Miracle-dz/UniFAR-FTUs huggingface dataset:
Sweetening the Dataset : Using Active Learning to Label Unlabelled Datasets

Supervised machine learning approaches assume the existence of a large collection of manually labelled examples of the problem under consideration. However, in many cases such a collection does not exist and creating one is time consuming and expensive. This can be a barrier to the use of supervised learning in certain situations, particularly when the doubt as to whether the system will work or not makes the cost of creating a dataset unjustifiable. Active learning is a machine learning technique that has been used widely to create classification systems in the absence of large numbers of labelled examples, but that can also be used to create such collections. This paper will describe a system that uses active learning to label large collections of unlabelled data. We will show that the system can create an accurately labelled dataset approximately 10 times the size of the set of examples manually labelled by an expert. The experiments described are based on recipe data from the 1st Computer Cooking Contest to be held at ECCBR’08 and focus on identifying those recipes in the set that are desserts.
Evolving Mechanistic Concepts of Epileptiform Synchronization and their Relevance in Curing Focal Epileptic Disorders

The synchronized activity of neuronal networks under physiological conditions is mirrored by specific oscillatory patterns of the EEG that are associated with different behavioral states and cognitive functions. Excessive synchronization can, however, lead to focal epileptiform activity characterized by interictal and ictal discharges in epileptic patients and animal models. This review focusses on studies that have addressed epileptiform synchronization in temporal lobe regions by em-ploying in vitro and in vivo recording techniques. First, we consider the role of ionotropic and metabotropic excitatory glu-tamatergic transmission in seizure generation as well as the paradoxical role of GABAA signaling in initiating and perhaps maintaining focal seizure activity. Second, we address non-synaptic mechanisms (which include voltage-gated ionic currents and gap junctions) in the generation of epileptiform synchronization. For each mechanism, we discuss the actions of anti-epileptic drugs that are presumably modulating excitatory or inhibitory signaling and voltage-gated currents to prevent sei-zures in epileptic patients. These findings provide insights into the mechanisms of seizure initiation and maintenance, thus leading to the development of specific pharmacological treatments for focal epileptic disorders.
Oncological hyperthermia: The correct dosing in clinical applications

The problem with the application of conventional hyperthermia in oncology is firmly connected to the dose definition, which conventionally uses the concept of the homogeneous (isothermal) temperature of the target. Its imprecise control and complex evaluation is the primary barrier to the extensive clinical applications. The aim of this study was to show the basis of the problems of the misleading dose concept. A clear clarification of the proper dose concept must begin with the description of the limitations of the present doses in conventional hyperthermia applications. The surmounting of the limits the dose of oncologic hyperthermia has to be based on the applicability of the Eyring transition state theory on thermal effects. In order to avoid the countereffects of thermal homeostasis, the use of precise heating on the nanoscale with highly efficient energy delivery is recommended. The nano-scale heating allows for an energy-based dose to control the process. The main aspects of the method are the following: i) It is not isothermal (no homogeneous heating); ii) malignant cells are heated selectively; and iii) it employs high heating efficacy, with less energy loss. The applied rigorous thermodynamical considerations show the proper terminology and dose concept of hyperthermia, which is based on the energy-absorption (such as in the case of ionizing radiation) instead of the temperature-based ideas. On the whole, according to the present study, the appropriate dose in oncological hyperthermia must use an energy-based concept, as it is well-known in all the ionizing radiation therapies. We propose the use of Gy (J/kg) in cases of non-ionizing radiation (hyperthermia) as well.
High-Purity and Wide-Range Signal Generator for Bioimpedance Spectroscopy

This brief presents an integrated high-purity current signal generator (SG), part of a bioimpedance spectroscopy system that performs measurements in the frequency range from 1 kHz to 2 MHz, and it is able to measure bioimpedance values from 100 <inline-formula> <tex-math notation="LaTeX">${ \Omega }$ </tex-math></inline-formula> to 1 <inline-formula> <tex-math notation="LaTeX">$\text{M}{ \Omega }$ </tex-math></inline-formula>. The SG is implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{m}$ </tex-math></inline-formula> CMOS process, it is powered by a single 1.8 V voltage source, and occupies a total area of 1.62 mm<sup>2</sup>. It is able to generate single-frequency signals from 1 kHz to 2 MHz in 12 steps logarithmically spaced. High signal purity is achieved by using a second-order low-pass filter, with a bandwidth that can be programmed from 4 kHz to 8 MHz, in 12 points logarithmically spaced. The SG’s power consumption varies from 750 <inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{W}$ </tex-math></inline-formula>, at the lowest frequencies and gain, to 2.06 mW at the highest frequencies and gain. The output current levels can be modified from 130 nA up to 10 <inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{A}$ </tex-math></inline-formula> in five programmable steps (9.5 dB per step). The SG achieves a spurious-free dynamic range larger than 40 dB while covering almost three decades in frequency. Such performance enables measurements with errors below 1%, as it is required for accurate bioimpedance measurements in many medical applications.
Chronic Pain Evaluation: A Valid, Standardized Assessment Instrument

