Miracle-dz/UniFAR-FTUs huggingface dataset:
Lorentzian-Model-Based Bayesian Analysis for Automated Estimation of Attenuated Resonance Spectrum

Extracting information from a signal exhibiting damped resonances is a challenging task in many practical cases due to the presence of noise and high attenuation. The interpretation of the signal relies on a model whose order (i.e., the number of resonances) is in general unknown. In this study, the signal is modeled as a sum of Lorentzian lineshapes, and a Bayesian framework is designed to simultaneously remove the baseline distortion, select the number of resonances, and recover the parameters of each lineshape including frequency, damping factor, resonance amplitude, and noise magnitude. The Bayesian problem is solved resorting to a reversible jump Markov chain Monte Carlo (RJ-MCMC) sampling scheme. The algorithm is tested on synthetic signals as well as experimental data from a resonant ultrasound spectroscopy experiment aiming to measure elastic properties. The results show that, compared to the well-known linear prediction singular value decomposition method, the RJ-MCMC method achieves a better performance with the advantages of joint model selection, high accuracy estimation, and uncertainty evaluation. We found that when the signal-to-noise-ratio is larger than 20 dB, the average relative error for frequency extraction is smaller than $\text{0.5}\%$. Such an algorithm enables to estimate the number of resonances and extract tens of resonance parameters from a highly attenuated spectrum, which can significantly facilitate the automated processing of signals exhibiting damped resonances.
Anole: A Highly Efficient Dynamically Reconfigurable Crypto-Processor for Symmetric-Key Algorithms

This paper presents a dynamically reconfigurable processing array named Anole for symmetric-key algorithms. Processing elements and the interconnections between them are designed to support various block and stream ciphers. Without affecting flexibility, three key techniques are presented to increase energy efficiency (throughput/power, the number of operations per unit energy consumption) and area efficiency (throughput/area). First, the distributed control network supports multithreading on reconfigurable fabrics at a low cost, thereby maximizing the utility of computing resources in the space domain. Second, the concurrent computation and reconfiguration scheme integrates configuration contexts with processing data to simultaneously execute in the data-path. The resulted immediate switching between different configurations increases the utilization rate of hardware resources in the temporal domain. Third, under configuration context compression and organization, the context memory size and configuration time are further minimized. Anole is implemented on a 7.75 mm2 silicon square with TSMC 65-nm technology at 400 MHz. Experiments show that Anole significantly outperforms field programmable gate array and general purpose processor by more than two orders of magnitude in energy and area efficiencies. Compared with state-of-the-art reconfigurable solutions, Anole achieves (average) $16.5\boldsymbol {\times }$ higher energy efficiency and $9.4\boldsymbol {\times }$ higher area efficiency.
Facilitating Data-Centric Recommendation in Knowledge Graph

In order to help Earth scientists share knowledge and expedite scientific exploration, NASA is developing NASA Earth Science Enterprise (ESE) whose underlying basis is NASA Science Knowledge Graph (SKG). This paper focuses on the information model design of the SKG, where entity-and relationship-encapsulated features are extracted as first-class citizens in SKG. The rationale is that typological structural analysis can thus be exploited for runtime knowledge extraction, discovery, and prediction, in addition to semantic analysis. Based on the information model, a knowledge discovery technique is equipped to answer queries and provide personalized recommendation based upon the SKG. Deep learning techniques, i.e., Stacked Denoising Auto-Encoders (SDAE) and Translating Embeddings (TransE), are applied to detect structural similarity and explore paths at runtime, respectively, supporting higher scalability and performance. Experiments on a science-oriented testbed serves as a proof of concept and demonstrates the feasibility and effectiveness of the techniques.
Estimating the impact of administration of dewormers on smallholder chickens in Odisha State, India

ABSTRACT Helminth infections, in particular infections with nematodes are highly prevalent and an impediment to the productivity of chickens in smallholder settings. Infections can be easily and cheaply treated using dewormers. We present an empirical framework for estimating the impact of administration of locally available dewormers on chicken weight in a smallholder setting in Odisha State of India. We recruited 1,040 chickens aged between 40 and 70 d from 168 households in 13 village groups in Odisha. Chickens were randomly assigned to treatment with a dewormer (fenbendazole), or non‐treatment. Each chicken was tagged with 2 legbands and weighed, then followed up after 28 and 56 d and reweighed. To account for the local variations in exposure and for variations between flocks, the data were analyzed in a multilevel mixed model with flock within village as nested random effects. After 56 d, the modeled results showed that all chickens had gained a mean of 288.3 g but heavier chickens at the baseline gained more weight than lighter chickens. In addition to this, the treated chickens had gained an additional mean of 90.55 g relative to non‐treated chickens (P < 0.001). In this setting, we have demonstrated that administration of dewormers has a clear beneficial impact on chicken weight, but it also indicates that other management practices can have a substantial impact on chicken weight.
Upper Limb Deep Vein Thrombosis: A Case Report of an Increasingly Common Condition

