Miracle-dz/UniFAR-FTUs huggingface dataset:
Deductive Association Networks

we introduce deductive association networks(DANs), a network that performs deductive reasoning. To have high-dimensional thinking, combining various axioms and putting the results back into another axiom is necessary to produce new relationships and results. For example, it would be given two propositions:"Socrates is a man. "and"All men are mortals. "and two propositions could be used to infer the new proposition,"Therefore Socrates is mortal. ". To evaluate, we used MNIST Dataset, a handwritten numerical image dataset, to apply it to the group theory and show the results of performing deductive learning.
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs

The collection and availability of big data, combined with advances in pre-trained models (e.g., BERT, XLNET, etc), have revolutionized the predictive performance of modern natural language processing tasks, ranging from text classification to text generation. This allows corporations to provide machine learning as a service (MLaaS) by encapsulating fine-tuned BERT-based models as APIs. However, BERT-based APIs have exhibited a series of security and privacy vulnerabilities. For example, prior work has exploited the security issues of the BERT-based APIs through the adversarial examples crafted by the extracted model. However, the privacy leakage problems of the BERT-based APIs through the extracted model have not been well studied. On the other hand, due to the high capacity of BERT-based APIs, the fine-tuned model is easy to be overlearned, but what kind of information can be leaked from the extracted model remains unknown. In this work, we bridge this gap by first presenting an effective model extraction attack, where the adversary can practically steal a BERT-based API (the target/victim model) by only querying a limited number of queries. We further develop an effective attribute inference attack which can infer the sensitive attribute of the training data used by the BERT-based APIs. Our extensive experiments on benchmark datasets under various realistic settings validate the potential vulnerabilities of BERT-based APIs. Moreover, we demonstrate that two promising defense methods become ineffective against our attacks, which calls for more effective defense methods.
The Statistical Complexity of Interactive Decision Making

A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning algorithms that achieve near-optimal regret. This question is analogous to the classical problem of optimal (supervised) statistical learning, where there are well-known complexity measures (e.g., VC dimension and Rademacher complexity) that govern the statistical complexity of learning. However, characterizing the statistical complexity of interactive learning is substantially more challenging due to the adaptive nature of the problem. The main result of this work provides a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. In particular, we provide: 1. a lower bound on the optimal regret for any interactive decision making problem, establishing the Decision-Estimation Coefficient as a fundamental limit. 2. a unified algorithm design principle, Estimation-to-Decisions (E2D), which transforms any algorithm for supervised estimation into an online algorithm for decision making. E2D attains a regret bound that matches our lower bound up to dependence on a notion of estimation performance, thereby achieving optimal sample-efficient learning as characterized by the Decision-Estimation Coefficient. Taken together, these results constitute a theory of learnability for interactive decision making. When applied to reinforcement learning settings, the Decision-Estimation Coefficient recovers essentially all existing hardness results and lower bounds. More broadly, the approach can be viewed as a decision-theoretic analogue of the classical Le Cam theory of statistical estimation; it also unifies a number of existing approaches -- both Bayesian and frequentist.
Reducible normal generators for mapping class groups are abundant

In this article, we study the normal generation of the mapping class group. We first show that a mapping class is a normal generator if its restriction on the invariant subsurface normally generates the (pure) mapping class group of the subsurface. As an application, we provided a criterion for reducible mapping classes to normally generate the mapping class groups in terms of its asymptotic translation lengths on Teichm\"uller spaces. This is an analogue to the work of Lanier-Margalit dealing with pseudo-Anosov normal generators.
Electrochemical impedance monitoring of immunochemical reactions using varying IDE geometries for signal enhancement

