Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo imaging of blood-brain barrier disruption in a multiple sclerosis animal model

Multiple sclerosis is an inflammatory disease of the central nervous system that affects many young adults worldwide. Despite research efforts, the cause of the disease remains unknown. In order to develop better therapies and ultimately find a cure, animal models are used to identify cellular biomarkers. Characterizing these models is essential in order to properly relate findings to the human disease and test possible medications. This thesis is the result of a three-year Master’s project at Centre de recherche de l’Institut universitaire en santé mentale de Québec (CRIUSMQ) that aimed to study the blood-brain barrier as a possible biomarker of disease pathology in a multiple sclerosis animal model. Chapter 1 introduces the different themes addressed in this project. Chapters 2, 3 and 4 focus on the materials and methods chosen, while chapter 5 presents the results obtained. These findings are discussed in chapter 6.
LABORATORY DATA COMPRESSION

Most of the existing tools for lossless data compression, including LHA , Zip , gzip , and bzip2 , are based on either textual substitution (LZ77 or LZ78) or block sorting, followed by entropy coding. These tools assume that the data have clear 8-bit boundaries and contain many repetitive substrings. Laboratory data such as A/D converter outputs, however, does not in general satisfy these conditions. To compress such data, we developed a general-purpose real-time compression library suitable for quantized (up to 16-bit) time-series data of unlimited number of channels. The ﬁrst part of the algorithm adaptively chooses a prediction model among a family of polynomials, and estimates the variance of the prediction residuals. The second part of the algorithm encodes the residuals by length-limited minimum-redundancy coding, assuming either Gaussian or Laplace distributions. The library is used by our Java-based data management system developed for the National Institute for Fusion Science (NIFS). It can also be used as a standalone compression tool. Typical compression ratio is around 4 : 1, and compression/decompression through-puts are around 2-million 16-bit samples per second on a 400MHz Pentium-II PC running Linux.
Research of Cultivation and Domestication of Festuca kivilovii steud

Characteristic and cultivated technique of Festuca kiviloii steud were introduced in the paper.Festuca kiviloii steud adapted to being extended in the alpine pasture for high yield,high quality,strong adaptation,cold,drought and salt resistance.It can be planted as permanent sown grassland with other forage for its blooming tiller,strong regeneration and trample resistance.It was a high quality forage to improve grassland in alpine area and planting lawn.
The Variational Playground for Machine Learning in Statistical Physics

The past decade has seen a prodigious rise of machine learning (ML) applications to several domains, including computer vision, automation, cybersecurity, and much more. The high reputation of ML techniques, and most chiefly deep learning [1], comes from the fact that they have successfully solved key problems in those applied areas, substantially improving on existing technology. Given the generality of ML-based approaches, in recent years we are witnessing a massive wave of ML applications to fundamental sciences, including physics. This momentum, however, often makes the task of navigating through new literature rather hard. In this respect, it is often challenging to grasp what the real potential of ML applications to physics is, once some of the inevitable hype associated with the rise of this new field is safely kept under control. One criterion I find useful for this purpose, is to focus on whether the proposed ML technology allows to successfully, and convincingly address existing challenging problems, improving on existing computational techniques. In this sense, what makes ML applications to pure science clearly gaugeable, is that it is possible to devise very stringent criteria to establish the success or failure of ML techniques applied to the solution of challenging problems. For example, whereas it can be somehow subjective to establish a baseline performance for image classification, it is much less subjective to establish how well a machine must perform to improve on existing techniques in the realm of computational physics. In this sense, ML applications targeting the direct solution of challenging physical problems are natural candidates to assess the advances in the field, and try to answer to some of the skeptical, “it’s all-hype” voices. Wu, Wang, and Zhang consider the ambitious problem of solving statistical mechanics. Take for example a classical spin system, described by the Boltzmann probability density
Penalty-based Sequence Generative Adversarial Networks with Enhanced Transformer for Text Generation

In this paper, we propose a new model to solve the problem of text generation, which is based on the concept of seqGAN, combines self-attention with modeling localness, and introduces the penalty-based objective function. This model has much better performance than the original model. In the original model, the generator’s ability of the text feature extraction is insufficient. We introduce self-attention with modeling localness, which greatly enhances its ability to capture long distance and shortrange dependencies. In addition, we use the penalty-based objective function instead of the loss function of the original model to solve the problem of mode collapse. Experimental results demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the quality of generated texts.
The effect of attention and interpretation therapy on psychological resilience, cancer-related fatigue, and negative emotions of patients after colon cancer surgery.