Upper limb deep vein thrombosis (DVT) is a less common phenomenon than lower limb DVT. Repeated trauma secondary to sport- or job-related arm movements and positions has been recognized as the predisposing factor for upper limb DVT. We describe a 38-year-old male computer programmer admitted with swelling and pain in his left upper limb. Venous duplex ultrasound confirmed the presence of axillary vein thrombosis. Coagulation studies for secondary thrombosis were unremarkable. The patient was treated with full anticoagulation using low molecular weight heparin and warfarin. On subsequent follow-up at 3 months, the patient was symptom free and duplex sonography showed no evidence of thrombosis.
An Optimized Lesk-Based Algorithm for Word Sense Disambiguation

Abstract Computational complexity is a characteristic of almost all Lesk-based algorithms for word sense disambiguation (WSD). In this paper, we address this issue by developing a simple and optimized variant of the algorithm using topic composition in documents based on the theory underlying topic models. The knowledge resource adopted is the English WordNet enriched with linguistic knowledge from Wikipedia and Semcor corpus. Besides the algorithm’s eficiency, we also evaluate its efectiveness using two datasets; a general domain dataset and domain-specific dataset. The algorithm achieves a superior performance on the general domain dataset and superior performance for knowledge-based techniques on the domain-specific dataset.
Reliability and Stability on the Numerical Analysis in Structural Equation Modeling

Structural equation modeling has been widely used in various research studies and has been incorporated into many software programs such as Mplus, CALIS, EQS, LISREL, Amos and so on. At the meantime, the diversity, instability of the analysis results or no solutions has been found in some analysis models. Several problems on the numerical analysis such as the constraints of residual variance, initial values and the different solutions by software in use have also been found in SEM analysis. This paper discusses the reliability and stability on the numerical analysis in structural equation modeling by using four kinds of SEM programs and three kinds of sample data. The study will introduce an application of the optimized calculation of genetic algorithms (GA) in structural equation modeling in order to see elaborately what is going on with these issues and also to examine the goodness-of-fit, validity, stability and reliability of structural model. Furthermore, the empirical analysis is presented to discuss the above issue in the questionnaire survey data about drunken driving behavior in Bangkok.
Tracking mood fluctuations with functional network patterns

&NA; Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the ‘MyConnectome Project’, we construct a comprehensive mapping between resting‐state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting‐state networks. Finally, one of the top‐ranked nodes in mood‐related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual‐focused data sets for affective neuroscience.
ATINER ' s Conference Paper Series COM 2018-2514

Virtual 3D worlds are not only used for the visualization of complex learning matter, but get increasing importance in learning environments. Students for example act as avatars in an artificially generated world, in which they learn, develop, and present similarly. However, in our 3D lectures some of the learners suffered from motion sickness, an affect that is already known in computer games and similar uses. For example, in our project ‘Software Explorer’ we use a virtual 3D world for visualizing complex software structures. When working in the 3D room, the students are asked to do some tasks like searching classes or look for the connections between packages. This is done by pointing to items with a ‘laser pointer’, or by moving through the virtual world. In concluding evaluations, some students felt some indisposition – like the real motion sickness in a car or on a ship; a few students even had to pause acting in the virtual room. In our project ‘Kinetosis’ (motion sickness) we will examine the causes and the indications of a beginning motion sickness. In order to identify the triggers, we implement appropriate 3D worlds as test settings, for example buildings, computer games, or a virtual rollercoaster. In these settings, different parameters are tested, for example by adjusting frame rate, speed, acceleration, rotation, etc. We then measure resulting effects in the person ́s movement (via motion capture, video), eye movement (via eye tracking), body parameters (sweat, temperature, heart rate .), etc. We hope to confirm and extend our hypotheses of how to minimize the user ́s motion sickness like behavioural changes of the person (e.g. ‘don ́t go backwards in a 3D room’) or technical changes of the environment (e.g. adapting the frame rate, visual and acoustic diversion, tools for controlled motion like teleport .).
Dynamic Over-Voltage Operation of a Discrete-Level Supply-Modulated GaN-based RF PA

In this paper an investigation for increasing the peak output power of a GaN-based RF power amplifier (PA) by dynamically operating it at a higher supply voltage is presented. The PA is operated with a high peak-to-average power ratio signal with 100 MHz instantaneous modulation bandwidth and discrete-level (class-G) supply modulation. For the peak power levels the supply voltage is switched by the class-G modulator from 40 to 60 V. This increases the peak output power by 1 dB but has a very low negative impact, since the high-voltage pulses are too short to cause self-heating in the GaN-HEMT device. Modulated measurements with digital predistortion linearization also show a 1 dB increase in average output power without linearity degradation. With this over-voltage operation, the peak output power density of the GaN HEMT can be increased from 4.9 W/mm to 6.4 W/mm.
Crack growth in heterogeneous brittle solids: intermittency, crackling and induced seismicity

