Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Dual Processing Perspective of Software Architecture Decision Making

Software architecture research tends to assume or aim for rational approaches to software architecture decision making. Such approaches attempt to search the entire problem space, and to make optimal decisions based on enduring goals. The assumption here is that rational approaches are unilaterally desirable, and that intuitive approaches relying on experience and "gut feeling" always lead to reasoning errors. However, newer research is showing us that this assumption is misguided. Evidence from fields such as cognitive psychology, general management, and new product development has shown that using intuition during certain stages of the design process leads to more creative ideas, and may result in better and faster decision making during design activities. Therefore, by disparaging or ignoring intuition in software design, we are potentially missing out on an opportunity to improve software architecture decision making, by using intuition in contexts where it is appropriate and useful. Thus, this paper outlines the case for investigating the combination of intuition with rationality in software architecture decision making.
An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction

The energy landscape, which organizes microstates by energies, has shed light on many cellular processes governed by dynamic biological macromolecules leveraging their structural dynamics to regulate interactions with molecular partners. In particular, the protein energy landscape has been central to understanding the relationship between protein structure, dynamics, and function. The landscape view, however, remains underutilized in an important problem in protein modeling, decoy selection in template-free protein structure prediction. Given the amino-acid sequence of a protein, template-free methods compute thousands of structures, known as decoys, as part of an optimization process that seeks minima of an energy function. Selecting biologically-active/native structures from the computed decoys remains challenging. Research has shown that energy is an unreliable indicator of nativeness. In this paper, we advocate that, while comparison of energies is not informative for structures that already populate minima of an energy function, the landscape view exposes the overall organization of generated decoys. As we demonstrate, such organization highlights macrostates that contain native decoys. We present two different computational approaches to extracting such organization and demonstrate through the presented findings that a landscape-driven treatment is promising in furthering research on decoy selection.
Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

With accurate traffic prediction, future cellular networks can make self-management and embrace intelligent and efficient automation. This letter devotes itself to citywide cellular traffic prediction and proposes a deep learning approach to model the nonlinear dynamics of wireless traffic. By treating traffic data as images, both the spatial and temporal dependence of cell traffic are well captured utilizing densely connected convolutional neural networks. A parametric matrix based fusion scheme is further put forward to learn influence degrees of the spatial and temporal dependence. Experimental results show that the prediction performance in terms of root mean square error can be significantly improved compared with those existing algorithms. The prediction accuracy is also validated by using the data sets of Telecom Italia.
Green TCP Transmission Over Cognitive Radio Networks

The last decade has witnessed an unprecedented growth of mobile data traffic and energy consumption in wireless networks. Energy consumption can incur greenhouse gas emission and high expenditure for maintaining wireless networks. To reduce the energy consumption, some previous works have so far considered improving the energy efficiency of various wireless networks. However, most of them largely ignore the energy efficiency of delivering transmission control protocol (TCP) applications, one of the most popular transmission protocols used for many Internet applications. Even though a couple of previous works have studied the problem of energy-efficient TCP transmission, they do not optimize its energy efficiency by taking into account the interweaving of parameters deployed at different layers of the protocol stack. Hence, they may not indeed lead to energy-efficient TCP transmission in wireless networks. In this paper, we consider a unified way of reducing energy consumption for transmitting TCP applications in cognitive radio networks (CRNs). In particular, we design an energy-efficient TCP transmission scheme for CRNs, which takes into account spectrum sensing, spectrum access, modulation and coding scheme, transmission power, and TCP packet length to enhance the energy efficiency. To validate our proposed scheme, we offer simulation results to show the significant energy efficiency improvement.
Minimally Invasive Intervention for Primary Caries Lesions: Are Dentists Implementing This Concept?

Contemporary minimally invasive treatment concepts for restorative treatment of primary caries lesions include both delayed intervention and smaller-sized preparations restricted to removal of carious tissue. The aim of this study was to investigate whether these concepts have resulted in a trend towards a more conservative choice made by dentists regarding treatment thresholds and restorative techniques. The results from previously conducted, precoded questionnaires developed by Espelid and Tveit, as well as from a recent Dutch questionnaire, were collected and analysed. A worldwide trend towards more minimally invasive strategies in the operative treatment of caries lesions could not be observed, neither for the initiation of operative treatment nor for the preparation techniques. However, in some countries, changes over time could be assessed, especially in Norway, where a reduction in the proportion of interventions is visible for both occlusal and approximal lesions, indicating that more dentists are postponing interventions until the lesions have progressed to a deeper level. From the Dutch national survey, it could be concluded that operators that intervene at an earlier stage of approximal lesioning (stage ≤4) also intervene at an earlier stage of occlusal caries (stage ≤3) (p = 0.012; OR = 2.52; 95% CI: 1.22–5.22). Generally, it can be concluded that dentists worldwide still tend to operatively intervene at a too early stage of caries, although variations exist between countries. A worldwide shift could be observed in the restorative material applied, since composite resin has almost completely replaced amalgam for restoring primary caries lesions.
Inhomogeneous Double Thinning—Modeling and Analysis of Cellular Networks by Using Inhomogeneous Poisson Point Processes

