Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional effects of chimeric antigen receptor co-receptor signaling domains in human regulatory T cells

Comparison of 10 co-receptor variant CARs revealed a requirement for CD28wt for optimal suppression by human regulatory T cells. Regulating the regulatory cells Regulatory T cells (Tregs) are antigen-specific immunosuppressive cells that help prevent undesired immune responses. Chimeric antigen receptors (CARs) are engineered T cell receptors that allow direct recognition of a target antigen without requiring antigen presentation. CARs, most often, have been used with conventional T cells, directing them to attack cancer cells, for example. CAR Tregs have also been developed for treating autoimmune disorders or transplant rejection, but their biology is not as well understood. Dawson et al. addressed this knowledge gap by systematically studying and optimizing CAR signaling domains in Tregs and then applying them to a mouse model of graft-versus-host disease. Antigen-specific regulatory T cells (Tregs) engineered with chimeric antigen receptors (CARs) are a potent immunosuppressive cellular therapy in multiple disease models and could overcome shortcomings of polyclonal Treg therapy. CAR therapy was initially developed with conventional T cells, which have different signaling requirements than do Tregs. To date, most of the CAR Treg studies used second-generation CARs, encoding a CD28 or 4-1BB co-receptor signaling domain and CD3ζ, but it was not known if this CAR design was optimal for Tregs. Using a human leukocyte antigen–A2–specific CAR platform and human Tregs, we compared 10 CARs with different co-receptor signaling domains and systematically tested their function and CAR-stimulated gene expression profile. Tregs expressing a CAR encoding CD28wt were markedly superior to all other CARs tested in an in vivo model of graft-versus-host disease. In vitro assays revealed stable expression of Helios and an ability to suppress CD80 expression on dendritic cells as key in vitro predictors of in vivo function. This comprehensive study of CAR signaling domain variants in Tregs can be leveraged to optimize CAR design for use in antigen-specific Treg therapy.
Binary Executable Files Homology Detection with Genetic Algorithm

Software homology detection is very meaningful for software copyright protection and malicious code variants detection. In this paper, we propose a genetic algorithm to justify the binary code similarity. First of all, the binary executable ﬁles are converted into control ﬂow graph , and then use genetic algorithm to compute the similarity among control ﬂow graphs, which is regarded as the evaluation metric for software homology detection. The experimental results show that the method is not only effective, but also the average time eﬃciency is 0.3 times that of the classical algorithm of graph edit distance.
Collision-free path planning based on new navigation function for an industrial robotic manipulator in human-robot coexistence environments

ABSTRACT This paper presents a method for real-time collision-free path planning that uses a new navigation function for environments in which humans and robots cooperate or coexist, which guarantees the safety of a human operator who works with a collaborative robot. This method follows 10 skeletal joints in a human operator, which are detected using an RGB-D sensor, in order to detect humans. A new navigation function ensures collision-free planning for a robotic manipulator that avoids local minima and generates a shorter path. Simulation results show the effectiveness of the proposed method in comparison to two existing methods. The applicability and practicability of the proposed method are demonstrated by experiments using a 6-DoF industrial manipulator HIWIN RA605 with an RGB-D vision sensor (Kinect V1), a real-time operation system extension (RTX64) and an EtherCAT network protocol.
Cloning, analysis and expression of a drought-related gene MeP5CS from melon.

Using designed primers based on the conserved amino acid sequences of known drought-related genes to amplify cDNA fragments from melon(Cucumis melo L.)by RT-PCR,a drought-related gene named MeP5CS was obtained.Bioinformatics analysis indicated that the full-length of cDNA sequence was 1 000 bp,which contained an open reading frame of 753 bp and encoded a protein of 250 amino acid residues with a calculated molecular weight of 82.18 kD and isoelectric point of 4.90. The MeP5CS protein showed 94%,81% and 73% similarity to the P5CS from Aegiceras corniculatum,Actinidia deliciosa and Vitis vinifera.The protein include α-helix(40.2%),β-turn(25.2%),random coil(34.6%)and a cleavage site between nineteen and twenty amino acid residues.The protein is a hydrophobic protein and there is two transmembrane helix and thirteen phosphorylation sites.The result ofRT-PCR analysis indicated that MeP5CS expression levels were different in roots,stems and leaves,and it was the highest in roots and middle in stems,lower in leaves.The results showed that the AMF can induce expression of MeP5CS gene in melon under water stress,and enhance the drought resistance of melon;Expression differences in tissues were related to different tissues and the duration of water stress.Under water deficit,AMF could increase Pro accumulation in melon leaves,gene expression and proline accumulation were positively correlated.
That Was Then, This Is Now: A Security Evaluation of Password Generation, Storage, and Autofill in Browser-Based Password Managers

