Miracle-dz/UniFAR-FTUs huggingface dataset:
Editorial: Neuroinformatics of large-scale brain modelling

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A 20-mW 640-MHz CMOS continuous-time ΣΔ ADC with 20-MHz signal bandwidth, 80-dB dynamic range and 12-bit ENOB

A wide bandwidth continuous-time sigma-delta ADC, operating between 20 and 40 MS/s output data rate, is imple- mented in 130-nm CMOS. The circuit is targeted for applications that demand high bandwidth, high resolution, and low power, such as wireless and wireline communications, medical imaging, video, and instrumentation. The third-order continuous-time mod- ulator comprises a third-order RC operational-amplifier-based loop filter and 4-bit internal quantizer operating at 640 MHz. A 400-fs rms jitter LC PLL with 450-kHz bandwidth is integrated, generating the low-jitter clock for the jitter-sensitive contin- uous-time ADC from a single-ended input clock between 13.5 and 40 MHz. To reduce clock jitter sensitivity, nonreturn-to-zero (NRZ) DAC pulse shaping is used. The excess loop delay is set to half the sampling period of the quantizer and the degradation of modulator stability due to excess loop delay is avoided with a new architecture. The ADC achieves 76-dB SNR, 78-dB THD, and a 74-dB SNDR or 12 ENOB over a 20-MHz signal band at an OSR of 16. The power consumption of the CT modulator itself is 20 mW and in total the ADC dissipates 58 mW from the 1.2-V supply. Index Terms—Analog-to-digital conversion, CMOS analog inte- grated circuits, continuous-time modulation, continuous-time filters, delta-sigma modulation, low-pass filter, low power design, low-voltage design, multibit internal quantization, sigma-delta modulation.
Potential use of gold-silver core-shell nanoparticles derived from Garcinia mangostana peel for anticancer compound, protocatechuic acid delivery

Colorectal cancer is one of the most killing cancers and this has become a global problem. Current treatment and anticancer drugs cannot specifically target the cancerous cells, thus causing toxicity towards surrounding non-cancer cells. Hence, there is an urgent need to discover a more target-specific therapeutic agent to overcome this problem. Core-shell nanoparticles have emerged as good candidate for anticancer treatment. This study aimed to synthesize core-shell nanoparticles via green method which utilised crude peels extract of Garcinia mangostana as reducing and stabilising agents for drug delivery. Gold-silver core-shell nanoparticles (Au-AgNPs) were synthesized through seed germination process in which gold nanoparticles acted as the seed. A complete coating was observed through transmission electron microscopy (TEM) when the ratio of AuNPs and AgNPs was 1:9. The size of Au-AgNPs was 38.22 ± 8.41 nm and was mostly spherical in shape. Plant-based drug, protocatechuic acid (PCA) was loaded on the Au-AgNPs to investigate their anticancer activity. In HCT116 colon cancer cells, PCA-loaded Au-AgNPs (IC50 = 10.78 μg/ml) showed higher inhibitory action than the free PCA (IC50= 148.09 μg/ml) and Au-AgNPs alone (IC50= 24.36 μg/ml). Up to 80% inhibition of HCT116 cells was observed after the treatment of PCA-loaded Au-AgNPs at 15.63 μg/ml. The PCA-loaded Au-AgNPs also showed a better selectivity towards HCT116 compared to CCD112 colon normal cells when tested at the same concentrations. These findings suggest that Au-AgNPs system can be used as a potent nanocarrier to combat cancerous cells by offering additional anticancer properties to the loaded drug.
The mental health status of offshore oil platform workers during the COVID-19 pandemic

Background Previous studies indicated that offshore workers have a high level of work-related stress on an everyday basis. This study aims to assess the prevalence and determinants of mental health conditions in offshore oil platform workers during the COVID-19 pandemic. Methods Workers of three oil and gas platforms were assessed in this cross-sectional study. Their mental status was evaluated by the Posttraumatic Stress Disorder (PTSD-8) questionnaire, and Depression Anxiety, Stress Scales (DASS) questionnaires. Furthermore, we assessed satisfaction with life (SWL) with a single question. Finally, multivariate logistic regression was used to determine the association of demographic and work-related variables with mental health outcomes. Results Overall, 278 (Males:197, Females: 81) out of 315 invited workers with a mean age of 35.6 (SD: 7.2) years were included in this study using a random sampling method (participation rate: 88.2%). PTSD symptoms and Insomnia were observed in 9 (3.2%) and 138 (49.6%) of the participants, respectively. The prevalence of stress, anxiety, and depressive symptoms were 72 (25.9%), 70 (24.6%), and 85 (30.5%), respectively. Based on multivariable (adjusted) logistic regression analysis, women had significantly higher odds of stress and anxiety than men; those with an academic education were significantly more dissatisfied with their lives than those without an academic education. Conclusion Our findings revealed a high prevalence of anxiety, depressive symptoms, and stress among offshore oil platformers during the COVID-19 pandemic, especially in women. Indicating that women and those with a higher education level in the oil platform work settings are more susceptible to stressors.
WMM : a resilient Weak Memory Model