Development of the Pain Assessment Instrument Uses for the Pain Assessment Instrument Scientific Validation of the Pain Assessment Instrument Content of the Pain Assessment Instrument Administration and Utilization of the Pain Assessment Instrument Predicting Subsequent Employment Status of SSA Disability Applicants with Chronic Pain Standardization of Chronic Pain Assessment: A Multiperspective Approach References Additional Reading Glossary of Abbreviations Appendix 1 - Pain Screening Instrument Appendix 2 - Patient Assessment Appendix 3 - Physician Assessment Appendix 4 - Integrated Pain Report
European Perspective on 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: Proceedings of an Interactive International Symposium.

BACKGROUND The American Thyroid Association (ATA) management guidelines for patients with thyroid nodules and differentiated thyroid cancer (DTC) are highly influential practice recommendations. The latest revision appeared in 2015 ("ATA 2015"). These guidelines were developed predominantly by North American experts. European experts frequently have different perspectives, given epidemiological, technological/methodological, practice organization, and medicolegal differences between the respective regions. SUMMARY Divergent viewpoints were the focus of an invited symposium organized by the European Association of Nuclear Medicine involving 17 European thyroidologists, four ATA Guidelines Taskforce members, and an audience of 200 international experts. The group discussed the preoperative assessment of thyroid nodules, surgery and the role of pathology, radioiodine (RAI) therapy (RAIT), the assessment of initial therapy and dynamic risk stratification, and the treatment of persistent disease, recurrences, and advanced thyroid cancer. The dialogue resulted in this position paper contrasting European and ATA 2015 perspectives on key issues. One difference pertains to the permissiveness of ATA 2015 regarding lobectomy for primary tumors ≤4 cm. European panelists cited preclusion of RAIT, potential need for completion thyroidectomy, frequent inability to avoid chronic thyroid hormone replacement, and limitations of supportive evidence as arguments against widely applying lobectomy. Significant divergence involved ATA 2015's guidance regarding RAIT. European panelists favored wider use of postoperative RAIT than does ATA 2015. Rationales included the modality's association with favorable patient outcomes and generally limited toxicity, and lack of high-quality evidence supporting withholding RAIT. Additionally, European panelists favored recombinant human thyrotropin (rhTSH) in more settings than does ATA 2015, citing avoidance of hypothyroid morbidity and quality-of-life impairment, without apparent sacrifice in oncologic outcomes. Based on clinical evidence plus theoretical advantages, European experts advocated dosimetric versus fixed-activity RAIT approaches for advanced DTC. European panelists noted that the ATA 2015 risk-stratification system requires information sometimes unavailable in everyday practice. ATA 2015 recommendations regarding RAI-refractory DTC should consider potential palliative benefits of RAIT in patients who also have RAI-susceptible lesions. CONCLUSIONS European panelists suggested modifications to approximately one-third of ATA 2015 recommendations. Varying European and ATA 2015 perspectives can stimulate analysis and discussion of the literature and performance of primary research to resolve discrepant recommendations and potentially improve patient outcomes.
Similarity measures for semantic relation extraction

Semantic relations, such as synonyms, hypernyms and co-hyponyms proved to be useful for text processing applications, including text similarity, query expansion, question answering and word sense disambiguation. Such relations are practical because of the gap between lexical surface of the text and its meaning. Indeed, the same concept is often represented by different terms. However, existing resources often do not cover a vocabulary required by a given system. Manual resource construction is prohibitively expensive for many projects. On the other hand, precision of the existing extractors still do not meet quality of the hand-crafted resources. All these factors motivate the development of novel extraction methods. This thesis deals with similarity measures for semantic relation extraction. The main research question we address, is how to improve precision and coverage of such measures. First, we perform a large-scale study the baseline techniques. Second, we propose four novel measures. One of them significantly outperforms the baselines, the others perform comparably to the state-of-the-art techniques. Finally, we successfully apply one of the novel measures in two text processing systems.
Novel Compact Bent Transformers Embedding Routing Capability in Waveguide Structures

