Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiated electromagnetic immunity analysis of VCO using IC stripline method

As system integration in wireless portable electronic devices has increased significantly, the analysis of electromagnetic compatibility (EMC) has become important to prevent the malfunction of integrated circuits (ICs). To evaluate the radiative immunity of an integrated circuit, we employ the IEC-standardized IC stripline method. In this paper, we provide an electromagnetic immunity analysis of the radiated electromagnetic noise coupling from an IC stripline to a ring voltage-controlled oscillator (VCO). We present the design of an IC stripline that meets the IEC 62132-8 standard, the test setup and the experimental results of the VCO test chip. The measurement shows that the radio frequency (RF) source from the IC stripline causes changes in the center frequency and output characteristics. VCO output variations show that the design is relatively immune to radiated noise of the same frequency as the oscillator output, and the output frequency spectrum spreads out more with higher noise amplitude due to injection. The experiment shows that the IC stripline method is an effective solution for the radiated electromagnetic analysis of an IC.
WikiLabel: an encyclopedic approach to labeling documents en masse

This paper presents a particular approach to collective labeling of multiple documents, which works by associating the documents with Wikipedia pages and labeling them with headings the pages carry. The approach has an obvious advantage over past approaches in that it is able to produce fluent labels, as they are hand-written by human editors. We carried out some experiments on the TDT5 dataset, which found that the approach works rather robustly for an arbitrary set of documents in the news domain. Comparisons were made with some baselines, including the state of the art, with results strongly in favor of our approach.
Efficient development and reuse of domain-specific languages for automation systems

Domain-Specific Languages DSLs help to decrease system development costs by providing developers with an effective way to construct systems for a specific domain. However, for DSL construction, a developer has to invest some upfront investment. If this investment is smaller than the benefit in terms of more effective development for domain-specific systems, then the construction of a DSL pays off. In order to decrease the initial effort to construct DSLs for a specific domain the automation domain, we present an efficient DSL architecture which allows structured reuse within the automation domain. With this DSL architecture, it is easier to build an initial automation DSL and when building multiple automation DSLs, significant parts of the DSL can be reused across different automation domains. We present the DSL architecture and discuss its benefits and drawbacks. Furthermore, we present and evaluate three automation DSL case studies which apply the described architecture.
Coordination of Cloud Computing and Smart Power Grids

The emergence of cloud computing has established a trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers and cloud computing also provide opportunities to help the grid with respect to robustness and load balancing. To gain insights into these opportunities, we formulate the service request routing problem in cloud computing jointly with the power flow analysis in smart grid and explain how these problems can be related. Simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that a grid-aware service request routing design in cloud computing can significantly help in load balancing in the electric grid and making the grid more reliable and more robust with respect to link breakage and load demand variations.
Can we understand van gogh's mood?: learning to infer affects from images in social networks

Can we understand van Gogh's mood from his artworks? For many years, people have tried to capture van Gogh's affects from his artworks so as to understand the essential meaning behind the images and catch on why van Gogh created these works. In this paper, we study the problem of inferring affects from images in social networks. In particular, we aim to answer: What are the fundamental features that reflect the affects of the authors in images? How the social network information can be leveraged to help detect these affects? We propose a semi-supervised framework to formulate the problem into a factor graph model. Experiments on 20,000 random-download Flickr images show that our method can achieve a precision of 49% with a recall of 24% on inferring authors'affects into 16 categories. Finally, we demonstrate the effectiveness of the proposed method on automatically understanding van Gogh's Mood from his artworks, and inferring the trend of public affects around special event.
The Fudan Participation in the 2015 BioASQ Challenge: Large-scale Biomedical Semantic Indexing and Question Answering

This article describes the participation of Fudan team in the 2015 BioASQ challenge. The challenge consists of two tasks, large- scale biomedical semantic indexing (task 3a) and biomedical question answering (task 3b). In task 3a, our method, MeSHLabeler, achieved the rst place in all 15 weeks of three batches. Based on 3215 annotated citations (June 6, 2015) out of all 4435 citations in the ocial
Drugs of Abuse Monitoring in Blood for Control of Driving Under the Influence of Drugs

