Miracle-dz/UniFAR-FTUs huggingface dataset:
Interdisciplinary Management of Cystic Neoplasms of the Pancreas

Cystic neoplasms of the pancreas are increasingly recognized due to the frequent use of abdominal imaging. It is reported that up to 20% of abdominal cross-sectional scans identify incidental asymptomatic pancreatic cysts. Proper characterization of pancreatic cystic neoplasms is important not only to recognize premalignant lesions that will require surgical resection, but also to allow nonoperative management of many cystic lesions that will not require resection with its inherent morbidity. Though reliable biomarkers are lacking, a wide spectrum of diagnostic modalities are available to evaluate pancreatic cystic neoplasms, including radiologic, endoscopic, laboratory, and pathologic analysis. An interdisciplinary approach to management of these lesions which incorporates recent, specialty-specific advances in the medical literature is herein suggested.
Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation

Word embedding has been found to be highly powerful to translate words from one language to another by a simple linear transform. However, we found some inconsistence among the objective functions of the embedding and the transform learning, as well as the distance measurement. This paper proposes a solution which normalizes the word vectors on a hypersphere and constrains the linear transform as an orthogonal transform. The experimental results confirmed that the proposed solution can offer better performance on a word similarity task and an English-toSpanish word translation task.
Design of Human Tracking Algorithm Based on Improved Camshift

Considering the problem of added background interference when initial target region is chose in Camshift tracking algorithm. This paper proposes a human tracking algorithm based on improved Camshift. The algorithm uses weight to determine the type of pixels in the back-projection, and then convert the back-projection into binary image, so as to improve the input accuracy of Camshift processing function. According to the human body size information, the algorithm appropriately improves the ratio of major axis and minor axis of search window, to optimize the output size of Camshift processing function. The contrast experiment proves the algorithm obviously improve accuracy and robustness of tracking effect.
Design and investigation of an optical standing-wave interferometer for displacement measurements

Laser interferometers are important instruments for the measurement of length in today's mechanical engineering and manufacturing technology. The principle on which interferometers have operated to date is that of interference between beams with the same direction of propagation. However, optical beams can interfere with each other not only in the same direction of propagation but also in opposing directions. The name given to this type of interference is the standing wave. A beam of light strikes a plane mirror at 90° to it, is reflected and interferes with the beam currently being reflected at the mirror. The outcome of the interference is a standing wave in front of the plane mirror. The only way of detecting the maxima and minima of the intensity of a standing wave photoelectrically is to use a photoelectric detector which is partially transparent. The photoelectric detector is placed in path of the standing wave, which propagates through it. Phase-shifted signals can be received if two photoelectric detectors with a phase shift between them are positioned in the standing wave. These enable sin and cos signals to be registered so that bi-directional fringe counting can take place. The authors have named this assembly an optical Standing-Wave Interferometer. The form taken by the partially transparent photoelectric detectors is that of photodiodes based on amorphous silicon, in a TCO-pin-TCO structure. Phase-shifted signals are received by two components with a TCO1-(pin)1-TCO2-(pin)2-TCO3 composition, integrated at the engineering stage, which are called by the authors transparent phase selective photodiodes (TPS). The TPS have been used to carry out measurement of length in a technological setting in such a way that the standing-wave interferometer could be compared with a plane mirror interferometer.
Traffic-Aware Patching for Cyber Security in Mobile IoT

The various types of communication technologies and mobility features in IoT on one hand enable fruitful and attractive applications, but on the other hand facilitate malware propagation, thereby raising new challenges in handling IoT-empowered malware for cyber security. Compared to the malware propagation control scheme in traditional wireless networks, where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to patch. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and practical solution. Specifically, patching intermediate nodes can effectively prevent the proliferation of malware propagation by securing infrastructure links and limiting malware propagation to local device-to-device dissemination. This article proposes a novel traffic-aware patching scheme to select important intermediate nodes to patch, which applies to the IoT system with limited patching resources and response time constraint. Experiments on real-world trace datasets in IoT networks are conducted to demonstrate the advantage of the proposed traffic-aware patching scheme in alleviating malware propagation.
A Two-Step Target Binding and Selectivity Support Vector Machines Approach for Virtual Screening of Dopamine Receptor Subtype-Selective Ligands