In this paper, novel compact bent transformers embedding routing capability in waveguide structures are presented. The new design technique proposed in this paper allows the designers to obtain a maximum of $\mathbf{N}^{\mathbf{3}}-\mathbf{1}$ bent alternatives for a given N-order in-line transformer. It is accomplished by means of mimicking the local reflection coefficients in magnitude and phase of each discontinuity with the targeted one. The novel design procedure has been proved trough the design and simulation of a Ku-band 90°-bent transformer between WR75 standard input port and a reduced-height output port. The novel structure achieves more than 50%-footprint reduction in comparison with its classical counterpart.
Integrated Modeling of GC-Content, Mappability, Tumor Impurity and Aneuploidy for Accurate Detection of Genomic Aberrations

Next-generation sequencing has been widely used in cancer-focused studies for comprehensive landscape of tumor genomes. Detection of genomic aberrations is one of the focal points in this area. Analysis of tumor sequencing data is usually complicated by several critical issues, such as GC-content bias, mappability bias, tumor impurity, and aneuploidy. Efficient computational methods are still in great demand for comprehensively addressing these issues. We introduce GPHMM-SEQ, a novel algorithm for inferring tumor impurity and ploidy as well as detecting copy number alterations and loss of heterozygosity from paired tumor-normal samples. Read depth signals derived from sequencing data are analyzed using a novel hidden Markov model that employs integrated representation of GC-content bias, mappability bias, tumor impurity, and aneuploidy. The evaluation on simulated and real tumor sequencing data demonstrates GPHMM-SEQ has the superior performance compared to existing methods.
A Survey of Mobile Social Networks: Applications, Social Characteristics, and Challenges

With the popularity of mobile devices and the development of the wireless technologies, humans can be connected ubiquitously. Because of the mobility of the devices, it is a hard task to maintain the end-to-end path between source node and destination node. Researchers have introduced analysis of nodes’ social behavior to solve the problem of data dissemination in the networks, which leads to the emersion of the Mobile Social Networks (MSNs). They increase the performance of data forwarding by the social relationships and interactions among nodes. Many schemes and algorithms have been proposed to enhance data forwarding performance and provide humanized service by introducing social features and digging social properties. In this paper, first, we investigate the architectures and evolutionary process of the MSNs. Then, the social features of nodes existing in the MSNs and main social properties of MSNs are described. In term of the state-of-the-art works, data forwarding strategies in the MSNs are divided into four categories, including only encounter history-based strategies, social-based strategies, incentive mechanisms, and forwarding methods that take social selfishness into consideration of this paper. Finally, the major issues and challenges are discussed.
Monotherapy with Lamotrigine in patients with Alzheimer's disease and seizures

We used Lamotrigine LTG (50-600mg /24h) in 12 patients, 58-to 95-years-old (A Group) and other antiepileptics in nine patients (B Group) with the diagnosis of probable Alzheimer's disease (AD) and seizures. We also administered LTG (50-700mg/24h) in 16 patients (C Group) and other antiepileptics in nine patients (D Group) with other neurological disorders with cognitive disorders and seizures. The results showed that in eight from 12 patients with AD, and in nine from 16 with other neurological disorders, seizures stopped after treatment with LTG—while in five from nine patients with AD and six from 18 with other neurological disorders, seizures stopped after treatment with other antiepileptic drugs. Side effects in 28 patients treated with LTG included only three mild rashes, while side effects in 18 patients treated with other antiepileptic drugs included—six patients with fatigue, two with rashes, one with dizziness, one with low platelets, one with sleepiness and one with loss of hair. Conclusively, this study shows that LTG is an effective and safe monotherapy in patients with cognitive disorders and AD.
Joint low‐complexity equalization and carrier frequency offset compensation for underwater acoustic OFDM communication systems with banded‐matrix approximation at different channel conditions

Underwater acoustic wireless communication is considered as one of the most challenging communication technologies due to low propagation speed, water salinity, water depth, PH degree, and water temperature. Equalization is one of the means to improve the system performance. Matched filter (MF), zero forcing (ZF), and minimum mean square error (MMSE) equalizers can be used for channel equalization. Unfortunately, the performance of the matched filter is destroyed in the Multiple‐Input Multiple‐Output (MIMO) configurations. In addition, the ZF equalizer suffers from noise enhancement and high complexity because of the direct matrix inversion. On the other hand, the MMSE equalizer requires estimating the operating signal‐to‐noise ratio (SNR) to work properly. In this paper, we present a joint low‐complexity regularized ZF equalizer and carrier frequency offset (CFO) compensation scheme to deal with these problems. The proposed equalization algorithm mitigates the noise enhancement problem using a constant regularization parameter, and it can be implemented with low complexity using the banded‐matrix approximation. Also, we test the performance of the proposed system at different channel conditions. Simulation results show that the proposed equalizer has the ability to enhance the system performance with lower complexity than those of the other equalization algorithms at different channel conditions.
Research on Priority Control Method of Conventional Public Traffic Signals