Driving under the influence of drugs is an issue of growing concern in the industrialized countries as a risk and a cause for road accidents. In forensic toxicology, the increasing number of samples for determination of drugs in blood is mainly due to zero-tolerance laws in several countries and well-trained police officers who can better recognize drivers under the influence of drugs of abuse. This review describes procedures for detection of the following drugs of abuse in whole blood, plasma, and serum: amphetamine, methamphetamine, 3,4-methylenedioxy methamphetamine (MDMA), N-ethyl-3, 4-methylenedioxyamphetamine (MDEA), 3,4-methylenedioxyamphetamine (MDA), cannabinoids (delta-9-tetrahydrocannabinol [THC], 11-hydroxy-delta-9-THC, 11-nor-9-carboxy-delta-9-THC), cocaine, benzoylecgonine, ecgonine methyl ester, cocaethylene, the opiates (heroin, 6-monoacetylmorphine, morphine, or codeine), and methadone as well as gamma-hydroxybutyric acid (GHB), lysergic acid diethylamide (LSD), phencyclidine (PCP), and psilocybin/psilocin. For many of the analytes, sensitive immunologic methods for screening are available. Gas chromatography-mass spectrometry (GC-MS) is still the state-of-the-art method for confirmatory analysis or for screening and confirmation in one step. Liquid chromatography-mass spectrometry (LC-MS) procedures for such purposes are also included in this review. Basic data about the biosample assayed, internal standard, workup, GC or LC column and mobile phase, detection mode, reference data, and validation data of each procedure are summarized in two tables.
Design of a wideband Rotman lens with dummy ports for wide-scan phased array applications

A wide-band wide-scan phased array is investigated by utilizing the beamforming network that is based on a microstrip Rotman lens. A wide beam scanning capability of ±40° for the frequency range 8-12 GHz is demonstrated. The most crucial part is to design a wide beam scanning rotman lens in conjunction-with a good impedance matching over the entire frequency band. To do so, dummy ports are placed between inputs ports and output ports of Rotman lens as mutual coupling between ports are reduced significantly which results in improving the performance of Rotman lens.
Real-Time QCM-D Monitoring of Deposition of Gold Nanorods on a Supported Lipid Bilayer as a Model Cell Membrane

Gold nanorods (GNRs) exhibit promising potentials in therapeutics and diagnostics. A quartz crystal microbalance with dissipation (QCM-D) monitoring was used to study the influence of size and solution chemistry on the deposition of GNRs on a model cell membrane. Supported lipid bilayers (SLBs) comprising zwitterionic 1,2-dioleoyl-sn-glycero-3-phosphocholine were used as the model cell membrane. QCM-D results showed that the deposition mass of GNRs on SLBs increased with increasing GNR concentration from 1 to 50 μM. The GNRs with smaller size exhibited a higher deposition mass in comparison with the larger one when they had a similar zeta potential. Additionally, pH also influenced the deposition behavior of GNRs on SLBs because of the change in their electrostatic interaction. Under neutral pH conditions, the deposition masses of GNRs on SLBs increased with increasing concentrations of NaCl, which is probably attributed to the reduced electrostatic repulsive force. However, the deposition mass of GNRs de.
Petuum: A Framework for Iterative-Convergent Distributed ML

A major bottleneck to applying advanced ML programs at industrial scales is the migration of an academic implementation, often specialized for a small, well-controlled computer platform such as desktop PCs and small lab-clusters, to a big, less predicable platform such as a corporate cluster or the cloud. This poses enormous challenges: how does one train huge models with billions of parameters on massive data, especially when substantial expertise is required to handle many low-level systems issues? We propose a new architecture of systems components that systematically addresses these challenges, thus providing a general-purpose distributed platform for Big Machine Learning. Our architecture specifically exploits the fact that many ML programs are fundamentally loss function minimization problems, and that their iterative-convergent nature presents many unique opportunities to minimize loss, such as via dynamic variable scheduling and error-bounded consistency models for synchronization. Thus, we treat data, parameter and variable blocks as computing units to be dynamically scheduled and updated in an error-bounded manner, with the goal of minimizing the loss function as quickly as possible.
Long-Term Cardiac-Targeted RNA Interference for the Treatment of Heart Failure Restores Cardiac Function and Reduces Pathological Hypertrophy