A good memory model should have a precise definition that can be understood by any computer architect readily. It should also be resilient in the sense that it should not break when new microarchitecture optimizations are introduced to improve singlethreaded performance. We introduce WMM, a new weak memory model, which meets these criteria. WMM permits all load-store reorderings except a store is not allowed to overtake a load. WMM also permits both memory dependency speculation and load-value prediction. We define the operational semantics of WMM using a novel conceptual device called invalidation buffer, which achieves the effect of out-of-order instruction execution even when instructions are executed in-order and one-at-a-time. We show via examples where memory fences need to be inserted for different programming paradigms. We highlight the differences between WMM and other weak memory models including Release Consistency and Power. Our preliminary performance evaluation using the SPLASH benchmarks shows that WMM implementation performs significantly better than the aggressive implementations of SC. WMM holds the promise to be a vendor-independent stable memory model which will not stifle microarchitectural innovations. Thesis Supervisor: Arvind Title: Johnson Professor of Computer Science and Engineering
Probabilistic approaches to consumer-generated review recommendation

Consumer-generated reviews play an important role in online purchase decisions for many consumers. However, the quality and helpfulness of online reviews varies significantly. In addition, the helpfulness of different consumer-generated reviews is not disclosed to consumers unless they carefully analyze the overwhelming number of available contents. Therefore, it is of vital importance to develop predictive models that can evaluate online product reviews efficiently and then display the most useful reviews to consumers, in order to assist them in making purchase decisions. This thesis examines the problem of building computational models for predicting whether a consumer-generated review is helpful based on consumers' online votes on other reviews (where a consumer's vote on a review is either HELPFUL or UNHELPFUL), with the aim of suggesting the most suitable products and vendors to consumers. In particular, we propose in this thesis three different helpfulness prediction approaches for consumer-generated reviews. Our entropy-based approach is relatively simple and suitable for applications requiring simple recommendation engine with fully-voted reviews. However, our entropy-based approach, as well as the existing approaches, lack a general framework and are all limited to utilizing fully-voted reviews. We therefore present a probabilistic helpfulness prediction framework to overcome these limitations. To demonstrate the versatility and flexibility of this framework, we propose an EM-based model and a logistic regression-based model. We show that the EM-based model can utilize reviews voted by a very small number of voters as the training set, and the logistic regression-based model is suitable for real-time helpfulness predicting of consumer-generated reviews. To our best knowledge, this is the first framework for modeling review helpfulness and measuring the goodness of models. Although this thesis primarily considers the problem of review helpfulness prediction, the presented probabilistic methodologies are, in general, applicable for developing recommender systems that make recommendation based on other forms of user-generated contents.
A case for information theory-based modelling of human multitasking performance

ABSTRACT Technological advances have led to increased prevalence of human multitasking. Traditionally, multitasking performance is measured for each individual task, utilising multiple performance metrics. Recently, the human operator informatic model (HOIM) has been developed as a quantitative model of human–machine interaction capable of measuring multitasking performance with a single metric. In this study, we investigated the performance effects of both a different number of tasks and five different task combinations while maintaining a constant level of overall difficulty. Results indicate that, at a constant difficulty level, neither number of tasks nor task combination significantly affects multitasking performance, indicating the reliability of input baud rate as a measure of system complexity. No task interaction effects were found among the different tasks. This study demonstrates the application of information theory for modelling of human multitasking performance and makes a case for the use of the HOIM in future studies of human information processing.
FEATURE SELECTION FOR IMAGE RETRIEVAL BASED ON EVOLUTIONARY COMPUTATION

In this paper we implemented feature selection for content based image retrieval using evolutionary computation. In this system, we used feature extraction techniques for color, texture and shape. The three techniques are used for feature extraction such as color moment, Gabor filter, and Edge histogram descriptor. To reduce the dimensionality and find best optimal features from feature set using feature selection based on two evolutionary computations i.e. Genetic algorithm, and Binary Bat Algorithm. These subset features are divided into similar image classes using k-means clustering algorithm for fast retrieval and improve the computational time. We compared these two algorithms with different parameters i.e. precision, recall and computational time of image retrieval. The experimental result shows feature selection using BBA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to feature selection using GA. In this method selects different combinations of features which user retrieves more similar images. The CBIR system is more efficient and better performs using feature selection based on BBA.
InP DHBT-based clock and data recovery circuits for ultra-high-speed optical data links

In this work, up to 80 Gbit/s monolithically integrated clock and data recovery (CDR) circuits with 1:2 demultiplexer are developed. The integrated circuits are manufactured using an InP double heterojunction bipolar transistor (DHBT) technology, featuring cut-off frequency values of more than 250 GHz. The outstanding and (to some extent) record achievements throughout the work make an essential contribution to the development of future optical telecommunication networks operating at 80 Gbit/s.
Renewal of seasonally anoxic Saanich Inlet is temporally and spatially dynamic