Crack growth is the basic mechanism leading to the failure of brittle materials. Engineering addresses this problem within the framework of continuum mechanics, which links deterministically the crack motion to the applied loading. Such an idealization, however, fails in several situations and in particular cannot capture the highly erratic (earthquake-like) dynamics sometimes observed in slowly fracturing heterogeneous solids. Here, we examine this problem by means of innovative experiments of crack growth in artificial rocks of controlled microstructure. The dynamical events are analysed at both global and local scales, from the time fluctuation of the spatially averaged crack speed and the induced acoustic emission, respectively. Their statistics are characterized and compared with the predictions of a recent approach mapping fracture onset to the depinning of an elastic interface. Finally, the overall time–size organization of the events is characterized to shed light on the mechanisms underlying the scaling laws observed in seismology. This article is part of the theme issue ‘Statistical physics of fracture and earthquakes’.
Optimal and Adaptive Signal Processing

Designed for undergraduate and postgraduate students in electrical engineering, as well as for practising scientists and engineers, this text covers the theory of optimal and adaptive signal processing using examples and computer simulations drawn from a wide range of applications including speech and audio communications, reflection seismology and sonar systems. The material is presented without a heavy reliance on mathematics, and focuses on one-dimensional and array procession results, as well as a wide range of adaptive filtre algorithms and implementations. Topics discussed include random signals and optimal processing, adaptive signal processing with the LMS algorithm, applications of adaptive filtering, algorithm and structures for adaptive filtering, spectral analysis and array signal processing.
IntegrationDistiller: Automating Integration Analysis and Testing of Object-Oriented Applications

Software systems typically consist of various interacting components and units. While these components can be tested and shown to work correctly in isolation, when integrated and start interacting with each other, they may fail to produce the desired behaviors and results. Integration testing plays an important role in revealing issues in interactions among cooperating components. Identifying different interaction scenarios, however, is not a trivial task when performing integration testing. On the other hand, most of the integration testing solutions proposed in the literature are manual which hinders their scalability and applicability when it comes to large industrial systems. In this paper we introduce IntegrationDistiller as an automated solution and tool to identify integration scenarios and generate test cases (in the form of method call sequences) for . NET applications. It works by analyzing the code and automatically identifying class couplings, interacting methods, as well as invocation points. Moreover, the tool also helps and supports testers in identifying timing issues at integration level by automatic code instrumentation at invocation points. The code analysis engine of IntegrationDistiller is built and automated using . NET compiler platform, known as Roslyn. Hence, this work is the first in utilizing Roslyn features for automatic integration analysis and integration test case generation. This work has been done as part of our collaboration with ABB Industrial Automation Control Technologies (IACT) in Västerås-Sweden to address the integration testing challenges of the software part of the ABB Ability™ 800xA distributed control systems.
Extreme Geochemical Conditions and Dispersal Limitation Retard Primary Succession of Microbial Communities in Gold Tailings

Microbial community succession in tailings materials is poorly understood at present, and likely to be substantially different from similar processes in natural primary successional environments due to the unusual geochemical properties of tailings and the isolated design of tailings storage facilities. This is the first study to evaluate processes of primary succession in microbial communities colonizing unamended tailings, and compare the relative importance of stochastic (predominantly dust-borne dispersal) and deterministic (strong selection pressures from extreme geochemical properties) processes in governing community assembly rates and trajectories to those observed in natural environments. Dispersal-based recruitment required > 6 months to shift microbial community composition in unamended, field-weathered gold tailings; and in the absence of targeted inoculants, recruitment was dominated by salt- and alkali-tolerant species. In addition, cell numbers were less than 106 cells/g tailings until > 6 months after deposition. Laboratory experiments simulating microbial cell addition via dust revealed that high (>6 months’ equivalent) dust addition rates were required to effect stabilization of microbial cell counts in tailings. In field-weathered tailings, topsoil addition during rehabilitation works exerted a double effect, acting as a microbial inoculant and correcting geochemical properties of tailings. However, microbial communities in rehabilitated tailings remained compositionally distinct from those of reference soils in surrounding environments. pH, water extractable Mg, and water extractable Fe emerged as major controls on microbial community composition in the field-weathered gold tailings. Overall, this study highlights the need for application of targeted microbial inoculants to accelerate rates of microbial community succession in tailings, which are limited primarily by slow dispersal due to physical and spatial isolation of tailings facilities from inoculant sources; and for geochemical properties of tailings to be amended to moderate values to encourage microbial community diversification and succession.
A Compact Ciphertext-Policy Attribute-Based Encryption Scheme for the Information-Centric Internet of Things