Target selective drugs, such as dopamine receptor (DR) subtype selective ligands, are developed for enhanced therapeutics and reduced side effects. In silico methods have been explored for searching DR selective ligands, but encountered difficulties associated with high subtype similarity and ligand structural diversity. Machine learning methods have shown promising potential in searching target selective compounds. Their target selective capability can be further enhanced. In this work, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching DR subtype-selective ligands, which was tested together with three previously-used machine learning methods for searching D1, D2, D3 and D4 selective ligands. It correctly identified 50.6%–88.0% of the 21–408 subtype selective and 71.7%–81.0% of the 39–147 multi-subtype ligands. Its subtype selective ligand identification rates are significantly better than, and its multi-subtype ligand identification rates are comparable to the best rates of the previously used methods. Our method produced low false-hit rates in screening 13.56 M PubChem, 168,016 MDDR and 657,736 ChEMBLdb compounds. Molecular features important for subtype selectivity were extracted by using the recursive feature elimination feature selection method. These features are consistent with literature-reported features. Our method showed similar performance in searching estrogen receptor subtype selective ligands. Our study demonstrated the usefulness of the two-step target binding and selectivity screening method in searching subtype selective ligands from large compound libraries.
Data persistence in sensor networks: towards optimal encoding for data recovery in partial network failures

Sensor networks consist of a number of sensors spread across a geographical area. Each sensor has communication capability and some level of intelligence for signal processing and networking of data. Each sensor node in the network routinely 'senses' and stores data from its immediate environment. An important requirement of the sensor network is that the collected data be disseminated to the proper end users. In some cases, there are fairly strict requirements on this communication. For example, the detection of an intruder in a surveillance network should be immediately communicated to the police authorities. Each sensor node also has some storage capacity to store the collected data or to assemble the data prior to communicating it to another node.
Arabidopsis lyrata Small RNAs: Transient MIRNA and Small Interfering RNA Loci within the Arabidopsis Genus[W][OA]

This study exploits the recent production of a draft nuclear genome sequence for Arabidopsis lyrata to examine evolution of plant MIRNA and p4-siRNA loci between two congenic Brassicaceae species. The results indicate that many MIRNAs and most p4-siRNA hot spots are rapidly changing and evolutionarily transient within the Arabidopsis genus. Twenty-one-nucleotide microRNAs (miRNAs) and 24-nucleotide Pol IV-dependent small interfering RNAs (p4-siRNAs) are the most abundant types of small RNAs in angiosperms. Some miRNAs are well conserved among different plant lineages, whereas others are less conserved, and it is not clear whether less-conserved miRNAs have the same functionality as the well conserved ones. p4-siRNAs are broadly produced in the Arabidopsis genome, sometimes from active hot spot loci, but it is unknown whether individual p4-siRNA hot spots are retained as hot spots between plant species. In this study, we compare small RNAs in two closely related species (Arabidopsis thaliana and Arabidopsis lyrata) and find that less-conserved miRNAs have high rates of divergence in MIRNA hairpin structures, mature miRNA sequences, and target-complementary sites in the other species. The fidelity of miRNA biogenesis from many less-conserved MIRNA hairpins frequently deteriorates in the sister species relative to the species of first discovery. We also observe that p4-siRNA occupied loci have a slight tendency to be retained as p4-siRNA loci between species, but the most active A. lyrata p4-siRNA hot spots are generally not syntenic to the most active p4-siRNA hot spots of A. thaliana. Altogether, our findings indicate that many MIRNAs and most p4-siRNA hot spots are rapidly changing and evolutionarily transient within the Arabidopsis genus.
Pseudo-Differential Sensing Framework for STT-MRAM: A Cross-Layer Perspective

