Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and Robust Spike-Driven Deep Convolutional Neural Networks Based on NOR Flash Computing Array

In this article, we propose an efficient and robust spike-driven convolutional neural network (SCNN) based on the NOR flash computing array (NFCA), which is mapped by the pretrained convolutional neural network with the same structure. The spike-driven system eliminates the additional analog-to-digital/digital-to-analog (AD/DA) conversion in the NFCA-based CNN. To study the performance of the hardware implementation, an NFCA-based SCNN for the recognition of the Mixed National Institute of Standards and Technology (MNIST) data set is simulated. Simulation results illustrate that the system achieves 97.94% accuracy with the computing speed of $1 \times 10^{6}$ frame per second (fps). Compared with the typical mixed-signal NFCA-based CNN, the NFCA-based SCNN saves 97% area and 56% energy consumption. Moreover, the NFCA-based SCNN demonstrates great robustness to 30% image noise with less than 2% accuracy loss. The impact of random telegraph noise (RTN) is also greatly reduced in which less than 1% accuracy decrease can be achieved at the 32-nm technology node.
Persistent proliferation of keratinocytes and prolonged expression of pronociceptive inflammatory mediators might be associated with the postoperative pain in KK mice

Epidermal keratinocytes play a vital role in restoration of the intact skin barrier during wound healing. The negative effect of hyperglycemia may prolong the wound healing process. Epidermal keratinocytes have been demonstrated to modulate and directly initiate nociceptive responses in rat models of fractures and chemotherapy-induced neuropathic pain. However, it is unclear whether epidermal keratinocytes are involved in the development and maintenance of incisional pain in nondiabetic or diabetic animals. In the current study, using behavioral tests and immunohistochemistry, we investigated the differential keratinocytes proliferation and expression of pronociceptive inflammatory mediators in keratinocytes in C57BL/6J mice and diabetic KK mice. Our data showed that plantar incision induced postoperative pain hypersensitivity in both C57BL/6J mice and KK mice, while the duration of postoperative pain hypersensitivity in KK mice was longer than that in C57BL/6J mice. Moreover, plantar incision induced the keratinocytes proliferation and expression of IL-1β and TNF-α in keratinocytes in both C57BL/6J mice and KK mice. Interestingly, compared to C57BL/6J mice, the slower and more persistent proliferation of keratinocytes and expression of IL-1β and TNF-α in keratinocytes were observed in KK mice. Together, our study suggested that plantar incision may induce the differential keratinocytes proliferation and expression of IL-1β and TNF-α in kertinocytes in diabetic and nondiabetic animals, which might be associated with the development and maintenance differences in diabetic and nondiabetic postoperative pain.
Genetic characterization of a multiparent recombinant inbred line of rice population

The field experiment was conducted at test base in Department of Agronomy, Yangtze University and Rice Research Institute of Guangdong, Academy of Agricultural Sciences, during 2010–11 to study the genetic characterization of a multiparent recombinant inbred line rice population. Multi-parent advanced generation inter-cross (MAGIC) population can be used to dissect complex agronomic traits by both linkage and association mapping. In this study, we report the development of the first rice MAGIC population with 12 founder lines. A subset of 206 lines of the MAGIC population was analyzed with SSR markers. The results indicated that plentiful recombination types were detected in the MAGIC population. Cluster analysis showed that the 12 founder and 206 lines were divided into 11 and 64 groups, respectively, when the genetic similarity coefficient was 0.685. Single-marker analysis detected 13 and 25 SSR markers associated with plant height and heading date, respectively. Step-wise regression further confirmed that 5 and 11 of the markers were associated with plant height and heading date, respectively. Seven and 11 of the identified markers had been reported previously to be linked to plant height and heading date QTLs, respectively. The results showed that MAGIC populations had abundant genetic diversity and could be efficiently used to study multi-alleles and association analysis.
What influences child feeding in the Northern Triangle? A mixed‐methods systematic review