Password managers have the potential to help users more effectively manage their passwords and address many of the concerns surrounding password-based authentication, however prior research has identified significant vulnerabilities in existing password managers. Since that time, five years has passed, leaving it unclear whether password managers remain vulnerable or whether they are now ready for broad adoption. To answer this question, we evaluate thirteen popular password managers and consider all three stages of the password manager lifecycle--password generation, storage, and autofill. Our evaluation is the first analysis of password generation in password managers, finding several non-random character distributions and identifying instances where generated passwords were vulnerable to online and offline guessing attacks. For password storage and autofill, we replicate past evaluations, demonstrating that while password managers have improved in the half-decade since those prior evaluations, there are still significant issues, particularly with browser-based password managers; these problems include unencrypted metadata, unsafe defaults, and vulnerabilities to clickjacking attacks. Based on our results, we identify password managers to avoid, provide recommendations on how to improve existing password managers, and identify areas of future research.
One-Code-Pass User Authentication Based on QR Code and Secret Sharing

The quick response (QR) code has gained extensive popularity in information storage and identiﬁcation in our daily lives due to its small printout size, high storage capacity and error correction ability. Taking into account these fascinating features of the QR code, we propose a user authentication protocol based on QR code and secret sharing. It is the ﬁrst user authentication protocol that implements the “one-code-pass” functionality in which an authorized user can use only one QR code to access various services from all departments within an organization. In the proposed protocol, a secret is divided into many shares in the form of QR codes held among diﬀerent de-partments and users. The original secret must be restored by the cooperation of both the department’s share and the user’s share for the authentication. Experimental results demonstrate that the proposed protocol has high robustness and is secure against various typical attacks.
Horizontal Privilege Escalation in Trusted Applications

Trusted Execution Environments (TEEs) use hardware-basedisolation toguardsensitivedatafrom conventionalmono-lithic OSes. While such isolation strengthens security guarantees, it also introduces a semantic gap between the TEE on the one side and the conventional OS and applications on the other. In this work, we studied the impact of this semantic gap on the handling of sensitive data by Trusted Applications (TAs) running in popular TEEs. We found that the combination of two properties, ( i ) multi-tenancy and ( ii ) statefulness in TAs leads to vulnerabilities of Horizontal Privilege Escalation (HPE). These vulnerabilities leaked sensitive session data or provided cryptographic oracles without requiring code execution vulnerabilities in TEE logic. We identiﬁed 19 HPE vulnerabilities present across 95 TAs running on three major ARM TrustZone-basedtrustedOSes. Ourresults showedthatHPE attacks can be used to decrypt DRM protected content, to forge attestations, andtoobtaincryptographickeysunderallthreeevaluatedOSes. Here, we present HOOPER an automatic symbolic execution based scanner for HPE vulnerabilities, in order to aid manual analysis and to dramatically reduce overall time. In particular, in the Teegris Trusted OS HOOPER is able to identify 19 out of 24 HPE-based attack ﬂows in 24-hours contrasted with our original manual analysis time of approximately four weeks.
GREYONE: Data Flow Sensitive Fuzzing

Data flow analysis (e.g., dynamic taint analysis) has proven to be useful for guiding fuzzers to explore hard-to-reach code and find vulnerabilities. However, traditional taint analysis is labor-intensive, inaccurate and slow, affecting the fuzzing efficiency. Apart from taint, few data flow features are utilized. In this paper, we proposed a data flow sensitive fuzzing solution GREYONE. We first utilize the classic feature taint to guide fuzzing. A lightweight and sound fuzzing-driven taint inference (FTI) is adopted to infer taint of variables, by monitoring their value changes while mutating input bytes during fuzzing. With the taint, we propose a novel input prioritization model to determine which branch to explore, which bytes to mutate and how to mutate. Further, we use another data flow feature constraint conformance, i.e., distance of tainted variables to values expected in untouched branches, to tune the evolution direction of fuzzing. We implemented a prototype of GREYONE and evaluated it on the LAVA data set and 19 real world programs. The results showed that it outperforms various state-of-the-art fuzzers in terms of both code coverage and vulnerability discovery. In the LAVA data set, GREYONE found all listed bugs and 336 more unlisted. In real world programs, GREYONE on average found 2.12X unique program paths and 3.09X unique bugs than state-of-the-art evolutionary fuzzers, including AFL, VUzzer, CollAFL, Angora and Honggfuzz, Moreover, GREYONE on average found 1.2X unique program paths and 1.52X unique bugs than a state-of-the-art symbolic exeuction assisted fuzzer QSYM. In total, it found 105 new security bugs, of which 41 are confirmed by CVE.
The relationship between energy consumption and carbon emissiont with economic growth in Liaoning Province

