Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep brain stimulation can suppress pathological synchronisation in parkinsonian patients

Background Although deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a highly effective therapeutic intervention in severe Parkinson's disease, its mechanism of action remains unclear. One possibility is that DBS suppresses local pathologically synchronised oscillatory activity. Methods To explore this, the authors recorded from DBS electrodes implanted in the STN of 16 patients with Parkinson's disease during simultaneous stimulation (pulse width 60 μs; frequency 130 Hz) of the same target using a specially designed amplifier. The authors analysed data from 25 sides. Results The authors found that DBS progressively suppressed peaks in local field potential activity at frequencies between 11 and 30 Hz as voltage was increased beyond a stimulation threshold of 1.5 V. Median peak power had fallen to 54% of baseline values by a stimulation intensity of 3.0 V. Conclusion The findings suggest that DBS can suppress pathological 11–30 Hz activity in the vicinity of stimulation in patients with Parkinson's disease. This suppression occurs at stimulation voltages that are clinically effective.
Reliability and dimensionality of the Short Alcohol Dependence Data (SADD) questionnaire in a clinical sample of hospitalized patients: using the SADD in a general hospital setting.

OBJECTIVE To assess the internal consistency and dimensionality of the Short Alcohol Dependence Data questionnaire in a general hospital sample. METHOD A cross-sectional study was carried out involving 169 randomly selected inpatients of a general hospital in São Paulo, Brazil. A Portuguese version of the 15-item Short Alcohol Dependence Data questionnaire was applied together with a questionnaire to assess demographic and anthropometric data. RESULTS The internal consistency of the Short Alcohol Dependence Data as measured using Cronbach's alpha was 0.79, indicating that the items of the questionnaire are related to the same construct. Principal Component Analysis of the scale resulted in a five-dimension solution accounting for 69.6% of the total variance. The mean score of the Short Alcohol Dependence Data in this sample was 7.96 (SD = 6.8). CONCLUSION The adapted version of the SADD seems to retain the characteristics of the original version. The internal consistency of the instrument suggests that its questions converge to the same construct. The factorial solution reflects different dimensions that correspond to distinct aspects of the alcohol dependence syndrome.
Narrow bus encoding for low-power DSP systems

High levels of integration in integrated circuits often lead to the problem of running out of pins. Narrow data buses can be used to alleviate this problem provided that the degraded performance due to wait cycles can be tolerated. We address bus coding methods for low-power core-based systems incorporating narrow buses. We show that transition signaling combined with bus-invert coding, which we call BITS coding, is particularly suitable for the data patterns of typical DSP applications on narrow data buses. The application of BITS coding to real circuit design is limited by the extra bus line introduced, which changes the pinout of the chip. We propose a new coding method, which does not require the extra bus line but retains the advantage of BITS.
High-resolution magic angle spinning 1H-NMR spectroscopy studies on the renal biochemistry in the bank vole (Clethrionomys glareolus) and the effects of arsenic (As3+) toxicity

1. High-resolution magic angle spinning (MAS) 1H-NMR spectroscopy was used to study renal metabolism and the toxicity of As3+, a common environmental contaminant, in the bank vole (Clethrionomys glareolus), a wild species of rodent. 2. Following a 14-day exposure to an environmentally relevant dose of As2O3 (28 mg kg -1 feed), voles displayed tissue damage at autopsy. MAS 1H spectra indicated abnormal lipid profiles in these samples. 3. Tissue necrosis was also evident from measurements of the apparent diffusion coefficient of water in the intact tissue using MAS 1H diffusion-weighted spectroscopy, its first application to toxicology. 4. Comparison of renal tissue from the wood mouse (Apodemus sylvaticus) exposed to identical exposure levels of As3+ suggested that the bank vole is particularly vulnerable to As3+ toxicity.
Overcoming Dataset Bias : An Unsupervised Domain Adaptation Approach

Recent studies have shown that recognition datasets are biased. Paying no heed to those biases, learning algorithms often result in classifiers with poor crossdataset generalization. We are developing domain adaptation techniques to overcome those biases and yield classifiers with significantly improved performance when generalized to new testing datasets. Our work enables us to continue to harvest the benefits of existing vision datasets for the time being. Moreover, it also sheds insights about how to construct new ones. In particular, domain adaptation raises the bar for collecting data — the most informative data are those which cannot be classified well by learning algorithms that adapt from existing datasets.
Adversarial Top- $K$ Ranking