Abstract Optimising child feeding behaviours could improve child health in Guatemala, Honduras and El Salvador, where undernutrition rates remain high. However, the design of interventions to improve child feeding behaviours is limited by piecemeal, theoretically underdeveloped evidence on factors that may influence these behaviours. Between July 2018 and January 2020, we systematically searched Cochrane, Medline, EMBASE, Global Health and LILACS databases, grey literature websites and reference lists, for evidence of region‐specific causes of child feeding behaviours and the effectiveness of related interventions and policies. The Behaviour Change Wheel was used as a framework to synthesise and map the resulting literature. We identified 2,905 records and included 68 relevant studies of mixed quality, published between 1964 and 2019. Most (n = 50) were quantitative, 15 were qualitative and three used mixed methods. A total of 39 studies described causes of child feeding behaviour; 29 evaluated interventions or policies. Frequently cited barriers to breastfeeding included mothers' beliefs and perceptions of colostrum and breast milk sufficiency; fears around child illness; and familial and societal pressures, particularly from paternal grandmothers. Child diets were influenced by similar beliefs and mothers' lack of money, time and control over household finances and decisions. Interventions (n = 22) primarily provided foods or supplements with education, resulting in mixed effects on breastfeeding and child diets. Policy evaluations (n = 7) showed positive and null effects on child feeding practices. We conclude that interventions should address context‐specific barriers to optimal feeding behaviours, use behaviour change theory to apply appropriate techniques and evaluate impact using robust research methods.
An Improved Deep Learning Approach for Thyroid Nodule Diagnosis

Although thyroid ultrasonography (US) has been widely applied, it is still difficult to distinguish benign and malignant nodules. Currently, convolutional neural network (CNN) based methods have been proposed and shown promising performance for benign and malignant nodules classification. It is known that the US images are usually captured in multi-angles, and the same thyroid in different US images have inconsistent content. However, most of the existing CNN based methods extract features using fixed convolution kernels, which could be a big issue for processing US images. Moreover, fully-connected (FC) layers are usually adopted in CNN, which could cause the loss of inter-pixel relations. In this paper, we propose a new CNN which is integrated with squeeze-and-excitation (SE) module and maximum retention of inter-pixel relations module (CNN-SE-MPR). It can adaptively select features from different US images and preserve the inter-pixel relations. Moreover, we introduce transfer learning to avoid problems such as local optimum and data insufficiency. The proposed network is tested on 407 thyroid US images collected from cooperated hospitals. Confirmed by ablation experiments and the comparison experiments under the state-of-the-art methods, it is shown that our method improves the accuracy of the diagnosis results.
Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.
Application of ozone on induction of resistance in Vigna unguiculata cv. Co 6, against Fusarium wilt

Abstract The efficacy of ozone in controlling Fusarium oxysporum, the casual agent of wilt disease, was evaluated in Vigna unguiculata (cowpea). Different concentrations of ozone (T1, T2, T3, T4 and T5) were passed to the Vigna unguiculata seedlings among which T3 treatment reduced Fusarium wilt more effectively than other treatments by enhancing growth promotion along with the activation of the defense-related enzymes than other treatments. T3 treatment provokes enhanced biomass production along with increased activity of chitinase, β-1,3-glucanase, peroxidase and phenylalanine ammonia-lyase than other treatments relative to control seedlings. Moreover, this is the first report of ozone protecting seedlings against Fusarium wilt of Vigna unguiculata plants.
III-N/Si₃N₄ Integrated Photonics Platform for Blue Wavelengths

In this paper, we report a low-loss photonic integrated circuits (PICs) platform at blue wavelengths of the visible spectral regime. Silicon nitride (SiN) is a popular passive waveguide material due to its fabrication flexibility, CMOS compatibility and spectral transparency in this wavelength regime. For active devices including lasers, gallium nitride (GaN) and its alloys are considered. Several basic building blocks for the development of a complete integrated platform, including blue diode lasers, in-plane- and out-of-plane light couplers, as well as on-and off-chip coupling between these active and passive components are theoretically investigated. The proposed in-plane and out-of-plane architectures operate through edge- and vertical grating couplers (VGCs), respectively. With edge-coupling, large mode-mismatch between the GaN laser diode and SiN waveguide is alleviated through nanotapers on both the active and passive sections and the calculated peak coupling efficiency is achieved to be 74% at a wavelength of 450 nm. We also separately designed efficient VGCs for coupling light from standard, commercial, off-the-shelf fibers to the SiN chip, and edge couplers for fiber-chip coupling, exhibiting coupling efficiencies of 51% and 83%, respectively. For robust on-chip light-coupling between active and passive circuit elements, with relaxed alignment tolerances, two approaches, i.e., flip-chip based hybrid integration and evanescent coupling based heterogeneous integration are studied. Calculated maximum coupling efficiencies of 40% (−4 dB) are achieved for both the hybrid and heterogeneous schemes. The theoretical work performed is an initial step towards demonstrating complex blue PICs which could offer a comprehensive range of photonic functionalities.
Greater variability in lipid measurements associated with cardiovascular disease and mortality: A 10‐year diabetes cohort study