With the rapid increase of leakage currents, non-volatile memories have become competitive candidates in the next-generation computer architecture. Among them, STT-MRAM shows great promise in working memory with high density, high speed and tremendous endurance, etc. However, based on our investigations, the dynamic write power and read reliability are two critical challenges of STT-MRAM. In this work, we propose a synergistic pseudo-differential sensing (PDS) framework that employs device, circuit and architectural techniques to address these challenges. In specific, three design techniques, including cell cluster, asymmetric sensing amplifier and self-error-detection-correction, are proposed to implement the PDS framework. We show that the holistic device-circuit-architecture cross-layer co-design enables STT-MRAM to be utilized in the cache memory, benefiting from the improved density, reliability and energy-efficiency. Our experimental results show that the proposed PDS scheme improves the read margin by ∼35.6 percent, reduces the area, read latency, read energy, write latency and write power by ∼46.7, ∼9.8, ∼30.3, ∼2.3 and ∼31.1 percent respectively, compared with the typical 1T1MTJ cell structure for the cache capacity of 8 MB. In addition, the proposed PDS scheme reduces the dynamic energy by ∼32.9 percent and leakage energy by ∼830 percent, improves the IPC by ∼1.3 percent and miss rate by ∼36.9 percent respectively, compared with conventional SRAM based cache.
MCR for CLIR

The Multilingual Central Repository (MCR) is based on the design of the EuroWordNet database. The MCR holds WordNets in various languages (English, Spanish, Italian, Catalan and Basque), which are interconnected via an Inter-Lingual- Index (ILI). In addition, the MCR holds a number of ontologies and domain labels related to all concepts. This paper describes the integration and evaluation of the MCR in a cross-lingual information retrieval system, developed by Irion Technologies, as well as a public exercise for evaluating combined MCR, WSD and CLIR strategies.
Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management

This paper presents an integrated distribution locational marginal pricing (DLMP) method designed to alleviate congestion induced by electric vehicle (EV) loads in future power systems. In the proposed approach, the distribution system operator (DSO) determines distribution locational marginal prices (DLMPs) by solving the social welfare optimization of the electric distribution system which considers EV aggregators as price takers in the local DSO market and demand price elasticity. Nonlinear optimization has been used to solve the social welfare optimization problem in order to obtain the DLMPs. The efficacy of the proposed approach was demonstrated by using the bus 4 distribution system of the Roy Billinton Test System (RBTS) and Danish driving data. The case study results show that the integrated DLMP methodology can successfully alleviate the congestion caused by EV loads. It is also shown that the socially optimal charging schedule can be implemented through a decentralized mechanism where loads respond autonomously to the posted DLMPs by maximizing their individual net surplus.
How Should Technology Affordances Be Measured? An Initial Comparison of Two Approaches

Technology affordance and constraint theory (TACT) is an emerging perspective in the Information Systems (IS) literature to understand technology use and consequences. A technology affordance (or constraint) is defined as a relational construct that depends on the interaction between technology features and individual goals (e.g.,Leonardi 2013; Majchrzak and Markus 2012; Strong et al. 2014; Volkoff and Strong 2013). Most of the existing TACT research is either qualitative or conceptual. Researchers argue that to establish the status of TACT as a lens to understand technology use and consequence, it is necessary to apply TACT to a specific context, generate some testable propositions and empirically test these developed propositions (Majchrzak and Markus 2012). Measuring technology affordances represents a challenge to researchers.
Fabrication and characterization of bipolar plates of vinyl ester resin/graphite-based composite for polymer electrolyte membrane fuel cells

The lightweight economical bipolar plates for proton exchange membrane fuel cells were fabricated by using vinyl ester resin as matrix material, graphite powder as reinforcement and conducting polymer as additive. Effects of various contents of graphite and its particle size on electrical conductivity, physical and mechanical properties of the composite were investigated. The optimum composition of composite plates was determined at 55% graphite, 40% resin and 5% poly(1,4-phenylene sulphide) based on measurements of physical, electrical and mechanical properties with various graphite contents. The plate molded with 2.93 mm thickness gives density of 1.81 g cm−3 and compressive strength of 73 MPa, which is in consonance with reported standard. Thermogravimetric analysis–differential thermal analysis–derivative thermogravimetry of the composite was carried out in air atmosphere to find out mode of degradation and basis of its stability. The study reveals that composite is less stable than resin and graphite, but much more stable than operational fuel cell which works at 80–100°C.
State and maximum friction coefficient estimation in vehicle dynamics using UKF