In this paper, we introduce a new methodology for modeling and analyzing downlink cellular networks, where the base stations (BSs) constitute a motion-invariant point process (PP) that exhibits some degree of interactions among the points, i.e., spatial repulsion or spatial clustering. The proposed approach is based on the theory of inhomogeneous Poisson PPs (I-PPPs) and is referred to as inhomogeneous double thinning (IDT) approach. In a PP, the distribution of the distance from a randomly distributed (typical) user to its nearest BS depends on the degree of spatial repulsion or clustering exhibited by the PP. In addition, the average number of interfering BSs that lies within a given distance from the typical user is a function of the repulsion and clustering characteristics of the PP. The proposed approach consists of approximating the original motion-invariant PP with an equivalent PP that is made of the superposition of two conditionally independent I-PPPs. The inhomogeneities of both PPs are created from the point of view of the typical user (“user-centric”): the first one is based on the distribution of the user’s distance to its nearest BS and the second one is based on the distance-dependent average number of interfering BSs around the user. The inhomogeneities are mathematically modeled through two distance-dependent thinning functions and a tractable expression of the coverage probability is obtained. Sufficient conditions on the parameters of the thinning functions that guarantee better or worse coverage compared with the baseline homogeneous PPP model are identified. The accuracy of the IDT approach is substantiated with the aid of empirical data for the spatial distribution of the BSs.
Lower- and Higher-Level Social Cognitive Factors Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls: Relationship With Neurocognition and Functional Outcome

Background Schizophrenia spectrum disorders (SSDs) often feature social cognitive deficits. However, little work has focused on the factor structure of social cognition, and results have been inconsistent in schizophrenia. This study aimed to elucidate the factor structure of social cognition across people with SSDs and healthy controls. It was hypothesized that a 2-factor model, including lower-level "simulation" and higher-level "mentalizing" factors, would demonstrate the best fit across participants. Methods Participants with SSDs (N = 164) and healthy controls (N = 102) completed social cognitive tasks ranging from emotion recognition to complex mental state inference, as well as clinical and functional outcome, and neurocognitive measures. Structural equation modeling was used to test social cognitive models, models of social cognition and neurocognition, measurement invariance between cases and controls, and relationships with outcome measures. Results A 2-factor (simulation and mentalizing) model fit the social cognitive data best across participants and showed adequate measurement invariance in both SSD and control groups. Patients showed lower simulation and mentalizing scores than controls, but only mentalizing was significantly associated with negative symptoms and functional outcome. Social cognition also mediated the relationship between neurocognition and both negative symptoms and functional outcome. Conclusions These results uniquely indicate that distinct lower- and higher-level aspects of social cognition exist across SSDs and healthy controls. Further, mentalizing may be particularly linked to negative symptoms and functional outcome. This informs future studies of the neural circuitry underlying social cognition and the development of targeted treatment options for improving functional outcome.
EMI Filter Design Based on High-Frequency Modeling of Common-mode Chokes

Aiming at the bad performance of EMI filter in the high frequency band, the non-ideal factors of common-mode chokes and the limitations of the existing high-frequency model are summarized. Considering the frequency characteristics of core material and the influence of parasitic capacitance, an improved cascaded Foster network model is established, and then the common-mode and differential-mode high-frequency equivalent circuits of EMI filter in lS0kHz-30MHz are developed. Furthermore, an EMI filter design method based on the optimization of the impedance characteristics of common- mode chokes is proposed. An experiment is carried out to verify the method by an 1.2SkW Boost PFC prototype. Results indicate that the device with the proposed EMI filter can pass the conducted EMI test successfully, and the design scheme can provide effective guidance for the remediation of conducted electromagnetic interference.
Current Sensorless MTPA Operation of Interior PMSM Drives for Vehicular Applications

This paper presents a direct voltage control method for interior permanent magnet synchronous motors (IPMSMs) with a single speed regulator. The method achieves maximum torque per ampere (MTPA) operation by controlling the magnitude and the angle of the voltage vector. For that, the mathematical model for the MTPA trajectory of the IPMSM is derived in the voltage plane. As such, no current sensor is needed, which makes the proposed strategy tolerant to current sensors failure unlike cascaded control loop based methodologies. Although no current sensor is used, the control strategy tracks MTPA trajectory by taking into account both voltage and current limits of the machine. The complete MTPA derivation in the voltage plane is presented in this paper; but, only the final solution is needed for real-time implementation. Henceforth, the simplicity of the control scheme combined with its current sensor dependence free characteristics make it a good candidate for real-time implementation in vehicular applications. The concept is developed and evaluated experimentally on a 10-HP IPMSM.
Engineering Software Architectures of Blockchain-Oriented Applications

