Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a 5mW capacitive accelerometer based on MEMS and CMOS technology

This work presents an open-loop, fully differential capacitive MEMS accelerometer implemented in CMOS technology. The mathematical model of this open-loop system is derived, which considers non-ideal factors in circuits such as nonlinear distortion and noises. These non-ideal factors have been discussed through system level simulation using MATLAB. Simulation results show that the system has good capacitive sensitivity and is robust to noises. The detailed design of interface circuit in the proposed MEMS accelerometer is presented, using sigma-delta (Σ-Δ) modulation. Finally, the chip-level physical layout of interface circuit is implemented and tested, using silicon-on-insulator (SOI) substrate with 1μm CMOS process. Results have shown the chip with area of 1.32mm2 and power consumption of about 5mW. The proposed MEMS accelerometer is designed for acceleration of less than 5G, and its resolution is 1.923mG.
Mitigating Negative Behaviors in Student Project Teams: An Information Technology Solution

The evolution of the information systems (IS) profession as well as the IS educational environment has resulted in student project teams becoming an increasingly important component of IS education. Negative behaviors within these project teams result in less than optimal project outcomes and confound attempts to fairly evaluate individual efforts within the team. These negative behaviors can also result in unfavorable perceptions of the educational experience for many students. An information technology-based application consisting of an online interactive activity log and an online peer review system was developed to mitigate the common negative behaviors of social loafing and free riding and provide more accurate and useful information for evaluation. Its successful implementation in a specific classroom environment is discussed and evidence of its efficacy is supplied in the form of subjective assessment by the instructors and the results of a survey measuring student perceptions of
Collaborative Users' Brand Preference Mining across Multiple Domains from Implicit Feedbacks

Advanced e-applications require comprehensive knowledge about their users’ preferences in order to provide accurate personalized services. In this paper, we propose to learn users’ preferences to product brands from their implicit feedbacks such as their searching and browsing behaviors in user Web browsing log data. The user brand preference learning problem is challenge since (1) the users’ implicit feedbacks are extremely sparse in various product domains; and (2) we can only observe positive feedbacks from users’ behaviors. In this paper, we propose a latent factor model to collaboratively mine users’ brand preferences across multiple domains simultaneously. By collective learning, the learning processes in all the domains are mutually enhanced and hence the problem of data scarcity in each single domain can be effectively addressed. On the other hand, we learn our model with an adaption of the Bayesian personalized ranking (BPR) optimization criterion which is a general learning framework for collaborative filtering from implicit feedbacks. Experiments with both synthetic and real world datasets show that our proposed model significantly outperforms the baselines.
A Toolkit to Assist Massachusetts School Nurses Start Elementary School Substance Use Prevention Education Programs

Purpose: Massachusetts currently only mandates fifth-twelfth grade substance use prevention. Literature states it is counter productive to wait until the adolescent years to initiate substance use prevention due to the adolescent developmental stage of social conformity. Starting evidence-based substance use prevention education in early elementary school will arm children with the knowledge and confidence they need to make healthy choices and reduce future high school drug rates. Methods: A toolkit was created to assist Massachusetts elementary school nurses to start a substance use prevention program in their schools/districts. The toolkit was presented and evaluated in January 2018 by the Massachusetts school nurse leaders that attended the five regional school nurse leader meetings. A preand post-presentation survey was distributed to 192 school nurse leaders to ascertain if the toolkit was helpful. Results: Out of the 351 school nurse leaders in the Commonwealth, 192 attended the five meetings, 160 completed surveys, and 39 wrote comments. With survey results and written feedback from 11% of Massachusetts school nurse leaders (39 out of 351), the toolkit was edited and improved. The final toolkit was posted February 2018 to the Boston University School Health Institute for Education and Leadership Development website with the endorsement of the Massachusetts Department of Public Health. Conclusion: Survey analysis revealed that the toolkit presentation increased school nurse leaders’ likelihood of starting an early elementary school substance use prevention program (average mean increase of .825 on a 1-4 Likert Scale).
On the Surjectivity of the Galois Representations Associated to Non-CM Elliptic Curves

Abstract Let $E$ be an elliptic curve defined over $\mathbb{Q}$ , of conductor $N$ and without complex multiplication. For any positive integer $l$ , let ${{\phi }_{1}}$ be the Galois representation associated to the $l$ -division points of $E$ . From a celebrated 1972 result of Serre we know that ${{\phi }_{1}}$ is surjective for any sufficiently large prime $l$ . In this paper we find conditional and unconditional upper bounds in terms of $N$ for the primes $l$ for which ${{\phi }_{1}}$ is not surjective.
Efficient Querying of Inconsistent Databases with Binary Integer Programming