Background— RNA interference (RNAi) has the potential to be a novel therapeutic strategy in diverse areas of medicine. Here, we report on targeted RNAi for the treatment of heart failure, an important disorder in humans that results from multiple causes. Successful treatment of heart failure is demonstrated in a rat model of transaortic banding by RNAi targeting of phospholamban, a key regulator of cardiac Ca2+ homeostasis. Whereas gene therapy rests on recombinant protein expression as its basic principle, RNAi therapy uses regulatory RNAs to achieve its effect. Methods and Results— We describe structural requirements to obtain high RNAi activity from adenoviral and adeno-associated virus (AAV9) vectors and show that an adenoviral short hairpin RNA vector (AdV-shRNA) silenced phospholamban in cardiomyocytes (primary neonatal rat cardiomyocytes) and improved hemodynamics in heart-failure rats 1 month after aortic root injection. For simplified long-term therapy, we developed a dimeric cardiotropic adeno-associated virus vector (rAAV9-shPLB) to deliver RNAi activity to the heart via intravenous injection. Cardiac phospholamban protein was reduced to 25%, and suppression of sacroplasmic reticulum Ca2+ ATPase in the HF groups was rescued. In contrast to traditional vectors, rAAV9 showed high affinity for myocardium but low affinity for liver and other organs. rAAV9-shPLB therapy restored diastolic (left ventricular end-diastolic pressure, dp/dtmin, and &tgr;) and systolic (fractional shortening) functional parameters to normal ranges. The massive cardiac dilation was normalized, and cardiac hypertrophy, cardiomyocyte diameter, and cardiac fibrosis were reduced significantly. Importantly, no evidence was found of microRNA deregulation or hepatotoxicity during these RNAi therapies. Conclusions— Our data show for the first time the high efficacy of an RNAi therapeutic strategy in a cardiac disease.
Natural variation identifies genes affecting drought-induced abscisic acid accumulation in Arabidopsis thaliana

Significance Abscisic acid (ABA) accumulates to high levels during drought stress, and ABA levels, in turn, control many downstream responses that determine plant growth, productivity, and survival during drought. Despite the central importance of ABA, we know little of how drought stress elicits ABA accumulation, how different aspects of ABA metabolism are coordinated to match the level of ABA to stress severity, or how variation in ABA accumulation may contribute to environmental adaptation. When exposed to an equal severity of water limitation, Arabidopsis thaliana accessions exhibited wide variation in their ABA content. These results allowed us to identify effectors of ABA accumulation and also indicated that variation in ABA accumulation may contribute to variation in other drought-related traits. Accumulation of the stress hormone abscisic acid (ABA) in response to drought and low water-potential controls many downstream acclimation mechanisms. However, mechanisms controlling ABA accumulation itself are less known. There was a 10-fold range of variation in ABA levels among nearly 300 Arabidopsis thaliana accessions exposed to the same low water-potential severity. Genome-wide association analysis (GWAS) identified genomic regions containing clusters of ABA-associated SNPs. Candidate genes within these regions included few genes with known stress or ABA-related function. The GWAS data were used to guide reverse genetic analysis, which found effectors of ABA accumulation. These included plasma-membrane–localized signaling proteins such as receptor-like kinases, aspartic protease, a putative lipid-binding START domain protein, and other membrane proteins of unknown function as well as a RING U-box protein and possible effect of tonoplast transport on ABA accumulation. Putative loss-of-function polymorphisms within the START domain protein were associated with climate factors at accession sites of origin, indicating its potential involvement in drought adaptation. Overall, using ABA accumulation as a basis for a combined GWAS–reverse genetic strategy revealed the broad natural variation in low-water-potential–induced ABA accumulation and was successful in identifying genes that affect ABA levels and may act in upstream drought-related sensing and signaling mechanisms. ABA effector loci were identified even when each one was of incremental effect, consistent with control of ABA accumulation being distributed among the many branches of ABA metabolism or mediated by genes with partially redundant function.
Segmentation of Factories on Electricity Consumption Behaviors Using Load Profile Data

In recent years, the new achievements in the field of technology and data science allowed to gather detailed and well-structured information about electricity consumption behaviors of industrial enterprises. Such type of information can find numerous applications in the power distribution industry. The utilities often use the data from contracts to assign each industrial customer a class label according to this type defined in predetermined industry segmentation. Such type of fixed-chart segmentation is not able to satisfy the needs of modern enterprises for the flexible and dynamic determination of production modes. In this paper, we address this problem by proposing a new method for the segmentation of various types of factories based on their electricity consumption patterns represented in load profile data. It exploits the evolution-based characteristics of smart meter data of multiple types of factories to remove irrelevant features. We use data visualization to estimate the number of clusters and apply the well-known k-means algorithm on filtered data to generate segmentation. Experimental results on real load profile data collected with smart meters from manufacturing industries in Guangdong province of China have shown that the new clustering approach produced the meaningful segmentation of factories that reflect production operations.
Using the Personality Assessment Inventory to Discriminate among Borderline Personality Disorder, Bipolar Disorder, and Post-traumatic Stress Disorder

I am submitting herewith a dissertation written by Shannon Dleen Mullen-Magbalon entitled “Using the Personality Assessment Inventory to Discriminate among Borderline Personality Disorder, Bipolar Disorder, and Post-traumatic Stress Disorder. ” I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Psychology.
Decomposition methods for large scale LP decoding