We study the top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> ranking problem where the goal is to recover the set of top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> ranked items out of a large collection of items based on partially revealed preferences. We consider an <italic>adversarial crowdsourced</italic> setting where there are two population sets, and pairwise comparison samples drawn from one of the populations follow the standard Bradley-Terry-Luce model (i.e., the chance of item <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> beating item <inline-formula> <tex-math notation="LaTeX">$j$ </tex-math></inline-formula> is proportional to the relative score of item <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> to item <inline-formula> <tex-math notation="LaTeX">$j$ </tex-math></inline-formula>), while in the other population, the corresponding chance is inversely proportional to the relative score. When the relative size of the two populations is known, we characterize the minimax limit on the sample size required (up to a constant) for reliably identifying the top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> items, and demonstrate how it scales with the relative size. Moreover, by leveraging a tensor decomposition method for disambiguating mixture distributions, we extend our result to the more realistic scenario, in which the relative population size is unknown, thus establishing an upper bound on the fundamental limit of the sample size for recovering the top-<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> set.
Generalized network tomography

For successful estimation, the usual network tomography algorithms crucially require i) end-to-end data generated using multicast probe packets, real or emulated, and ii) the network to be a tree rooted at a single sender with destinations at leaves. These requirements, consequently, limit their scope of application. In this paper, we address successfully a general problem, henceforth called generalized network tomography, wherein the objective is to estimate the link performance parameters for networks with arbitrary topologies using only end-to-end measurements of pure unicast probe packets. Mathematically, given a binary matrix A, we propose a novel algorithm to uniquely estimate the distribution of X, a vector of independent non-negative random variables, using only IID samples of the components of the random vector Y = AX. This algorithm, in fact, does not even require any prior knowledge of the unknown distributions. The idea is to approximate the distribution of each component of X using linear combinations of known exponential bases and estimate the unknown weights. These weights are obtained by solving a set of polynomial systems based on the moment generating function of the components of Y. For unique identifiability, it is only required that every pair of columns of the matrix A be linearly independent, a property that holds true for the routing matrices of all multicast tree networks. Matlab based simulations have been included to illustrate the potential of the proposed scheme.
Molecular Systems Biology Peer Review Process File Generalized Bacterial Genome Editing Using Mobile Group Ii Introns and Cre-lox Transaction Report

(Note: With the exception of the correction of typographical or spelling errors that could be a source of ambiguity, letters and reports are not edited. The original formatting of letters and referee reports may not be reflected in this compilation.) Thank you again for submitting your work to Molecular Systems Biology. We have now heard back from the three referees who agreed to evaluate your manuscript. As you will see from the reports below, the referees find the topic of your study of potential interest. They raise, however, substantial concerns on your work, which, I am afraid to say, preclude its publication in its present form. The reviewers, in general, agreed that this work describes a genome editing method that may have advantages other existing technologies. Reviewers #1 and #3, however, were not yet fully convinced of the generality or portability of this method, and wrote that tests on additional bacterial species, or at another genomic loci, would substantially improve this work. These two reviewers both rated the "validity of conclusions" and the "suitability of publication" as "medium" in our online system, indicating that additional evidence appears to be required to conclusively demonstrate the broad usefulness of this methodology. In addition, the reviewers had a number of concerns related to the presentation of this work, and felt that a more detailed description of the experimental methods and plasmids would be essential. Moreover, the last reviewer felt that greater clarity was required in describing the full experimental process and the total time required to perform a genome edit-including plasmid transformations, genome editing, and finally plasmid curing.
Novel role of the Ca 2 1 -ATPase in NMDA-induced intracellular acidiﬁcation

Novel role of the Ca 2 1 -ATPase in NMDA-induced intracellular acidiﬁcation. Am. J. Physiol. 277 ( Cell Physiol. 46): C717–C727, 1999. —The mechanism involved in N -methyl- D -glucamine (NMDA)-induced Ca 2 1 dependent intracellular acidosis is not clear. In this study, we investigated in detail several possible mechanisms using cultured rat cerebellar granule cells and microﬂuorometry [fura 2-AM or 2 8 ,7 8 -bis(2-carboxyethyl)-5(6)-carboxyﬂuores-cein-AM]. When 100 µM NMDA or 40 mM KCl was added, a marked increase in the intracellular Ca 2 1 concentration ([Ca 2 1 ] i ) and a decrease in the intracellular pH were seen. Acidosis was completely prevented by the use of Ca 2 1 -free medium or 1,2-bis(2-aminophenoxy)ethane- N , N , N 8 , N 8 -tetra-acetic acid-AM, suggesting that it resulted from an inﬂux of extracellular Ca 2 1 . The following four mechanisms that could conceivably have been involved were excluded: 1 ) Ca 2 1 displacement of intracellular H 1 from common binding sites; 2 ) activation of an acid loader or inhibition of acid extruders; 3 ) overproduction of CO 2 or lactate; and 4 ) collapse of the mitochondrial membrane potential due to Ca 2 1 uptake, resulting in inhibition of cytosolic H 1 uptake. However, NMDA/KCl-induced acidosis was largely prevented by glycolytic inhibitors (iodoacetate or deoxyglucose in glucose-free medium) or by inhibitors of the Ca 2 1 -ATPase (i.e., Ca 2 1 /H 1 exchanger), including La 3 1 , orthovanadate, eosin B, or an extracellular pH of 8.5. Our results therefore suggest that Ca 2 1 -ATPase is involved in NMDA-induced intracellular acidosis in granule cells. We also provide new evidence that NMDA-evoked intracellular acidosis probably serves as a negative feedback signal, probably with the acidiﬁcation itself inhibiting the NMDA-induced [Ca 2 1 ] i increase.
Research on the process window for multi-step forming of CF/PA12 composite prepreg