Electrochemical Impedance Spectroscopy (EIS) has been applied to the detection of analytes for immunosensors [1-3]. The development of hand held devices based on this technique is a very promising prospect for point-of-care applications and is an attractive alternative to laboratory-based immunochemical analysis [1, 4]. The work in this paper will focus primarily on the development of an EIS method of transduction for immunoassay detection that could be potentially introduced into a hand held point-of-care device. Varying geometries of IDEs will be reported and discussed to improve the detection of antigen.
Analysis and design of lumped- and lumped-distributed-element directional couplers for MIC and MMIC applications

An analysis of lumped-element and lumped-distributed-element directional couplers is described. The assumed generality of the ring-type four-port enables different types of directionality to be developed. The equations that make possible an idealized design of co-, contra- and trans-directional couplers for arbitrary power division and transformation ratio are derived. A design procedure for three-branch, lumped-element directional couplers that allows an impedance transformation between the input and output ports is also included. Computer simulations of the performance of various couplers with lumped elements represented by realistic models are included. >
Automated Code Tracing Exercises for CS1

The ability for students to read and comprehend code is a fundamental skill in computer programming. Relying on students to build this skill through typical programming assignments can lead to many persevering through trial and error rather than understanding. This paper describes Trace Generator, a work-in-progress application for generating automatically graded code tracing questions for Python and C programs. The fundamental principles behind this work are mastery through repetition and providing comprehensive and understandable feedback to enable students to learn from their mistakes. Feedback and reflections from the use of the generated questions with two introductory procedural programming classes (200 students) are also discussed. Analysis of student attempts suggests a willingness to complete quizzes multiple times until they achieved a satisfactory score (average final result of 91%).
TBIT: Transformer-based Business Prediction Model Integrating Work Order Textual Contextual Information

Accurate business prediction provides a good understanding of individual business trends and has a significant impact on reducing overall operating costs and ensuring a high level of customer service quality. Currently, artificial intelligence and big data technologies have been widely used in various businesses of electric power customer service, but how to effectively use the heterogeneous information in customer service data for business prediction has become a major challenge. To solve this problem, this paper proposes a Transformer-based Business Prediction Model Integrating Work Order Textual Contextual Information (TBIT) to perform customer service business prediction. The model first uses one-dimensional convolution and self-attention mechanism to mine the semantic contexts contained in the work order text information. Then, the Transformer is used to fuse the customer's dynamic preference information with the work order text context, and to mine the global time sequence dependence of work order data, so as to realize customer service business prediction. The final experiments show that the TBIT model can perform representation learning on work order data more effectively compared to the baseline models, and also show superior performance in the customer service business prediction task.
Octreotide-conjugated silver nanoparticles for active targeting of somatostatin receptors and their application in a nebulized rat model

Abstract Drug uptake and distribution through cell–receptor interactions are of prime interest in reducing the adverse effects and increasing the therapeutic effectiveness of delivered formulations. This study aimed to formulate silver nanoparticles (AgNPs) conjugated to somatostatin analogs for specific delivery through somatostatin receptors (SSTRs) expressed on cells and by nebulizing the prepared AgNPs formulations into lung cells for in vivo application. AgNPs were prepared using the citrate reduction method, yielding AgNPs–CTT, which was further chemically conjugated to octreotide (OCT) to form AgNPs–OCT through an amide linkage. The AgNPs–OCT formulation was coated using alginate to yield a carrier, AgNPs–OCT–Alg, feasible for drug delivery through nebulization. AgNPs were uniform in size with an acceptable range of zeta potential. Furthermore, the concentrations of AgNP formulations were found safe for the model cell lines used, and cell proliferation was significantly reduced in a dose-dependent manner (p < 0.05). In the healthy lung tissues, AgNPs–OCT–Alg accumulated at a concentration of 0.416 ± 5.7 mg/kgtissue, as determined via inductively coupled plasma optical emission spectrometry. This study established the accumulation of AgNPs, specifically the AgNPs–OCT–Alg, in lung tissues, and substantiated the active, specific, and selective targeting of SSTRs at pulmonary sites. The anticancer efficacy of the formulations was in vitro tested and confirmed in the MCF-7 cell lines. Owing to the delivery suitability and cytotoxic effects of the AgNPs–OCT–Alg formulation, it is a potential drug delivery formulation for lung cancer therapy in the future.
Fuwen Wei—Recipient of the 2021 Molecular Ecology Prize