Feldman et al. (IEEE Trans. Inform. Theory, Mar. 2005) showed that linear programming (LP) can be used to decode linear error correcting codes. The bit-error-rate performance of LP decoding is comparable to state-of-the-art BP decoders, but has significantly stronger theoretical guarantees. However, LP decoding when implemented with standard LP solvers does not easily scale to the block lengths of modern error correcting codes. In this paper we draw on decomposition methods from optimization theory to develop efficient distributed algorithms for LP decoding. The key enabling technical result is a nearly linear time algorithm for two-norm projection onto the parity polytope. This allows us to use LP decoding, with all its theoretical guarantees, to decode large-scale error correcting codes efficiently.
Broadcast-free collection protocol

Asynchronous low-power listening techniques reduce the energy footprint of radio communication by enforcing link layer duty cycling. At the same time, these techniques make broadcast traffic significantly more expensive than unicast traffic. Because broadcast is a key network primitive and is widely used in various protocols, recently several techniques have been proposed to reduce the amount of broadcast activity by merging broadcasts from different protocols. In this paper we focus on collection protocols and investigate the more extreme approach of eliminating broadcast completely. To this end, we design, implement and, evaluate a Broadcast-Free Collection Protocol, BFC. We derive first-order models to quantify the costs of broadcasts, and evaluate the performance of BFC on a public testbed. Compared to the Collection Tree Protocol, the de facto standard for data collection, BFC achieves double-digit percentage improvements on the duty cycles. The specific benefits to individual nodes depend on the relative cost of unicast activity; we show that the nodes that benefit the most are the sink's neighbors, which are crucial for network lifetime extension. Eliminating broadcast also brings several other advantages, including extra flexibility with link layer calibrations and energy savings in the presence of poor connectivity.
Snubber design for transformer protection

Historically, failures of distribution transformers due to transient overvoltage phenomena has led to the development of Resistor-Capacitor snubber circuits for the protection of the transformer and winding insulation. These transients are most often observed when dry-type transformers are close coupled to vacuum switching devices. Typically, snubber circuit design has been specific for the particular application, and complex engineering studies were required. However, the design of a snubber circuit itself may be performed without these detailed studies. If snubber studies are required, the procedures for system modeling and simulation are explained in a step by step manner.
Using Paper-Folding in the Primary Years to Promote Student Engagement in Mathematical Learning

Traditionally, craft activities have been incorporated into early childhood mathematical learning experiences as an effective means of fostering curiosity and interest, and introducing abstract mathematical concepts through the use of concrete materials. However, in the primary schooling years creative and active mathematical learning experiences often give way to approaches such as computational drills and rote learning which are less than inspiring for students and often do not result in meaningful understanding. This paper shows that craft activities, in particular paper-folding, can also be valuable in the primary years as a means of promoting affective, behavioural and cognitive engagement in the mathematical learning. In the primary classroom, the inclusion of paper-folding activities in mathematics can offer an appealing and creative means of addressing some of the goals for teaching and learning mathematics in the 21 st century as identified by English (2002), namely: engaging students effectively in mathematical modelling, visualising, algebraic thinking and problem-solving. Paper-folding activities can provide a hands-on, active experience that contributes to the development of mathematical ideas, thinking and concepts; skills in communicating mathematically; and group interaction skills. The particular focus of this study is to investigate how paperfolding in primary mathematics can also be used to promote affective, behavioural and cognitive engagement in mathematical learning.
Sexual, seasonal, and environmental variation in plasma carotenoids in great tits, Parus major.

In many birds, carotenoids have dual functions as irreversible plumage pigments and as physiologically essential vitamins and antioxidants. They must be obtained through the diet and may therefore be a limiting resource, a constraint that is likely to vary with factors such as sex, habitat, and time of year. In the present study, we investigated signs of carotenoid limitation in great tits, Parus major, in relation to sex, season, year, and within an urban versus a rural habitat. The two main carotenoids, lutein and zeaxanthin, were analysed by high-performance liquid chromatography in the plasma and in the yellow carotenoid-based breast feathers. We found that plasma carotenoid concentrations were significantly influenced by sex, season, and year, but not by urban versus rural habitat. At moult, plasma concentration was positively correlated with feather pigmentation, independent of body condition and sex. During the breeding season, however, circulating carotenoid concentrations were negatively related to the feather pigmentation (i.e. from previous autumn moult). We suggest that great tits are carotenoid deprived before leaf emergence, and that carotenoid utilization and limitations are sex-specific, but that there are neither any obvious honesty-maintaining costs of pigmentation, nor any fitness consequences of the colour variation. © 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 521–527.