Thermoplastic composites have gained more importance in recent decades, mainly in aerospace industry. Press forming is widely used for manufacturing thermoplastic composites. The main aim of this research is to investigate the feasibility of multi-step forming of CF/PA12 composite prepreg. At first, the ideal process window of the CF/PA12 material was found experimentally. With that information, multi-step forming was done in a press setup that includes infra-red heating panels, a fast transfer system and a fast forming and consolidating pneumatic press. The quality of the multi-step formed laminates was compared with the laminates consolidated using a 1000 kN hot press. Cross-sectional microscopy, ultrasonic C-scan and short beam strength tests were performed for analyzing the performance of multi-step formed laminates. The thesis is oncluded by stating that there is a reduction in short beam strength in multi-step formed laminates of around 25% when compared to laminates made in the hot press.
Task Scheduling on the Cloud with Hard Constraints

Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more challenging than grid and cluster environments. This is because a user may have a budgetary constraint or a deadline for executing the BoT application in order to keep the overall execution costs low. The research in this paper is motivated to investigate task scheduling on the cloud, given two hard constraints based on a user-defined budget and a deadline. A heuristic algorithm is proposed and implemented to satisfy the hard constraints for executing the BoT application in a cost effective manner. The proposed algorithm is evaluated using four scenarios that are based on the trade-off between performance and the cost of using different cloud resource types. The experimental evaluation confirms the feasibility of the algorithm in satisfying the constraints. The key observation is that multiple resource types can be a better alternative to using a single type of resource.
OPTICAL/INFRARED FLARES OF GRB 080129 FROM LATE INTERNAL SHOCKS

Strong optical and near-infrared (NIR) flares were discovered in the afterglow of GRB 080129. Their temporal behaviors, the sudden emergence and the quick disappearance, are rather similar to those of many X-ray flares (for instance, the giant flare of GRB 050502B). We argue that the optical/NIR flares following GRB 080129 are a low-energy analogue of the X-ray flares and the most likely interpretation is the “late internal shock model. ” In this model, both the very sharp decline and the very small ratio between the duration and the occurrence time of the optical/NIR flares in GRB 080129 can be naturally interpreted. The initial Lorentz factor of the flare outflow is found to be ∼30, consistent with the constraint ⩽120 set by the forward shock afterglow modeling. Other possibilities, like the reverse shock emission or the radiation from the continued but weaker and weaker collision between the initial γ-ray burst outflow material, are disfavored.
Financial Options Pricing Using the MKPF Algorithm

A mixture Kalman Particle Filter (MKPF) based options pricing method is proposed. The MKPF algorithm uses the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) as proposal distribution to generate the importance sampling density. Each particle is firstly updated by the UKF and obtains a state estimation. Thereafter, this estimation is used as the prior of the EKF, in which the particle is updated again to gain the final estimation of the state. We use the classical B-S model in the experiment aiming at evaluating the performance of the newly proposed method and other existing algorithms. The experimental results show that the MKPF outperforms other algorithms.
Local lexical adaptation in Machine Translation through triangulation: SMT helping SMT

We present a framework where auxiliary MT systems are used to provide lexical predictions to a main SMT system. In this work, predictions are obtained by means of pivoting via auxiliary languages, and introduced into the main SMT system in the form of a low order language model, which is estimated on a sentence-by-sentence basis. The linear combination of models implemented by the decoder is thus extended with this additional language model. Experiments are carried out over three different translation tasks using the European Parliament corpus. For each task, nine additional languages are used as auxiliary languages to obtain the triangulated predictions. Translation accuracy results show that improvements in translation quality are obtained, even for large data conditions.
Multiple-Throw Millimeter-Wave FET Switches for Frequencies from 60 up to 120 GHz