In order to provide the scientific basis for saving energy,reducing CO2 emissions and promoting the rapid development of economy of Liaoning Province,the Gross Domestic Product(GDP) and CO2 emissions were surveyed and the relationship between economic growth and energy utilization efficiency,energy consumption intensity was analyzed in this study.The results showed that total energy consumption in second industry was extremely high,which was 5.67—8.41 times higher than that of the third industry,and 7.2—9.0 times higher than that of the first industry.The energy consumption per unit capita of GDP was 52%—70% higher than the national average level,mainly due to concentrated high energy demanding industries,such as coat mining,dressing,electricity and supply,etc.Besides,the energy utilization efficiency of second industry was quite low,equivalent to 11.89% of the first the industry,12.60% of the third industry.Although annual GDP growth rate was faster than the growth rate of energy consumption,which might indicate the increased energy input promoted the improvement of gross domestic product,economic growth is not completely dependent on energy consumption growth.Instead,the adjustment of industrial structure and technical innovation played a critical role on the development of economy in Liaoning.CO2 emissions and annual change of per capita GDP presented turning point in 2001 and 2005,with the increase of per capita GDP,emissions of CO2 appeared to fluctuate repeatedly,suggesting the relationship between environment and the economy in Liaoning is still not in the balanced and collaborative developing stage.To solve these issues,structure adjustments,technology innovation and reform were needed to improve energy utilization efficiency and improve economy.
LoopyCuts

We present a new fully automatic block-decomposition algorithm for feature-preserving, strongly hex-dominant meshing, that yields results with a drastically larger percentage of hex elements than prior art. Our method is guided by a surface field that conforms to both surface curvature and feature lines, and exploits an ordered set of cutting loops that evenly cover the input surface, defining an arrangement of loops suitable for hex-element generation. We decompose the solid into coarse blocks by iteratively cutting it with surfaces bounded by these loops. The vast majority of the obtained blocks can be turned into hexahedral cells via simple midpoint subdivision. Our method produces pure hexahedral meshes in approximately 80% of the cases, and hex-dominant meshes with less than 2% non-hexahedral cells in the remaining cases. We demonstrate the robustness of our method on 70+ models, including CAD objects with features of various complexity, organic and synthetic shapes, and provide extensive comparisons to prior art, demonstrating its superiority.
Examining Understanding and Knowledge of Time Management Perception for the Architectural Education in the United States

This research has acquired preliminary information about the existing conditions and understanding of members regarding time management for members in the architectural field. The goal of this research is to construct a basis for the time management education framework in architectural field in the future. This research mainly focuses on following questions based on limited objectives: Do groups of academics and professionals have understanding and knowledge of time management? Can the level of an individual’s scheduling techniques be correlated to the patterns of knowledge and understanding of time management principles and practices? Can the time management practice status in individual’s working environment be correlated to the patterns of knowledge and understanding of time management principles and practices? Can an individual’s self-confidence level be correlated to the patterns of knowledge and understanding of time management principles and practices? Data have been collected through comprehensive questionnaires given to academics and professionals in United States. By means of statistical analysis, the hidden patterns, deficiencies and relationships in attitudes about time management have been revealed. The statistical analysis has produced conclusions that, among several subdivisions, self-discipline and planning have strong relationships and confidence, personal organization, control, and information gathering subdivisions have certain relationships with objectives of time management education in this research.
No Amplification of Hindsight Bias Due to Time Delay

Hindsight bias is a phenomenon in which individuals tend to overestimate the degree of accuracy to which they can recall an event after the fact. This study examined the effect of time delay on hindsight bias. Participants (n = 66) gave numerical responses to test questions (Phase 1) and were later provided with the solutions to these questions (Phase 2), at which time they were asked to recall their original answers. Group 1 received a short time delay of 1 day between Phases 1 and 2, while Group 2 experienced a medium delay (1 week) and Group 3 a long delay (3 weeks). Although non-significant results were obtained, a trend was found toward increased hindsight bias with longer time delay.
Rapid Determination on Fatty Acids Content by Gas Chromatography in Soybean

This paper discussed the rapid determination on fatty acids content by gas chromatography in soybean,which justsaponificate and Methyl esterificate the lipids from grinding soybean seeds directly without having to extract lipids from soybean,then get the gas chromatography determination by chromatography column HP519091J-413.The time of peak value of palmitic acid,stearic acid,oleic acid,linoleic acid and linolenic acid was6.57,7.83,8.73,8.86,9.06 min,respectively.The method proved to be time and material saving,and easy to operate.The method also helpful for soybean acids determination in early generation for it only needs few samples.
TempNodeEmb: Temporal Node Embedding considering temporal edge influence matrix