Dr. Fuwen Wei obtained his Bachelor Degree in Biology in 1984 and his Masters Degree in Zoology in 1987 from China West Normal University. He carried out his doctoral studies from 1994 to 1997 and obtained his Ph.D. degree in 1997 from the Institute of Zoology, Chinese Academy of Sciences (CAS). Since then, he has been working as a Principal Investigator in the Institute of Zoology, CAS and served as the Deputy Director of the Institute from 2008– 2017. Dr. Wei received the National Science Fund for Distinguished Young Scholars from the National Natural Science Foundation of China in 2001. He was elected as an Academician of Chinese Academy of Sciences in 2017, an Academician of The World Academy of Sciences in 2018, and an Academician of Academia Europaea in 2021. He won the CAS Outstanding Science and Technology Achievement Award in 2018 and the China Natural Science Award in 2019. Dr. Wei's research has focused on conservation biology and molecular ecology of endangered animals, and he is a pioneer in the conservation genomics and metagenomics of endangered animals. He has applied genetic and genomic techniques to assess the evolutionary past, present and future of giant panda populations; infer the processes and causes that threaten them; reveal their adaptive mechanisms to specialized bamboo diet; and propose sciencebased conservation strategies for their longterm survival. His research offers a framework for endangered animal study. Below we summarize his main research advances. 2 | NONINVA SIVE GENETIC S AND ITS APPLIC ATION IN STUDIES OF GIANT PANDA S
A Fiber Optic Faint Pressure Sensor Used in High‐Temperature Environment

A fiber optic faint pressure sensor used in high‐temperature environment is presented and experimentally demonstrated. The bulky and ruggedized pressure sensor is an extrinsic Fabry–Perot interferometer manufactured with stainless steel. A pressure sensitivity of 7.1 pm/Pa and a pressure resolution of 1.39 Pa within the measurement range from 0 to 1500 Pa have been achieved. The temperature dependence of the sensor is also investigated. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1891–1893, 2013
Structural basis for antibody resistance to SARS-CoV-2 omicron variant

The recently reported B.1. 1.529 Omicron variant of SARS-CoV-2 includes 34 mutations in the spike protein relative to the Wuhan strain that initiated the COVID-19 pandemic, including 15 mutations in the receptor binding domain (RBD). Functional studies have shown omicron to substantially escape the activity of many SARS-CoV-2-neutralizing antibodies. Here we report a 3.1 Å resolution cryo-electron microscopy (cryo-EM) structure of the Omicron spike protein ectodomain. The structure depicts a spike that is exclusively in the 1-RBD-up conformation with increased mobility and inter-protomer asymmetry. Many mutations cause steric clashes and/or altered interactions at antibody binding surfaces, whereas others mediate changes of the spike structure in local regions to interfere with antibody recognition. Overall, the structure of the omicron spike reveals how mutations alter its conformation and explains its extraordinary ability to evade neutralizing antibodies. Highlights SARS-CoV-2 omicron spike exclusively adopts 1-RBD-up conformation Omicron substitutions alter conformation and mobility of RBD A subset of omicron mutations change the local conformation of spike The structure reveals the basis of antibody neutralization escape
Electroless Ni-Cu-P barrier between Si/Ti/Al pad and Sn-Pb flip-chip solder bumps