This paper presents the design and performance of various millimeter-wave FET switches realized in a metamorphic HEMT technology. The single-pole multi-throw switch configurations are targeting wireless communication frontends and imaging radiometers at 60, 94 and 120 GHz. In SPDT switches, state-of-the-art insertion loss of 1.4 and 1.8 dB is achieved at 60 and 94 GHz, respectively. Rivalled only by PIN diode switches, an insertion loss of <2 dB is demonstrated up to 120 GHz. Shorted stubs are used to compensate for parasitic FET capacitance and allow for matching. Linearity data is presented for 60 and 94 GHz SPDT switches. A comprehensive comparison with state-of-the-art planar SPDT switches is included. A 2:6 switch network for multi-antenna transceivers achieves <4 dB insertion loss at 60 GHz.
Generating Student Feedback from Time-Series Data Using Reinforcement Learning

We describe a statistical Natural Language Generation (NLG) method for summarisation of time-series data in the context of feedback generation for students. In this paper, we initially present a method for collecting time-series data from students (e.g. marks, lectures attended) and use example feedback from lecturers in a datadriven approach to content selection. We show a novel way of constructing a reward function for our Reinforcement Learning agent that is informed by the lecturers’ method of providing feedback. We evaluate our system with undergraduate students by comparing it to three baseline systems: a rule-based system, lecturerconstructed summaries and a Brute Force system. Our evaluation shows that the feedback generated by our learning agent is viewed by students to be as good as the feedback from the lecturers. Our findings suggest that the learning agent needs to take into account both the student and lecturers’ preferences.
Investigating classification for natural language processing tasks

This report investigates the application of classification techniques to four natural language processing (NLP) tasks. The classification paradigm falls within the family of statistical and machine learning (ML) methods and consists of a framework within which a mechanical ‘learner’ induces a functional mapping between elements drawn from a particular sample space and a set of designated target classes. It is applicable to a wide range of NLP problems and has met with a great deal of success due to its flexibility and firm theoretical foundations. The first task we investigate, topic classification, is firmly established within the NLP/ML communities as a benchmark application for classification research. Our aim is to arrive at a deeper understanding of how class granularity affects classification accuracy and to assess the impact of representational issues on different classification models. Our second task, content-based spam filtering, is a highly topical application for classification techniques due to the ever-worsening problem of unsolicited email. We assemble a new corpus and formulate a state-of-the-art classifier based on structured language model components. Thirdly, we introduce the problem of anonymisation, which has received little attention to date within the NLP community. We define the task in terms of obfuscating potentially sensitive references to real world entities and present a new publicly-available benchmark corpus. We explore the implications of the subjective nature of the problem and present an interactive model for anonymising large quantities of data based on syntactic analysis and active learning. Finally, we investigate the task of hedge classification, a relatively new application which is currently of growing interest due to the expansion of research into the application of NLP techniques to scientific literature for information extraction. A high level of annotation agreement is obtained using new guidelines and a new benchmark corpus is made publicly available. As part of our investigation, we develop a probabilistic model for training data acquisition within a semi-supervised learning framework which is explored both theoretically and experimentally. Throughout the report, many common themes of fundamental importance to classification for NLP are addressed, including sample representation, performance evaluation, learning model selection, linguistically-motivated feature engineering, corpus construction and real-world application.
Integrating Output from Specialized Modules in Machine Translation: Transliterations in Joshua

Integrating Output from Specialized Modules in Machine Translation: Transliterations in Joshua In many cases in SMT we want to allow specialized modules to propose translation fragments to the decoder and allow them to compete with translations contained in the phrase table. Transliteration is one module that may produce such specialized output. In this paper, as an example, we build a specialized Urdu transliteration module and integrate its output into an Urdu-English MT system. The module marks-up the test text using an XML format, and the decoder allows alternate translations (transliterations) to compete.
DAPS: Intelligent delay-aware packet scheduling for multipath transport

The increasing heterogeneity and asymmetry in wireless network environments makes QoS guarantees in terms of delays and throughput a challenging task. In this paper, we study a novel scheduling algorithm for multipath transport called Delay Aware Packet Scheduling (DAPS) which aims to reduce the receiver's buffer blocking time considered as a main parameter to enhance the QoS in wireless environments. We develop an analytical model of maximum receiver's buffer blocking time and extend the DAPS algorithm considering implementation issues. Performance evaluations based on ns-2 simulations highlight the enhanced QoS that DAPS can provide. With reference to the classical multipath transport protocol CMT-SCTP, we observe a significant reductions of the receiver's buffer occupancy, down by 77%, and the application delay, down by 63%.