To examine the associations between variability in lipids and the risk of cardiovascular disease (CVD) and mortality in patients with type 2 diabetes based on low‐density lipoprotein‐cholesterol (LDL‐C), the total cholesterol (TC) to high‐density lipoprotein‐cholesterol (HDL‐C) ratio and triglycerides (TG).
Deep Learning for Time Averaged Wall Shear Stress Prediction in Left Main Coronary Bifurcations

Analysing blood flow in coronary arteries has often been suggested in aiding the prediction of cardiovascular disease (CVD) risk. Blood flow induced hemodynamic indices can function as predictive measures in this pursuit and a fast method to calculate these may allow patient specific treatment considerations for improved clinical outcomes in the future. In vivo measurements of these metrics are not practical and thus computational fluid dynamic simulations (CFD) are widely used to investigate blood flow conditions, but require costly computation time for large scale studies such as patient specific considerations in patients screened for CVD. This paper proposes a deep learning approach to estimating the well established hemodynamic risk indicator time average wall shear stress (TAWSS) based on the vessel geometry. The model predicts TAWSS with good accuracy, achieving cross validation results of average Mean Absolute error of 0.0407 Pa and standard deviation of 0.002 Pa on a 127 patient CT angiography dataset, while being several orders of magnitude faster than computational simulations, using the vessel radii, angles between bifurcation (branching) vessels, curvature and other geometrical features. This bypasses costly computational simulations and allows large scale population studies as required for meaningful CVD risk prediction.
Epidermolysis bullosa acquisita (EBA) is a chronic sub- epidermal blistering disorder characterized by circulating IgG anti-basement membrane zone (BMZ) auto-antibo-

Journal Compilation © 2010 Acta Dermato-Venereologica. ISSN 0001-5555 Epidermolysis bullosa acquisita (EBA) is a chronic subepidermal blistering disorder characterized by circulating IgG anti-basement membrane zone (BMZ) auto-antibodies (1, 2). The EBA antigen has been identified as type VII collagen (3). More than 100 patients with coexisting psoriasis and autoimmune bullous diseases, such as bullous pemphigoid (BP) and pemphigus vulgaris (PV), have been reported in the literature but there have been only a few such cases of EBA (3–5).
Effects of Resident Education and Self-Implementation of Integrated Pest Management Strategy for Eliminating Bed Bug Infestation in Ahvaz City, Southwestern Iran

Background: Bed bugs as blood-sucking insects have become a public health problem in urban communities throughout the world. The objective of this study was to determine the effects of resident education and self-implementation of Integrated Pest Management (IPM) strategy for eliminating bed bug infestation in infected apartments in Ahvaz City, Iran. Methods: This interventional study was conducted in seventy apartments infested with bed bug (Cimex lectularius) in Ahvaz City, southwest Iran, during a 6-month period. The bed bug infestations reported to Health centers were inspected visually and confirmed by medical entomology experts. Then, the heads of the households were received techniques/tools of the IPM program and trained by medical entomology experts before self-implementation of control methods. Finally, the infected apartments were inspected by the experts at 1, 3, and 6 months after intervention and data were recorded in a checklist. Results: From the seventy infected apartments, 57%, 28% and 15% were considered as low, moderate, and high level infestation respectively. The bed bug infestation was eliminated from 53 apartments (76%) after one month and it reached to 62 apartments (88%) by the end of third month. Finally, after six months of applying IPM program, bed bugs infestation was eliminated from all infected apartments (100%). Residents expressed their 100% satisfaction with applying the bed bug IPM strategy. Conclusion: Training residents to implement the IPM program can reduce pest control costs, the volume of pesticides consumed, and human exposure to chemical pesticides, resulting in increased human and environmental health and safety.
Impact of agile testing over traditional testing

Nowadays, most of the companies are adopting the Agile method for developing and introducing the Agile new software product as a per customer needs. In this model or product, the agile testing deals with rapid working and successful in small situations. The other old approaches like waterfall, prototype etc. model where testing are done at the later phase, i
Fishing down Canadian aquatic food webs

The mean trophic level (TL) of fish landed in fisheries on the east and west coasts of Canada is declining by 0.030.10·decade1, similar to global trends. This finding is based on data from United Nations Food and Agriculture Organization and the Canadian Department of Fisheries and Oceans and other Canadian sources for the period 18731997. Significant rates of decline in mean TL were obtained even when key species  Atlantic cod (Gadus morhua) on the east coast and Pacific herring (Clupea pallasi) and Pacific hake (Merluccius productus) on the west coast  were omitted from the analysis. Fish taken in inland water fisheries did not exhibit a decline in mean TL. Two models were developed, based on length and age, respectively, for correcting TL estimates of individual species for the effects of changes in body size due to changes in fishing mortality. Both produced corrections that were small relative to changes in mean TL that resulted from changes in species composition of the catch over time. Overall.
The Round Complexity of Perfect MPC with Active Security and Optimal Resiliency