The information-centric Internet of things (IC-IoT) is different from the traditional Internet of things (IoT) in that the device-to-device pattern is generalized to a device-to-network pattern. Furthermore, in an IC-IoT environment, there is a demand for protecting the security of all data generated from IC-IoT devices. A cryptography scheme named attribute-based encryption (ABE) represents a smart method of providing the fine-grained access control that can sufficiently protect data security. The most attractive advantage of ABE is its expressive access policy, which makes the access control of data flexible and manageable. However, there is a serious problem caused by such an access policy; it incurs a greater ciphertext redundancy and computational overhead. This implies that the current ABE scheme is hard to implement in the thin client devices of IC-IoT. In this paper, we propose a universalized policy-compacting method via sharing public parts of the policy. Compared with the original policy, the compacted policy applies a more compact ciphertext and requires less computation, communication, and storage cost. However, the policy-compacting problem is proved to be a non-deterministic polynomial complete (NPC) problem. Thus, a greedy algorithm is provided to obtain an approximate minimum compacted policy scale. Finally, we propose a compact ciphertext-policy attribute-based encryption (CCP-ABE) scheme with the policy-compacting method. A security proof and performance evaluation show that the proposed CCP-ABE scheme provides a comprehensive performance improvement.
Pathology attributed to Mycobacterium chelonae infection among farmed and laboratory-infected Atlantic salmon

This study was promoted following concern over increasing mortality on 2 farms rearing Atlantic salmon Salmo salar in the Shetland Isles, Scotland. A Mycobacterium sp. was isolated from monbund, market-s~zed Atlantic salmon. Biochemical tests, 1ipi.d analys~s and PCR (polynlerase chain reaction) techniques confirmed the bacterium to be Mycobacterium chelonae. Multiple greyish-white miliary granuloma-like nodules were observed in several tissues. Dense hard-packed nodules contained abundant acid-fast bacteria. Atlantic salmon injected with M. chelonae remained sub-clinically infected, demonstrating the chronic nature of this disease. The source of the pathogen was not identified.
Antiepileptic Drug of Levetiracetam Decreases Centrotemporal Spike-Associated Activation in Rolandic Epilepsy

The objective was to study the modulation effects of levetiracetam on the fMRI activation/deactivation patterns associated with centrotemporal spikes (CTS) in Rolandic epilepsy. Forty patients with Rolandic epilepsy, including levetiracetam-medicated patients (n = 20) and drug-naive patients (n = 20), were studied. Single and sequential hemodynamic response functions-based EEG-fMRI analysis was performed to detect dynamic activation/deactivation associated with CTS. Comparisons of spatiotemporal features of activation/deactivation were performed between the two groups. Both the groups (CTS were detected in 12 cases of levetiracetam-medicated group, and 11 cases of drug-naive group) showed CTS-associated activation in the Rolandic cortex, whereas activation strength, time-to-peak delay, and overall activation were diminished in the levetiracetam-medicated group. Moreover, the drug-naive group showed deactivation in the regions engaged in higher cognition networks compared with the levetiracetam-medicated group. Levetiracetam inhibits CTS-associated activation intensity and alters the temporal pattern of this activation in the epileptogenic regions, and it also affects the brain deactivation related to higher cognition networks. The findings sheds a light on the pharmocological mechanism of levetiracetam therapy on Rolandic epilepsy.
Spirometry Use in Patients Prescribed Albuterol

Background: Previous studies have shown that spirometry is obtained in only about a third of patients with chronic obstructive pulmonary disease (COPD) in primary care practice. This study evaluated spirometry use in persons prescribed an albuterol inhaler in the primary care clinics at a Veterans Administration (VA) hospital. Methods: One hundred ninety-seven patients prescribed albuterol were reviewed for age, education level of the primary care practioners, other respiratory medications and diagnosis. Results: The average age was 63.2 years (SD, 11.5), and 93% of patients were male. Obtaining spirometry was not age or sex-dependent but became more frequent with the use of tiotropium (72.2%), long-acting beta agonists (71.8%), ipratropium (69.4%) or inhaled corticosteroids (63.5%) compared to albuterol alone (39.4%, p=0.0007). Eighty of the patients had a diagnosis of COPD (40.6%), 40 a diagnosis of asthma (20.3%), 23 other respiratory diagnoses (11.7%) but 54 (27.4%) had no respiratory diagnosis. Patients diagnosed with COPD were more likely to have spirometry performed (71.2%) than patients diagnosed with asthma (35%), other respiratory diagnosis (34.7%) or no respiratory diagnosis (40.7%) (p=0.00068). Conclusions: The above data demonstrate that spirometry is more frequently used in patients with COPD than previously reported and increases when additional medications are added to albuterol.