An inconsistent database is a database that violates one or more integrity constraints. A typical approach for answering a query over an inconsistent database is to first clean the inconsistent database by transforming it to a consistent one and then apply the query to the consistent database. An alternative and more principled approach, known as consistent query answering, derives the answers to a query over an inconsistent database without changing the database, but by taking into account all possible repairs of the database. In this paper, we study the problem of consistent query answering over inconsistent databases for the class for conjunctive queries under primary key constraints. We develop a system, called EQUIP, that represents a fundamental departure from existing approaches for computing the consistent answers to queries in this class. At the heart of EQUIP is a technique, based on Binary Integer Programming (BIP), that repeatedly searches for repairs to eliminate candidate consistent answers until no further such candidates can be eliminated. We establish rigorously the correctness of the algorithms behind EQUIP and carry out an extensive experimental investigation that validates the effectiveness of our approach. Specifically, EQUIP exhibits good and stable performance on conjunctive queries under primary key constraints, it significantly outperforms existing systems for computing the consistent answers of such queries in the case in which the consistent answers are not first-order rewritable, and it scales well.
Optimizing Description Logic Reasoning with Nominals : First Results

OWL-DL is a World Wide Web Consortium standard for representing ontologies on the Semantic Web. It can be seen as a syntactic variant of the Description Logic SHOIN (D), with an OWL-DL ontology corresponding to a SHOIN (D) knowledge base. The very recent accomplishment of a decision procedure for SHOIN (D) poses the challenge of turning the decision procedure into a practical implementation. In particular, we emphasize the need of new optimization techniques for nominals, especially in the presence of large number of individuals in the KB. In this paper, we present new techniques for optimizing DL reasoning in the presence of nominals in the TBox and individuals in a large ABox. We have integrated our optimizations in the open-source Pellet reasoner, which is sound and complete for SHOIN (D), and found that they suffice for efficiently classifying the famous Wine Ontology. We also show that these optimization techniques produce significant performance improvements in other widely used ontologies containing nominals, such as the OWL-S and AKT ontologies.
Body Mass Index and Cancer Risk: The Evidence for Causal Association

Increased body mass index (BMI), as an approximation of body adiposity, is a risk factor for developing several adult malignancies. To quantify these risks, we reported a comprehensive systematic review (Lancet 2008; 371: 569-78) of prospective observational studies determining associations between BMI and risk of incident cancer for 20 cancer types. We demonstrated that associations are: (i) sex-specific; (ii) exist for a wider range of malignancies than previously thought; and (iii) are broadly consistent across geographic populations. In the present paper, we tested these data against the Bradford-Hill criteria of causal association, and argue that the available data support strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence and probably analogy. However, the experimental evidence supporting reversibility is currently lacking, though indirect evidence from longitudinal data in cohort studies and long-term follow-up post-bariatric surgery is emerging. We additionally assessed these data against appropriate adjustment for available confounding factors; measurement error and study design; and residual confounding; and found lack of alternative explanations. We conclude that there is considerable evidence to support a causal association between BMI and risk for many cancer types, but in order to establish the role of weight control in cancer prevention, there is a need to develop trial frameworks in which to better test reversibility.
Cell-specific expression of the analgesic-antitumor peptide coding sequence under the control of the human α-fetoprotein gene promoter and enhancer

The aim of the present study was to construct a gene-modified hepatocellular carcinoma (HCC)-specific analgesic-antitumor peptide (AGAP) expression vector regulated by the α-fetoprotein (AFP) promoter and enhancer, in order to evaluate its effect. The AFP promoter is generally used in HCC-specific gene therapy strategies. However, this approach is limited by the weak activity of the AFP promoter. Linking the AFP enhancer and promoter has been shown to generate a stronger and more HCC-selective promoter. The AGAP DNA fragment was amplified from the total RNA of the Chinese scorpion, Buthus martensii Karsch. The fragment was subsequently cloned into the pAFP plasmid with the minimal essential DNA fragment, which included the AFP gene promoter and enhancer, to construct the recombinant plasmid, pAFP-AGAP. The plasmid was transfected into HepG2 cells and the mRNA expression levels of AGAP were detected by reverse transcription polymerase chain reaction (RT-PCR). In addition, Cell Counting Kit 8 (CCK-8) was used to analyze the cytotoxicity of plasmid transfection. The length, position and orientation of the inserted AGAP gene were all confirmed to be correct; thus, the recombinant vector was successfully constructed. Using RT-PCR and CCK-8 analysis, the mRNA expression levels of AGAP and the cytotoxicity in AFP-producing human HCC cells were determined. The AFP promoter and enhancer were found to specifically accelerate the expression of the target genes within the cells that were positive for AFP. Therefore, the method used in the present study was demonstrated to be a novel integration of traditional Chinese medicine with western medicine.
Reducing shift penalty in Domain Wall Memory through register locality

