consistent with the corresponding CLEO data of $\chi_{c1}\to \gamma V$. We expect further experimental measurement of $\chi_{c1}\to \gamma V$ at BES-III, which will be helpful to test the hadronic loop effect on $\chi_{c1}$ decay.Comment: 7 pages, 2 figures. Accepted for publication in Eur. Phys. J.

Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling

Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental yet challenging aspect of natural language processing (NLP). However, existing evaluation benchmarks primarily focus on the evaluation of inter-sentence properties and overlook critical discourse phenomena that cross sentences. To bridge the gap, we propose Disco-Bench, a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks, covering understanding, translation, and generation. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena (e.g. cohesion and coherence) in Chinese and/or English. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge. We totally evaluate 20 general-, in-domain and commercial models based on Transformer, advanced pretraining architectures and large language models (LLMs). Our results show (1) the challenge and necessity of our evaluation benchmark; (2) fine-grained pretraining based on literary document-level training data consistently improves the modeling of discourse information. We will release the datasets, pretrained models, and leaderboard, which we hope can significantly facilitate research in this field: https://github.com/longyuewangdcu/Disco-Bench.Comment: Zhaopeng Tu is the corresponding autho