In this paper, for the bus on the main road of the single intersection, the main road of the bus is given priority. According to the strategy of early green light and green light extension, the bus time of the main bus is given more green time, and the bus is guaranteed while ensuring the efficiency of the intersection. Delays are minimized. At the multi-intersection transit priority, the analysis will focus on the possible delays caused by the motorcade at the intersection, and analyze the relationship between the delay and the non-heading delay in terms of the delay between the vehicle fleet and the intersection, and establish the per capita Delayed the smallest coordinated control model to achieve priority control of public transport signals. And the examples are introduced for verification. The results show that the green light extension and the green light early rise two different signal priority methods can effectively reduce the delay of the intersection bus.
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data, while existing neural network models are not readily-adapted to the longitudinal setting. This paper develops a novel convolutional approach that addresses these drawbacks. We present MATCH-Net: a Missingness-Aware Temporal Convolutional Hitting-time Network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's Disease Neuroimaging Initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes attesting to the model's potential utility in clinical decision support.
Sound Change Integration Error: An Explanatory Model of Tinnitus

A growing body of research is focused on identifying and understanding the neurophysiological mechanisms that underlie tinnitus. Unfortunately, however, most current models cannot adequately explain the majority of tinnitus features. For instance, although tinnitus generally appears within minutes after entering a silent environment, most models postulate that tinnitus emerges over a much larger timescale (days). Similarly, there is a limited understanding of how the severity of tinnitus can differ in patients with a similar degree of hearing loss. To address this critical knowledge gap, we have formulated a novel explanatory model of tinnitus, the perception-update (PU) model, which rests on a theory of information processing and can explain several key characteristics of tinnitus onset. The PU model posits that the brain continuously updates the information received from the inner ear by comparing it to the received information immediately before. That is, the auditory system processes the relative change in sensory input, as opposed to the absolute value of the auditory input. This is analogous to the functioning of data compression technology used for music and images called differential pulse code modulation (differential PCM). The PU model proposes that the inner ear transmits sound change to the auditory cortex via an auditory N1 response, an event-related potential component that constitutes is a prime signaler of auditory input change. In cases of hearing impairment, the PU model posits that the auditory system finds itself in a state of uncertainty where perception has to be predicted based on previous stimulation parameters, which can lead to the emergence of tinnitus.
Exploring changes in health visitors knowledge, confidence and decision making for women with perinatal mental health difficulties following a brief training package

Rationale and objective: Perinatal Mental Health (PMH) is an issue that spans the spectrum of pregnancy and childbirth and is now acknowledged to be significant on a global level. Health visitors (HVs) are increasingly expected to extend their knowledge and to understand and identify PMH in the antenatal period and across the spectrum from mild/moderate to severe. While training has been shown to enable HVs to identify post-natal depression (PND) effectively and reduce the proportion of women at risk, the mechanisms underpinning this success are unclear. This paper reports on the findings of a mixed methods study aimed at examining the impact of a single half day training session on perinatal mental health problems (PMHP) on HVs knowledge, confidence and empowerment in relation to managing PMH. Methods: Findings from data gathered by Likert Scales and focus group discussions are presented. Results: Training can empower HVs to identify PMHP beyond PND and plays a vital role in promoting confidence. Conclusions: This research highlights the potential that training of this type has on service provision and delivery. In a resource-limited service, the feasibility of a brief training package has demonstrated positive results for health visitors, childbearing women and their families.
Origin of Low-Frequency Noise in Triple-Gate Junctionless n-MOSFETs

The low-frequency noise in triple-gate junctionless n-MOSFETs, with channel lengths varying from 95 to 25 nm and operating in the bulk and accumulation modes, is investigated by measurements in the frequency and time domains. The experimental drain current noise spectra present <inline-formula> <tex-math notation="LaTeX">$1/{f}$ </tex-math></inline-formula> and Lorentzian-type behavior components. The noise spectra in the time domain reveal that the Lorentzian-type behavior components are due to the capture and emission processes of carriers at discrete gate insulator traps, resulting in random telegraph noise (RTN). The <inline-formula> <tex-math notation="LaTeX">$1/{f}$ </tex-math></inline-formula> behavior can be described by the carrier number with the correlated mobility fluctuations model. In the below-threshold region, the conducting channel is isolated from the interface by depletion region. In the above-threshold region (bulk conduction mode), the histograms of the time-domain data show multilevel switching events, from which one or more individual traps can be distinguished. The extracted time constants of two-level RTN signals indicate the interaction of a single trap either with the channel or with both channel and gate. The relative RTN amplitude is described with the carrier number with the mobility correlated fluctuations physics-based model or with the “hole in the inversion layer” stochastic simulation-based model, enabling estimation of the flat-band voltage fluctuation caused by the RTN.