BACKGROUND Colon cancer is the most common malignant tumor of the gastrointestinal tract. This cancer and the related treatments bring a raft of lasting physiological and psychological impacts to patients. This study explored the effects of attention and interpretation therapy (AIT) on improving psychological resilience, cancer-related fatigue (CRF), and negative emotions in patients after colon cancer surgery. METHODS Patients who had undergone colon cancer surgery in the Affiliated Hospital of Jiangnan University were selected and randomly allocated into an experimental group and a control group, each with 100 cases. Patients in the control group received routine intervention measures, while the experimental group received an extra 10 weeks of AIT. Before and after 10 weeks of intervention, the effects of intervention were evaluated using the Connor-Davidson Resilience Scale (CD-RISC), Self-Rating Anxiety Scale (SAS), Selfrating Depression Scale (SDS) and the Revised Piper Fatigue Scale (PFS-R). RESULTS Before the intervention, there was no statistical difference between the scores of psychological resilience, CRF, and negative emotions between the two groups (P>0.05). We compared the scores before and after the 10 weeks of intervention and found that the scores of psychological resilience of the experimental group were higher than before, and the scores of CRF and negative emotion were lower than before. After the intervention, the psychological resilience score of the experimental group was higher than that of the control group, the CRF and negative emotions scores were lower than those of the control group, and the differences were statistically significant (P<0.05). CONCLUSIONS AIT can effectively strengthen the psychological resilience of patients after colon cancer surgery to a certain extent, reduce anxiety and depression, reduce the degree of CRF, and thus improve the patients' quality of life postoperatively.
The Multimedia Sentiment Model Based on Online Homestay Reviews

: Aiming at the fact that traditional sentiment analysis is based on text, without considering the factors such as special symbols and emoticon images, which can’t fully extract the user's emotions, this paper proposes a sentiment analysis method of online homestay reviews based on image-text fusion. For text datasets, first use Word2vec to build a topic clustering model, then find the corresponding topic attribute dictionary through the topic center words, use Bayesian classifier is used for sentiment analysis, compared with SVM and decision tree methods, to evaluate the effect; For the picture dataset, Convolutional Neural Network (CNN) model is initialized by parameter migration
Improving the Boot Time of the Android OS

— Increasing application requirements in embedded systems demand additional software initialization and configuration during startup, which adversely affects system boot time. The demand for fast startup is motivated by embedded systems, not only for consumer electronics such as digital TV and mobile phones but also for devices in automotive, medical and other applications. This paper presents novel approaches to reduce the startup time of complex embedded systems, using Android OS as an example. The startup latency of Android is analyzed in order to find ways to reduce the boot time. Then to reduce startup time, we propose a better U-boot Fast-Boot based on Suspend-Resume techniques, improve the read speed of SD cards
Two-level secret sharing schemes based on reverse super edge magic labelings

In this paper, we propose two-level secret sharing scheme based on a reverse edge magic labeling of star graphs. It is a scheme which creates two types of hierarchical sets. The (cid:12)rst set contains share that are more powerful than the share in the second set. We build banking secret sharing scheme that will share a secret among one bank manager and several sets of authorized staﬀ members.
Cross border regional planning: insights from Cascadia

ABSTRACT This analysis focuses on different levels of Cross-Border Regional Planning (CBRP) processes in the Cascadia borderland. The region is home to the business-led initiative ‘Cascadia Innovation Corridor’ (CIC), designed to foster cross-border economic integration. The CIC strives to build a global innovation ecosystem in Cascadia, including a new high-speed train to connect Seattle and Vancouver. This paper focuses on the scope of the CIC as a CBRP case. The authors evaluate engagement of city governments and coherency between different planning scales to determine whether the CIC has been addressing the major challenges that may prevent tighter economicintegration in Cascadia. The analysis deploys secondary data as well as primary data collected through surveys and interviews. The results shed light on a discrepancy between supra-regional ‘soft planning’ and the urban planning level. The authors offer an evidence-based proposal to broaden the scope of the CIC from a CBRP standpoint.
A Survey of Bidding Games on Graphs

A graph game is a two-player zero-sum game in which the players move a token throughout a graph to produce an inﬁnite path, which determines the winner or payoﬀ of the game. In bidding games , both players have budgets, and in each turn, we hold an “auction” (bidding) to determine which player moves the token. In this survey, we consider several bidding mechanisms and study their eﬀect on the properties of the game. Speciﬁcally, bidding games
Bilingual Emotion Analysis on Social Media throughout the COVID19 Pandemic in Portugal