General-purpose graphics processing units (GPGPUs) have the ability to execute hundreds to thousands of threads simultaneously. Extreme multithreading requires a large register file to hold state of executing threads and facilitate context switching. As feature size reduces, power consumption in the large register file becomes a major concern. In this work, we exploit Domain Wall Memory (DWM) which is a spin-based memory to reduce power consumption in register file. DWM is a promising technology and offers non-volatility, high energy efficiency, and high density by storing several bits into the domains of a ferromagnetic wire. However, despite of favourable properties of DMW over SRAM technology, DWM poses a unique challenge that the bits must be accessed serially through shift operations, leading to variable and potentially higher access latencies. To address this challenge, we propose a new predictive shift policy. In this policy, we exploit register locality across threads and predict source and destination operands of instructions. We record history of registers accessed by instructions and shift magnetic domains of DWM tracks for subsequent instructions, speculatively. Over a wide range of applications from NVIDIA CUDA SDK, ISPASS, and Rodinia, our predictive scheme achieves dramatic energy saving over an SRAM register file while changing performance negligibly.
AO Challenge , Part II : Lessons Learnt from Implementing a Reusable Aspect Framework

Writing a set of reusable aspects that can be used in isolation and in combination is a highly complex task. In this paper we identify a set of key features required for implementing reusable aspects frameworks with complex aspect dependencies and interactions based on our experience implementing AspectOptima, a language independent, aspectoriented framework that provides runtime support for transactions. By showing details of our implementation in AspectJ, we demonstrate how reusability and con gurability, although not directly supported by AspectJ, can be achieved by means of aspect design patterns. Where appropriate, we suggest potential language improvements that address the encountered limitations. Finally, we present performance measures that compare our aspect-oriented solution to a purely object-oriented one, and highlight the impact of language support for reusability and con gurability on performance.
Do Markets Prefer Open or Proprietary Standards for XML Standardization? An Event Study

Proprietary standardization seeks to increase a firm's market share (pie sharing). Open standardization seeks to increase the size of the market (pie expansion). In order to determine which of these creates more value for a firm involved in standardization, this paper investigates the market value of standardization initiatives, using an event study based on 148 XML schema standardization initiatives collected from the Lexis-Nexis database between January 1999 and December 2003. The results show that financial markets respond positively to announcements of proprietary XML schema standardization, but not to those of open XML schema standardization. Moreover, investors do not develop a preference for open standards over time. These results have implications for the management of standardization strategies and identify future research opportunities.
Learning minimal latent directed information trees

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD - We propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, we apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. To the best of our knowledge, this is the first approach that recovers these type of latent graphical models where samples are available only from a subset of processes.
Noncoherent sequence detection

New noncoherent sequence detection algorithms for combined demodulation and decoding of coded linear modulations transmitted over additive white Gaussian noise channels, possibly affected by intersymbol interference, are presented. Optimal sequence detection in the presence of a random rotation of the signal phase, assumed to be constant during the entire transmission, requires a receiver complexity exponentially increasing with the duration of the transmission. Based on proper approximations, simple suboptimal detection schemes based on the Viterbi algorithm are presented, whose performance approaches that of coherent detection. In a companion paper by Colavolpe and Raheli (see ibid., vol.47, no.9, p.1303-7, 1999), noncoherent sequence detection is extended to continuous phase modulations. In the proposed schemes, the tradeoff between complexity and performance is simply controlled by a parameter, referred to as implicit phase memory, and the number of states of a trellis diagram. Besides being realizable, these schemes have the convenient feature of allowing us to remove the constant phase assumption and encompass time-varying phase models. The proposed schemes compare favorably with other solutions previously proposed in the technical literature.
Impact of developing semiconductor technologies on two- and three-level converters for electric vehicle applications

New semiconductor technologies, like SiC, are being integrated into electric vehicles (EVs). These semiconductors reduce losses to improve the driving range of EVs, and higher switching frequencies minimize harmonic losses. This work presents a framework to quickly assess different topologies and semiconductor technologies where both two- and three-level converters are scrutinized.
On-line estimation of Dynamic State-of-Charge for lead acid battery based on fuzzy logic