This work investigates the barrier effect of the electroless Ni-Cu-P deposit between Al conductor and Sn-Pb solder bump, as well as the interfacial reaction with the Sn-Pb solder. For the Ni-Cu-P/63Sn-37Pb system, a (Ni, Cu)/sub 3/Sn/sub 4/ compound with three different morphologies: fine-grain, whisker, and polygon are formed at the Ni-Cu-P/63Sn-37Pb interface after reflow at 220/spl deg/C for 15 s. These (Ni, Cu)/sub 3/Sn/sub 4/ crystals transform into polygon shape with smooth appearance during 150/spl deg/C aging. For the Ni-Cu-P/95Pb-5Sn system, equiaxial (Ni, Cu)/sub 3/Sn/sub 4/ crystals are formed at the Ni-Cu-P/95Pb-5Sn interface after reflow at 350/spl deg/C for 15 s, and they also transform into polygon shape during 150/spl deg/C aging. In addition, the Ni-Cu-P deposit will crystallize to form Ni/sub 5/ P/sub 2/ during 350/spl deg/C reflow. The growth of the (Ni, Cu)/sub 3/ Sn/sub 4/ compound by solid state reaction is a diffusion controlled process for both Ni-Cu-P/63Sn-37Pb and Ni-Cu-P/95Pb-5Sn systems. A 4 /spl mu/m Ni-Cu-P deposit can provide adequate barrier function between an Al conductor and two Sn-Pb solders under 150/spl deg/C aging for 1000 h.
Corpus based sentimenal movie review analysis using auto encoder convolutional neural network

Abstract In natural language processing, most prominent branch is sentiment analysis. People’s emotions and attitudes are analyzed using this sentiment analysis towards service, some product, etc. In prediction of the future scope of a product, some benefits are given by sentiment analysis. However, manual analysis of such a huge amount of documents is a highly tedious task, especially with limited time. Hence, for solving this problem, various attempts are made in literature and proposed different sentiment analysis methods. However, in generation of lexicon, popular NLP tools has some drawbacks. The accuracy of lexicons based on humans is less and they are limited too. On the other hand, lexicons based on dictionary are highly general and they are domain specific. So, a technique called Corpus Integrated Autoencoder Convolutional Neural Network based Sentiment Analysis (CI-AECNN) is proposed in this work for solving this issue. The sentiment lexicon generation based on corpus is performed in this work. Candidates sentiment orientation are computed using this and seed lexicon are added with recognized sentiment words and from seed lexicon, words with incorrect sentiment are removed. The long short-term memory (LSTM) is used for performing Word Sense Disambiguation. Conditional random fields are used for extracting features. At last, auto-encoder, convolutional neural network is used for performing classification. In MATLAB simulation environment, conducted this research work’s overall analysis and it indicates that better results are produced by proposed technique when compared with available techniques.
Using hybrid GA-PSO algorithm to solve problem in machine scheduling

Abstract In this paper, we describe new hybrid genetic algorithm and particle swarm optimization to solve the sum of earliness and the number of tardy job on two-machines flow shop schedule problem is NP- hard. The study discusses a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) to tackle the presented mission. Extensive experiments, based on computers, suggest that the proposed mathematical models are efficient in solving flow shop problems with GA solved to n = 3000 while PSO solved to n = 500 job.
Globally Aging Cortical Spontaneous Activity Revealed by Multiple Metrics and Frequency Bands Using Resting-State Functional MRI

Most existing aging studies using functional MRI (fMRI) are based on cross-sectional data but misinterpreted their findings (i.e., age-related differences) as longitudinal outcomes (i.e., aging-related changes). To delineate aging-related changes the of human cerebral cortex, we employed the resting-state fMRI (rsfMRI) data from 24 healthy elders in the PREVENT-AD cohort, obtaining five longitudinal scans per subject. Cortical spontaneous activity is measured globally with three rsfMRI metrics including its amplitude, homogeneity, and homotopy at three different frequency bands (slow-5: 0.02–0.03 Hz, slow-4: 0.03–0.08 Hz, and slow-3 band: 0.08–0.22 Hz). General additive mixed models revealed a universal pattern of the aging-related changes for the global cortical spontaneous activity, indicating increases of these rsfMRI metrics during aging. This aging pattern follows specific frequency and spatial profiles where higher slow bands show more non-linear curves and the amplitude exhibits more extensive and significant aging-related changes than the connectivity. These findings provide strong evidence that cortical spontaneous activity is aging globally, inspiring its clinical utility as neuroimaging markers for neruodegeneration disorders.
Emotional Peer Support Interventions for Students With SEND: A Systematic Review