This paper presents preliminary work on the topic of emotion analysis on Twitter, in the context of the coronavirus pandemic in Portugal We collected, curated and analyzed covid-related tweets of users in Portugal in order to understand the evolution of the six basic emotions reflected in these tweets We analyzed tweets written in both English and Portuguese In this first step of our work we correlate this information with key events of the evolution of the pandemic in Portugal during March, which was the most critical period in Portugal We do so in an attempt to estimate the online manifestation of the psychological toll that this pandemic has on the overall well-being status of the general population Our findings show that the sentiment analysis of covid-related tweets is consistent with our hypothesis that negative emotions would intensify as the pandemic progressed The preliminary results obtained stand as proof of concept that the analysis of real-time tweets or other social media messages through sentiment analysis can be an important tool for behavioural and well-being tracking Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda All rights reserved
Impact assessment of climate change in cotton wheat production system

Climate change has extensive impacts on global agricultural systems, food security and bionetworks. Currently Pakistan is passing through a thriving phase of climate change threats due to its typical Agro-geo climatic position, and prevalent poverty. This study was conducted in the Institute of Agricultural and Resource Economics, University of Agriculture, Faisalabad, Pakistan during the year 2019. The study aims at estimation of socioeconomic impacts of climate change on current and future agricultural production system of Punjab, Pakistan. It evaluated economic impacts of yield reductions resulting from climate change on farm households by TOAMD model. Primary data on socioeconomic and crop production variables were collected from 165 farmers located in five districts of Punjab. Downscale climate data, crop model simulation outputs, RAPs and secondary data from global economic models were utilized in the analysis. The results of impacts analysis show that there would be significant negative impacts on current and future cotton production as cotton is highly sensitive to climate variations. Wheat production is relatively less sensitive to climate change and benefited from increased carbon dioxide concentration in some GCMs. Farmers of Cotton wheat cropping system of Punjab would be economic losers as net economic impacts are negative for all GCM in APSIM (Agricultural Production Systems simulator) and DSSAT (Decision Support System for Agro-technology Transfer). This would lead to increase in poverty in farming community. It is suggested that adaptation and mitigation strategies must be explored and practiced limiting the potential climate change damages in Pakistan.
Insensibility of Question Word Order in Visual Question Answering

In this paper we demonstrate whether the word order of input question affects the performance of existing Visual Question Answering(VQA) model trained on VQA v2 dataset. We show that randomly shufﬂing the word order of input question has little inﬂuence on the performance of VQA model. Also, we demonstrate that encoding the question with self-attention architecture that is independent of word order, shows nearly equal performance to Recurrent Neural Networks(RNNs) based encoder in existing VQA model. Based on these results, we show that existing VQA model does not utilize the word order of input question.
Multi-photon exchange processes in ultraperiphe relativistic heavy-ion collisions

The very strong electromagnetic fields present in ultraperipheral relativistic heavy-ion coll lead to important higher-order effects of the electromagnetic interaction. These multi-p exchange processes are studied using perturbation theory and the sudden or Glauber appro In many important cases, the multi-photon amplitudes factorize into independent single-p amplitudes. These amplitudes have a common impact parameter vector, which induces corr between the amplitudes. Impact-parameter dependent equivalent-photon spectra for simu excitation are calculated, as well as, impact-parameter dependent γ γ -luminosities. Excitations like the multi-phonon giant dipole resonances, vector meson production and multiple e+e−-pair production can be treated analytically in a bosonic model, analogous to the emission of soft p in QED.  2003 Elsevier B.V. All rights reserved.
Lightweight Action Recognition with Sequence-Specific Global Context

With the emergence of a large number of video resources, video action recognition is attracting much attention. Recently, realizing the outstanding performance of three-dimensional (3D) convolutional neural networks (CNNs), many works have began to apply them for action recognition and obtained satisfactory results. However, high computational over-heads greatly reduce the efficiency of 3D CNNs. To make up for the shortcoming, in this paper, we first propose two innovations — the Xwise Separable Convolution and the SS block, both of which are lightweight. Then we build an efficient 3D CNN called the XwiseNet based on our innovations. Our work aims to make 3D CNNs lightweight without reducing the recognition accuracy. The key idea of the Xwise Separable Convolution is extremely decoupling the 3D convolution in channel, spatial, and temporal dimensions. The SS block can capture temporal long-range dependencies via aggregating sequence-specific global context to each sequence feature. Experiments have verified that our XwiseNet achieves competitive performance with the least computational overhead.
Spherical Harmonics for Annotation-Free and Shape-Constrained 3D Cell Segmentation

Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. The vast amount of image data renders manual investigations a challenging or even infeasible task and varying image quality limits the applicability of automated methods. The resulting lack of annotated data not only impedes the successful training procedure of machine learning approaches
Dynamics and Control of a Quadrotor Aircraft