Understanding the evolutionary patterns of real-world evolving complex systems such as human interactions, transport networks, biological interactions, and computer networks has important implications in our daily lives. Predicting future links among the nodes in such networks reveals an important aspect of the evolution of temporal networks. To analyse networks, they are mapped to adjacency matrices, however, a single adjacency matrix cannot represent complex relationships (e.g. temporal pattern), and therefore, some approaches consider a simplified representation of temporal networks but in high-dimensional and generally sparse matrices. As a result, adjacency matrices cannot be directly used by machine learning models for making network or node level predictions. To overcome this problem, automated frameworks are proposed for learning low-dimensional vectors for nodes or edges, as state-of-the-art techniques in predicting temporal patterns in networks such as link prediction. However, these models fail to consider temporal dimensions of the networks. This gap motivated us to propose in this research a new node embedding technique which exploits the evolving nature of the networks considering a simple three-layer graph neural network at each time step, and extracting node orientation by Given's angle method. To prove our proposed algorithm's efficiency, we evaluated the efficiency of our proposed algorithm against six state-of-the-art benchmark network embedding models, on four real temporal networks data, and the results show our model outperforms other methods in predicting future links in temporal networks.
NMT-based Cross-lingual Document Embeddings

This paper investigates a cross-lingual document embedding method that improves the current Neural machine Translation framework based Document Vector (NTDV or simply NV). NV is developed with a self-attention mechanism under the neural machine translation (NMT) framework. In NV, each pair of parallel documents in different languages are projected to the same shared layer in the model. However, the pair of NV embeddings are not guaranteed to be similar. This paper further adds a distance constraint to the training objective function of NV so that the two embeddings of a parallel document are required to be as close as possible. The new method will be called constrained NV (cNV). In a cross-lingual document classification task, the new cNV performs as well as NV and outperforms other published studies that require forward-pass decoding. Compared with the previous NV, cNV does not need a translator during testing, and so the method is lighter and more flexible.
Understanding Brain Dynamics for Color Perception using Wearable EEG headband

The perception of color is an important cognitive feature of the human brain. The variety of colors that impinge upon the human eye can trigger changes in brain activity which can be captured using electroencephalography (EEG). In this work, we have designed a multiclass classification model to detect the primary colors from the features of raw EEG signals. In contrast to previous research, our method employs spectral power features, statistical features as well as correlation features from the signal band power obtained from continuous Morlet wavelet transform instead of raw EEG, for the classification task. We have applied dimensionality reduction techniques such as Forward Feature Selection and Stacked Autoencoders to reduce the dimension of data eventually increasing the model's efficiency. Our proposed methodology using Forward Selection and Random Forest Classifier gave the best overall accuracy of 80.6\% for intra-subject classification. Our approach shows promise in developing techniques for cognitive tasks using color cues such as controlling Internet of Thing (IoT) devices by looking at primary colors for individuals with restricted motor abilities.
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

Influence maximization (IM) is one of the most important problems in social network analysis. Its objective is to find a given number of seed nodes that maximize the spread of information through a social network. Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeat Monte Carlo (MC) simulations over and over to reliably estimate the influence (i.e., the number of infected nodes) of a seed set. In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training. To the best of our knowledge, MONSTOR is the first inductive method for this purpose. MONSTOR can greatly accelerate existing IM algorithms by replacing repeated MC simulations. In our experiments, MONSTOR provided highly accurate estimates, achieving 0.998 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Moreover, IM algorithms equipped with MONSTOR are more accurate than state-of-the-art competitors in 63% of IM use cases.
Exploring the musical mind : cognition, emotion, ability, function

PART A: COGNITIVE PROCESSES 1. The psychology of music reading 2. Experimental studies of music reading: a review 3. The uses of space in music notation 4. Immediate recall of melodies 5. Cognition and real music: the psychology of music comes of age 6. Psychological structures in music: core research 1980-1990 7. Book review of Language, Music and Mind 8. Does music mean anything? PART B: EMOTION AND MOTIVATION 9. Music as a language 10. Music psychology and the composer 11. Empirical studies of emotional response to music 12. Emotional responses to music: a review 13. Musical performance and emotion: issues and developments PART C: TALENT AND SKILL DEVELOPMENT 14. Musical expertise 15. Musical ability 16. The acquisition of music performance expertise 17. Are some children more gifted for music than others? PART D: MUSIC IN THE REAL WORLD 18. Everyday uses of music 19. Music: where cognition and emotion meet 20. Music and worship: a psychologist's perspective 21. Emotion, functionality, and the everyday experience of music 22. The sound of music versus the essence of music 23. Assessing music psychology research