In STOC 1988, Ben-Or, Goldwasser, and Wigderson (BGW) established an important milestone in the fields of cryptography and distributed computing by showing that every functionality can be computed with perfect (information-theoretic and error-free) security at the presence of an active (aka Byzantine) rushing adversary that controls up to $n/3$ of the parties. We study the round complexity of general secure multiparty computation in the BGW model. Our main result shows that every functionality can be realized in only four rounds of interaction, and that some functionalities cannot be computed in three rounds. This completely settles the round-complexity of perfect actively-secure optimally-resilient MPC, resolving a long line of research. Our lower-bound is based on a novel round-reduction technique that allows us to lift existing three-round lower-bounds for verifiable secret sharing to four-round lower-bounds for general MPC. To prove the upper-bound, we develop new round-efficient protocols for computing degree-2 functionalities over large fields, and establish the completeness of such functionalities. The latter result extends the recent completeness theorem of Applebaum, Brakerski and Tsabary (TCC 2018, Eurocrypt 2019) that was limited to the binary field.
vCNN: Verifiable Convolutional Neural Network

Inference using convolutional neural networks (CNNs) is often outsourced to the cloud for various applications. Hence it is crucial to detect the malfunction or manipulation of the inference results. To provide trustful services, the cloud services should prove that the inference results are correctly calculated with valid input data according to a legitimate model. Particularly, a resource-constrained client would prefer a small proof and fast verification. A pairing-based zero-knowledge Succinct Non-interactive ARgu-ment of Knowledge(zk-SNARK) scheme is a useful cryptographic primitive that satisfies both the short-proof and quick-verification requirements with only black-box access to the models, irrespective of the function complexity. However, they require tremendous efforts for the proof generation. It is impractical to build a proof us-ing traditional zk-SNARK approaches due to many (multiplication) operations in CNNs. This paper proposes a new efficient verifiable convolution neural network (vCNN) framework, which allows a client to verify the correctness of the inference result rapidly with short evidence provided by an untrusted server. Notably, the proposed vCNNs framework is the first practical pairing-based zk-SNARK scheme for CNNs, and it significantly reduces space and time complexities to generate a proof with providing perfect zero-knowledge and computational knowledge soundness. The experimental results validate the practicality of vCNN with improving VGG16 performance and key size by 18000 fold compared with the existing zk-SNARKs approach (reducing the key size from 1400 TB to 80 GB, and proving time from 10 years to 8 hours).
Genotype-environment interactions in tea (Camellia sinensis L.) and their implications in tea breeding and selection

SUMMARY An analysis of yield data from 31 tea clones grown in replicated trials at four locations revealed the presence of significant genotype-environment interactions. Joint regression analysisshowed that a significant proportion of these interactions could be attributed to differences in the linear response of each genotype (clone) to the range of environments. However, estimates of the deviations from regression were also significant, indicating the presence of some unpredictable variation. The response of each individual clone was estimated by computing the regression coefficient of individual yield on environmental mean yield at each location. The clones used in this study had regression values ranging from 0·19 to 2·08, indicating considerable genotypic difference in response to environmental change. Genetic variation in adaptation was also found to be present. Clones of the Assam type were found to possess general adaptation whereas the one clone of Indo-Chinese origin was specifically adapted to low-yielding environments. Some known features of several tea clones are explained on the basis of the linear regression model and a more scientific basis is provided for the recommendations of clones for differentdistricts. The implications of genotype-environment interactions in practical tea breeding and selection are also discussed.
Applied Mathematics and Computation

Ensemble clustering has been attracting increasing attention in recent years, because it is able to combine multiple base clusterings (ensemble members) into a more robust clustering. It mainly consists of two parts, generating multiple ensemble members and ﬁnding a ﬁnal partition. The construction of the information matrix plays an important role for ﬁnding a ﬁnal partition. In general categorical data ensemble clustering framework, most existing information matrices are constructed only relying on label information of ensemble members without considering original information of data sets. To solve this problem, a new ensemble clustering framework for categorical data is proposed, in which the information matrix considers label information and original data information together, and is instantiated into the ALM matrix in this paper. The ALM matrix takes account of not only the distribution of attribute content in each ensemble member, but also the relationship among ensemble members based on the distribution. To simplicity, the k -means technique is used to cluster the ALM matrix and form a new ensemble clustering algorithm. The experimental results have shown the beneﬁts of the ALM matrix by comparing the proposed algorithm with other ensemble clustering algorithms.