Emotional peer support systems have benefits for student-student relationships and allow for children and adolescents' participation in schools. For students with specific educational needs and disabilities (SEND), positive relationships seem to be more difficult to attain and these students are more vulnerable to suffer negative peer experiences such as bullying and social exclusion. Systems in which peers can show helpful behavior are beneficial for schools in order to create a positive, supportive climate. Emotional peer support entails social interaction through emotional or practical help based on what these peers have in common and many times with benefits for both. This systematic review identified interventions of emotional peer support in schools for students with SEND. Twenty-three studies were identified that involved four types of befriending: circle of friends, peer buddying, peer networks, and social lunch clubs. Studies reported mainly positive outcomes for both focus students and peer supporters in terms of increased social interaction and social acceptance, as well as enhanced self-esteem and empathy on the individual level. Further bonding of the students by friendship was also perceived, but more precise data is required to draw further conclusions. Support by the school as an institution, the specific role of the teacher, and family participation are important factors related to the impact of peer support systems. Information on these aspects was scarce, and it is recommended to include variables of this nature in future research. Intervention descriptions revealed students' active participation through suggestions for activities, however their involvement in organizing the systems was limited. More research is needed to learn about the opportunities of emotional peer support to improve student-student relationships including the active involvement of the peers themselves in this support.
Green Tea Attenuates methomyl-Induced Cardiac Toxicity Through Modulating Oxidative Stress and Nuclear Factor kappa B Up- Regulation in Mice

ABSTRACT Methomyl (ME) is a carbamate insecticide that is causes a change in biochemical parameters and affects the oxidative state in the body through the production of free radicals. Herbal medicines derived from plant extracts are useful in treating many diseases, as they are considered natural antioxidants that have a protective role against toxins. This study aimed to evaluate the ability of green tea extract (GTE) to treat ME-induced cardiac toxicity in experimental animals. The experimental animals were divided into five groups. Blood and tissue were collected to estimate the biochemical parameters. ME caused significantly elevated levels of AST, LDH, NF-κB, and malondialdehyde. While a significant decrease was observed in levels of superoxide dismutase, a decrease in glutathione and glutathione-S-transferase was observed. Alterations in these biochemical markers were referred to oxidative stress-induced cardiac damage. The administration of GTE at a concentration (1.5%) to the ME treated group caused the tested biochemical parameters to appear close to those of the control group. GTE alleviates ME-induced oxidative stress in female albino mice. A higher concentration of GTE {1.5%} appears to have a better therapeutic effect than a lower concentration of extract {0.75%} in cardiac toxicity ME.
Tax Morale: Direct and Indirect Paths between Trust Factors: Empirical Evidence from Greece

Abstract A plethora of evidence can be found in the literature supporting the view that citizens’ trust in the state plays a decisive role in their overall tax compliance. Nevertheless, in Greece, where the consequences of a ten years long economic crisis still exist, the phenomenon of tax evasion is still dealt with traditional enforcement methods like penalties, regular audits, and tax policy. A new conceptual model is proposed and empirically tested using data from 1,014 Greek citizens from fifty areas of Greece. It attempts to examine the combined effect of ten trust-related factors, which are grouped into four dimensions on tax morale (trust in governance quality, trust in principles and institutions, trust in fellow citizens and trust in personal perceptions). The results show that Greek citizens’ trust in tax reciprocity is the factor that mainly affects tax morale, with citizens’ trust in tax authorities and in the institution of democracy closely following.