Building blockchain-oriented applications forces developers to rethink the architecture of their software from the ground up. The use of blockchain technology poses multiple challenges as the software is operated in a decentralized, trustless, transparent and tamper-proof environment. When building decentralized apps the developers need to deal with blockchain properties such as decentralization, a certain delay in the execution of function calls of distributed code contracts and particularly need to consider how users interact with their application. We surveyed several existing decentralized apps and examined their architecture to identify multiple reoccurring architectural patterns, each with different implications regarding the trust, user experience and security. As building blockchain-oriented applications is gaining importance, models, tools and methods for blockchain-oriented software engineering have to be developed. This paper gives a first hint towards architectural patterns for blockchain-based applications and motivates why it is important to consider how the user interacts with the decentralized apps.
Be Aware of the Patient With Benign Follicular Thyroid Lesion Histology and Rising Thyroglobulin Level.

OBJECTIVE The accurate diagnosis of thyroid follicular/Hürthle cell tumors is challenging and a matter of controversy. We present a series of patients in whom a misclassification of follicular/Hürthle cell thyroid lesions as benign has led to devastating clinical outcomes. METHODS The Thyroid Cancer Registry of Rabin Medical Center was screened for patients with metastatic differentiated thyroid carcinoma (DTC) who had been initially diagnosed with benign follicular lesion between 1974 and 2015 and treated with hemithyroidectomy. Clinical, pathologic, and outcome data were collected from the medical files. Adequate pathology specimens, when available, were re-evaluated. RESULTS Seven patients met the inclusion criteria. The original pathologic diagnosis was follicular adenoma in 4 patients and Hürthle cell adenoma in 3 patients. Five patients had bone metastases, of whom one also had lung metastases and one, liver metastases. One patient had both cervical and lung metastases, and 1 patient had only meta-static neck lymph nodes. Six patients had a final diagnosis of encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC), and 1 patient was diagnosed as having follicular thyroid cancer metastasis by bone biopsy. In 3 of the patients, capsular invasion was detected retrospectively; only 1 patient had evidence of vascular invasion. All 7 patients had high levels of thyroglobulin at diagnosis of metastatic DTC. CONCLUSION Misclassification of follicular thyroid lesions as benign may lead to progressive disseminated DTC. To minimize the clinical risk of misdiagnosis, especially if a thorough evaluation of the specimens by an experienced pathologist is unfeasible, we suggest long-term follow-up of serum thyroglobulin levels. ABBREVIATIONS DTC = differentiated thyroid carcinoma; EFVPTC = encapsulated follicular variant of papillary thyroid carcinoma; FVPTC = follicular variant of papillary thyroid carcinoma; NIFTP = noninvasive follicular thyroid neoplasm with papillary-like nuclear features; PTC = papillary thyroid carcinoma.
FGF21 increases water intake, urine output and blood pressure in rats

Fibroblast growth factor 21 (FGF21) is a hormone secreted by the liver in response to metabolic stress. In addition to its well-characterized effects on energy homeostasis, FGF21 has been shown to increase water intake in animals. In this study, we sought to further explore the effects of FGF21 on fluid homeostasis in rats. A single dose of a long-acting FGF21 analog, PF-05231023, significantly increased water consumption, which was accompanied by an elevation in urine output that appeared prior to a significant change in water intake. We observed that FGF21 rapidly and significantly increased heart rate and blood pressure in telemeter-implanted rats, before changes in urine output and water intake were observed. Our data suggest that sympathetic activation may contribute to the pathogenesis by which FGF21 increases blood pressure as the baroreceptor unloading induced reflex tachycardia was significantly elevated in FGF21-treated animals. However, FGF21 was still capable of causing hypertension in animals in which approximately 40% of the sympathetic post-ganglionic neurons were ablated. Our data suggest that FGF21-induced water intake is in fact secondary to diuresis, which we propose to be a compensatory mechanism engaged to alleviate the acute hypertension caused by FGF21.
A MEMS Resonant Accelerometer With High Performance of Temperature Based on Electrostatic Spring Softening and Continuous Ring-Down Technique

Temperature fluctuations seriously damage the performance of resonant accelerometers. Two approaches have been proposed in this paper to address this problem. In structural design, an electrostatic softening spring is utilized to modulate the external acceleration on the frequency of resonator’s anti-phase modal without any stress transmission and thus improve the thermal robustness of the accelerometer. In control circuit, a continuous ring-down technique is employed for real-time quality factor (utilized as a virtual thermometer) monitoring, which attenuates measuring hysteresis and improves the precision of temperature compensation. A prototype is implemented to verify the proposed approaches. Experiments show that this accelerometer is insensitive to stress and has better performance of temperature compared with the vibration beam resonant accelerometer. The bias stability of acceleration output was improved from 35.2 to 0.08 mg with real-time quality factor compensation ranging from −40 °C to +40 °C.
Guide Fuzzing with Multi-Factor Potential Analysis