During flight a quadrotor, vertical take-off and landing aircraft (VTOL), experiences aerodynamic loads (forces and moments). These loads are induced by airflow about the structure of the quadrotor and consist of both laminar and turbulent airflow. Despite this fact, all existing works on controlling quadrotors either ignore these loads, or only consider loads resulting from laminar airflow. This simplification or ignorance of the aircrafts dynamics deteriorates the quadrotor's control performance in a practical implementation. To address the difficulties associated with laminar and turbulent airflow during flight, the resulting aerodynamic loads are treated as deterministic and stochastic components respectively. This paper presents an extended model for a quadrotor which takes into account aerodynamic loading during flight using Newtonian and Lagrangian mechanics. Furthermore, a combination of Euler angles and Modified Rodrigues Parameters (MRP) are used for the attitude representation of the aircraft. This is to reduce the occurrence of singularities in the dynamic equations. A combined one-step ahead backstepping and standard backstepping controller is designed to achieve path-tracking control of the quadrotor. This control method is used to compare and contrast the effects of taking into account turbulent loads with ignoring these loads on the behavior of the quadrotor. Weak and strong nonlinear Lyapunov functions are used to overcome difficulties caused by underactuation and Hessian terms induced by the stochastic differentiation rule. Themes: Aerodynamics, Turbulence.
Accurate Orientation Estimates for Deep Inertial Odometry

Many smartphone applications use inertial measurement units (IMUs) to sense movement, but the use of these sensors for pedestrian localization can be challenging due to their noise characteristics. Recent deep inertial odometry approaches to pedestrian navigation have demonstrated the increasing feasibility of inertial navigation. However, they still rely upon conventional phone orientation estimates that they assume to be accurate, while in fact these orientation estimates can be a signiﬁcant source of error. To address the problem of inaccurate orientation estimates, we present a data-driven inertial localization pipeline that performs both device orientation and position estimation using a commodity smartphone. Our system ﬁrst estimates device orientation via a deep recurrent neural network component, which is coupled with raw IMU measurements to estimate device positions via a second deep network component. To improve the robustness of the orientation estimates, we introduce a dynamic magnetometer calibration network and implement an Extended Kalman Filter to generate accurate orientation estimates from the calibrated magnetic ﬁeld. Our proposed model outperforms state-of-the-art methods by 40% in orientation error and up to 50% in position error on a large dataset we constructed that contains 20 hours of pedestrian motion across 3 large buildings and 15 subjects.
RealNodes: Interactive and Explorable 360 ◦ VR System with Visual Guidance User Interfaces

BSTRACT Emerging research expands the idea of using 360-degree panora-mas of the real-world for “360 VR” experiences beyond video and image viewing. However, most of these are strictly guided, with few opportunities for interaction or exploration. There is a desire for experiences with cohesive virtual environments with choice in navigation, versus scripted experiences with limited interaction. Unlike standard VR with the freedom of synthetic graphics, there are challenges in designing user interfaces (UIs) for 360 VR navigation within the limitations of ﬁxed assets. We designed RealNodes, a novel software system that presents an interactive and explorable 360 VR environment. We also developed four visual guidance UIs for 360 VR navigation. The results of a comparative study of these UIs determined that choice of user interface (UI) had a signiﬁcant effect on task completion times, showing one of the methods, Ar-row, was best. Arrow also exhibited positive but non-signiﬁcant trends in preference, user engagement, and simulator-sickness. Re-alNodes and the comparative study contribute preliminary results that inspire future investigation of how to design effective visual guidance metaphors for navigation in applications using novel 360 VR environments.
LSTM-Based Quantitative Trading Using Dynamic K-Top and Kelly Criterion

With the strong capability of modeling time sequence, long short-term memory (LSTM) networks have been widely applied to predicting financial time series. This has attracted tremendous attention in the quantitative trading area. A complete quantitative trading system usually has three tasks, including market timing, stock selection, and portfolio management. In this paper, we present an LSTM-based quantitative trading system and optimize this system from the following two aspects. Firstly, in the process of stock selection, we first introduce the dynamic K-top method in the LSTM-based quantitative trading system to follow the market change. Secondly, concerning portfolio management, we further incorporate the Kelly Criterion to attain an appropriate position ratio. Taking CSI300 constituent stocks as the study example, extensive experiments have been carried out to show the superiority of the proposed method. In comparison with the straight forward LSTM-based trading strategy, the improved LSTM-based trading strategy with the dynamic K-top method and the Kelly Criterion can achieve an increase of 44.97% over ten days in terms of accumulative return. In addition, our novel method can gain a win ratio of 55.95%, a monthly alpha of 0.16, a monthly Sharpe ratio of 2.17, and a monthly Sortino ratio of 2.96 disregarding the transaction costs.