Advanced driver assistance systems in modern vehicles have gained interest in the past decades. Most of these systems rely decisively on knowledge of the vehicle's state and influential parameters. Due to changing system or environmental conditions the functionality of these systems may lead to decreased performance or even failure. Especially, the road condition, represented by the maximum friction coefficient, essentially influencing the interaction of tires and road, has major influence on the vehicle's behavior. Therefore, a vast improvement of the assistance systems' performance can be achieved by online maximum friction coefficient estimation. In this paper a simultaneous online estimation of the vehicle's state and maximum friction coefficient is presented using a joint Unscented Kalman Filter. The state and friction estimation results are validated by comparing to measurements taken on a Volkswagen Golf GTE Plug-In Hybrid and offline identified values, respectively.
A decision support system to improve e-learning environments

Nowadays, due to the lack of face-to-face contact, distance course instructors have real difficulties knowing who their students are, how their students behave in the virtual course, what difficulties they find, what probability they have of passing the subject, in short, they need to have feedback which helps them to improve the learning-teaching process. Although most Learning Content Management Systems (LCMS) offer a reporting tool, in general, these do not show a clear vision of each student's academic progression. In this work, we propose a decision making system which helps instructors to answer these and other questions using data mining techniques applied to data from LCMSs databases. The goal of this system is that instructors do not require data mining knowledge, they only need to request a pattern or model, interpret the result and take the educational actions which they consider necessary.
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases

Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources. Existing solutions have approached these three challenges in isolation, and with only a particular type of performance goal in mind. In this paper, we introduce WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance metrics and workload characteristics. Our approach relies on supervised learning to train cost-effective decision tree models for guiding query placement, scheduling, and resource provisioning decisions. Applications can use these models for both batch and online scheduling of incoming workloads. A unique feature of our system is that it can adapt its offline model to stricter/looser performance goals with minimal re-training. This allows us to present alternative workload management strategies that address the typical performance vs. cost trade-off of cloud services. Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance goals and workload characteristics.
Blue-emitting organic electrofluorescence materials: progress and prospective

The intense interest in organic light-emitting devices (OLEDs) originates from their attractive prospects as the next generation display and lighting technologies. The development of blue emitters is of great significance in OLED applications as full-color displays and energy-saving lightings. The electrofluorescence using triplet energy for radiation has recently become a spotlight in the area of organic electronics. Various triplet-to-singlet conversion mechanisms have been established, including triplet–triplet annihilation (TTA), thermally activated delayed fluorescence (TADF) and “hot exciton” model with hybridized local and charge-transfer (HLCT) excited state, and they are expected to shed light on the development on blue OLEDs. This study revolves around the recent progress in blue electrofluorescence materials utilizing triplet excitons for radiation. Owing to the page limitation, special focus is placed on small molecule-based purely organic fluorophores with breakthrough device performances. We begin with the general information of blue-emitting organic electrofluorescence devices, and then give an overview of blue fluorescence OLEDs based on different electroluminescence mechanisms, which is followed by individual molecular design strategies towards high efficiency.
A feedforward S-band MIC amplifier system

A 2.2 GHz high-power feedforward amplifier system has been designed and fabricated, which has an RF gain of 30 dB and delivers an output power of 1.25 W with all IM distortion products at least down 50 dB from the carrier level. The power amplifiers and passive circuit components used in this system are all realised in thin-film hybrid form. The theoretical development of the system is described. The result of the temperature stability tests is given. The computer optimisation technique with multiband weighting functions used throughout the amplifier design process is presented. Finally, practical applications and a comparison of the advantages and drawbacks of this (feedforward) amplifier system with those produced by using the conventional (back-off) approach are discussed.
Data-driven approach for distribution network topology detection

This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on the real time analysis of time-series voltages measurements. The analysis approach draws on data from high-precision phasor measurement units (μPMUs or synchrophasors) for distribution networks. The key fact is that time-series measurement data taken from the distribution network has specific patterns representing state transitions such as topology changes. The proposed algorithm is based on comparison of actual voltage measurements with a library of signatures derived from the possible topologies simulation. The IEEE 33-bus model is used for the algorithm validation.