Fuzzing is a popular technique for software vulnerability mining. Although the state-of-the-art fuzzers combine many popular technologies to overcome the shortcomings of fuzzing, it leaves a lot to be desired. Symbolic execution can help fuzzer to generate effective input, but it brings heavy loads. Other technologies are difficult to support fuzzing to accurately generate inputs that satisfy constraints. Therefore, we propose Multi-Factor Potential Analysis (MPA), a new search strategy that enables fuzzing to traverse more paths based on symbolic execution. The goal of its search process is to find an unexplored path, in symbolic execution, which is easy to solve and has distinguished contribution to the growth rate of path coverage. Moreover, it also takes into account the high-risk functions contained in the path. Tinker-MPA, a tool that implements MPA strategy, is implemented. It traverses more paths in a limited time than the other state-of-the-art fuzzing tools such as AFL and Tinker on DARPA CGC benchmark. Besides, the vulnerability mining of Tinker-MPA is more efficient.
A Novel Tetramethylpyrazine Derivative Protects Against Glutamate-Induced Cytotoxicity Through PGC1α/Nrf2 and PI3K/Akt Signaling Pathways

Glutamate-induced excitotoxicity is one of the main causes of neuronal cell death in stroke. Compound 22a has been previously reported as a promising neuroprotective compound derived from tetramethylpyrazine, which is a widely used active ingredient of traditional Chinese medicine Chuanxiong (Ligusticum wallichii Franchat). Compound 22a can protect neurons from oxidative stress-induced PC12 cell death and alleviates the infarct areas and brain edema in a rat permanent middle cerebral artery occlusion model. In the current work, we further investigated the neuroprotective effects and underlying mechanisms of compound 22a against glutamate-induced excitotoxicity in primary culture of rat cerebellar granule neurons (CGNs). We found that pretreatment with compound 22a prevented glutamate-induced neuronal damage by maintaining mitochondrial membrane potential and attenuating cellular apoptosis. Compound 22a could also enhance peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) transcriptional activity and induce nuclear accumulation of Nrf2 in PC12 cells. Accordingly, pretreatment with compound 22a reversed the glutamate-induced down-regulation of expression of the proteins PGC1α, transcriptional factor NF-E2-related factor 2 (Nrf2), and hemooxygenase 1 (HO-1). In addition, compound 22a increased the phosphorylation of phosphoinositide 3-kinase (p-PI3K), phosphorylated protein kinase B (p-Akt), and glycogen synthase kinase 3β (p-GSK3β). Meanwhile, the small interfering RNA-mediated silencing of PGC1α expression and selective inhibitors targeting PI3K/Akt (LY294002 and Akt-iv) could significantly attenuate the neuroprotective effect of compound 22a. Taken together, compound 22a protected against glutamate-induced CGN injury possibly in part through regulation of PGC1α/Nrf2 and PI3K/Akt pathways.
Deep learning feature extraction for multispectral palmprint identification

Abstract. Person’s identity validation is becoming much more essential due to the increasing demand for high-security systems. A biometric system testifies the authenticity of specific physiological or behavioral characteristics-based biometric technology. This technology has been successfully applied to verification and identification systems. We analyze the multispectral palmprint biometric identification system in unimodal and multimodal modes. In an identification system, the feature extraction is a crucial step. For this reason, we propose an efficient deep learning feature extraction algorithm called discrete cosine transform network (DCTNet). The effectiveness of the proposed approach has been evaluated on two publicly available databases: CASIA and PolyU. The obtained results clearly indicate that the DCTNet deep learning-based feature extraction technique can achieve comparable performance to the best of the state-of-the-art techniques.
Extracting Inter-community Conflicts in Reddit

Anti-social behaviors in social media can happen both at user and community levels. While a great deal of attention is on the individual as an ‘aggressor,’ the banning of entire Reddit subcommunities (i.e., subreddits) demonstrates that this is a multi-layer concern. Existing research on inter-community conflict has largely focused on specific subcommunities or ideological opponents. However, antagonistic behaviors may be more pervasive and integrate into the broader network. In this work, we study the landscape of conflicts among subreddits by deriving higher-level (community) behaviors from the way individuals are sanctioned and rewarded. By constructing a conflict network, we characterize different patterns in subreddit-to-subreddit conflicts as well as communities of ‘co-targeted’ subreddits . The dynamics of these interactions also reveals a shift in conflict focus over time.