The performance and life expectancy of battery depend heavily on the battery management based on SOC estimation in photovoltaic systems and electric vehicles. The objective of work presented in this paper is to develop a novel on-line estimation of SOC for the lead acid battery. The proposed method consists of two major subroutines: Open-Circuit-Voltage (OCV) extrapolation and fuzzy logic compensation. The determination of initial SOC based on OCV extrapolation functions as the basis of Coulomb counting process. Thereafter the estimation of dynamic SOC is compensated by the fuzzy logic model, which adopted discharging current and temperature as input variables to calculate the compensation coefficient. Results of the simulations as well as experiment on a VRLA lead-acid battery is also included that indicate the validity of the proposed methodology.
AN ARBITRARY LAGRANGIAN-EULERIAN FINITE ELEMENT METHOD FOR CONE-CAP PLASTICITY ; APPLICATION TO POWDER COMPACTION SIMULATION

The compaction forming of metal powder is a process involving large deformations, large strain, non-linear material behavior and friction. Consequently, the numerical analysis of such a highly non-linear process is a formidable computational problem. In this paper, an ALE technique is presented based on a generalized cap plasticity model in simulation of powder forming processes. In ALE formulation, the reference configuration is used for describing the motion, instead of material configuration in Lagrangian, and spatial configuration in Eulerian formulation. This formulation introduces some convective terms in the finite element equations and consists of two phases. Each time step is analyzed according to Lagrangian phase until required convergence is attained. Then, the Eulerian phase is applied to keep mesh configuration regular. Because of relative displacement between mesh and material, all dependent variables such as stress and strain are converted through the Eulerian phase. In order to describe the constitutive model of the highly non-linear behavior of powder materials, a single cone-cap plasticity model is developed based on a hardening rule to define the dependence of the yield surface on the degree of plastic straining. This model reflects the yielding, frictional and densification characteristics of powder along with strain and geometrical hardening, which occur during the compaction process. Finally, the powder behavior during the compaction of several components is numerically simulated. It is shown that the proposed ALE model using cap plasticity theory is capable of simulating the metal powder during compaction. A.R. Khoei , M. Anahid , A.R. Azami and S. Azizi 2
Write or Radiate

We consider information flow via physical transport of inscribed media through space and compare it to information flow via electromagnetic radiation. Somewhat counterintuitively for point to point links, physical transport of inscribed mass is often energetically more efficient by many orders of magnitude than electromagnetic broadcast. And perhaps more surprising, even in a broadcast setting (depending on the receiver density) inscribed mass transport is still energetically more efficient. We examine these results for terrestrial telecommunications as well as point to point and broadcast communication over great distances with loose delay constraints.
Predicting blood glucose levels in diabetics using feature extraction and Artificial Neural Networks

Diabetes mellitus is one of the most common chronic diseases. The number of cases of diabetes in the world is likely to increase more than two fold in the next 30 years; from 115 million in 2000 to 284 million in 2030. In type I diabetes, the disease is caused by the failure of the pancreas to produce a sufficient amount of insulin which leads to an uncontrolled increase in blood glucose unless the patient administers insulin, typically by subcutaneous injection. This work is concerned with managing diabetic patients by trying to predict their glucose levels in the near future (30 minutes) on the basis of the current levels. The goal of this paper is to determine the future blood glucose value of a diabetic patient using an artificial neural network. Unlike other approaches, which involve questioning the patients, feature extraction procedures were implemented (diabetic dynamic model) on diabetic blood glucose time series, in order to extract a knowledge (how patient blood glucose level will change a according to the external food intake and any human day activities). An artificial neural network was then trained using these features in order to predict the future value of blood glucose level with an accepted accuracy.
A Hybrid Method to Predict Angina Pectoris through Mining Emergency Data

The Emergency Department (ED) has been frustrated by the problems of overcrowding, long waiting times and high costs over decades. With the development of computer techniques, various kinds of information systems have appeared and make people work more effectively, the Emergency Department Information System (EDIS) has been heralded as a "must" for the modern ED. This paper tries to build a hybrid method to predict angina pectoris in the form of EDIS. Based on the frameworks of patients flow in ED, real-world data were collected from the electronic medical records at the ED: more than 210000 records of 842 registered chest pain patients in total. By utilizing the data mining techniques, an expert system was proposed to help physicians with faster and more accurate decision making of diagnosis and lab test selections when they are diagnosing with angina pectoris patients.
Seeding methods for run transferable libraries

Run Transferable Libraries (RTL) is an extension for GP where individualsin a population choose functions from an external library of ADF-likefunctions rather than from a set of standard GP functions. All previous work done with RTL provided a predefined function set. Thiswork investigates mechanisms by which the library can be seeded with domainrelevent functionality.