Injection of oxygenated water into anoxic basins sets off a cascade of biogeochemical reactions and ecosystem shifts. The dynamic nature of these events can create spatial variability in the resulting water mass that strongly affects subsequent observations. Their irregularity can also make their prediction for experiment planning challenging. Our study focuses on Saanich Inlet, one of the most accessible and well-studied anoxic basins in the world. In the late summer and early fall, dense water can cross the sill into this fjord, in discrete events, bringing oxygen and nitrate to the deep waters of the inlet. We assess the potential strength of these renewal events using density measured at a bottom mooring on the sill. We find that the occurrence and potential strength of renewals is primarily controlled by tidal current speeds, which can be well predicted. However, the intensity of coastal upwelling, which brings dense water into the estuarine system, plays a significant secondary control, reducing predictability. We also demonstrate that renewals do not result in a homogeneous water mass filling the deep inlet. Instead, high frequency measurements from a profiling mooring in the centre of the inlet reveal that different densities intruding over the several-day renewal period create a complex layering of waters containing different proportions of new oxic and old anoxic waters. Finally, we show that not every instance of high density water observed over the sill results in flushing of the deepest waters inside the inlet. We hypothesize that each renewal improves the chance of a subsequent renewal in the same season by reducing the density contrast between waters entering and already inside the inlet. Consideration of the temporal and spatial complexity of these renewal dynamics is necessary to support studies using Saanich Inlet as a natural laboratory for exploring oxygen deficient systems.
Expression of biomarkers connected to endocrine disruption in Cottus gobio and Salmo trutta fario in relation to sewage treatment plant-efflux and pesticides

The increasing efflux on a large scale of organic contaminants holding endocrine disrupting activity from sewage treatment plants produces detrimental biological effects to various fish species. However, the impact of small-scale sewage treatment plant-efflux in small river streams and narrow creeks is largely unknown. Extensive pesticide output especially in vineyards and orchards also causes adverse effects on the endocrine system of wildlife fish species inhabiting nearby rivers. To elaborate whether fish species and populations in the areas of interest were at risk of experiencing endocrine disruption, we identified different biomarkers related to endocrine disruption in Cottus gobio and Salmo trutta fario and applied this approach to selected Austrian freshwater streams pre and post sewage treatment plants and permanent cultures with extensive pesticide output in South Tyrol. Overall, mRNA expression levels of vitellogenin, estrogen receptor α and zona pellucida genes in wildlife fish, compared to a control population reared under constant conditions in the laboratory were significantly increased. Sewage-treatment plant efflux did not significantly affect the mRNA expression levels while extensive use of pesticides altered mRNA expression significantly in C. gobio. C. gobio and S. trutta fario display different levels of mRNA expression. Cadmium and copper concentrations in liver tissues varied but did not indicate significant levels of contamination. Our results demonstrate the presence of endocrine disrupting chemicals in the tested freshwater streams. We anticipate our study to be a starting point for further studies focusing on the effects of endocrine disrupting chemicals on individuals and populations. Especially the fact that the two selected species reveal highly different levels of mRNA expression levels is of interest when applying biomarker approaches which can be a useful tool for monitoring projects and risk-assessment associated studies.
20 Years of Technology and Language Assessment in "Language Learning & Technology".

Carol A. Chapelle, Iowa State University Erik Voss, Northeastern University This review article provides an analysis of the research from the last two decades on the theme of technology and second language assessment. Based on an examination of the assessment scholarship published in Language Learning & Technology since its launch in 1997, we analyzed the review articles, research articles, book reviews, and commentaries as developing one of two primary thrusts of research on technology and language assessment: technology for efficiency and technology for innovation.
Study on phytotoxicity evaluation and physiological properties of nicosulfuron on sugar beet (Beta vulgaris L.)

Nicosulfuron is an herbicide widely used in corn fields. In northeast China, sugar beet is often planted adjacent to corn, resulting in frequent phytotoxicity of nicosulfuron drift in sugar beet fields. This study was conducted by spraying nicosulfuron to assess the phytotoxicity and clarify the mechanism of nicosulfuron toxicity on sugar beet. The results showed that nicosulfuron impaired growth and development by reducing photosynthetic capacity and disrupting antioxidant systems at a lethal dose of 81.83 g a.i. ha–1. Nicosulfuron damaged the function of photosynthetic system II (PSII), lowered photosynthetic pigment content, and inhibited photosynthetic efficiency. Compared with the control, the electron transfer of PSII was blocked. The ability of PSII reaction centers to capture and utilize light energy was reduced, resulting in a weakened photosynthetic capacity. The maximum net photosynthetic rate (Amax), light saturation point (LSP), and apparent quantum yield (AQY) decreased gradually as the nicosulfuron dose increased, whereas the light compensation point (LCP) and dark respiration (Rd) increased. Nicosulfuron led to reactive oxygen species (ROS) accumulation in sugar beet leaf, a significant rise in malondialdehyde (MDA) content, electrolytic leakage (EL), and considerable oxidative damage to the antioxidant system. This study is beneficial for elucidating the effects of nicosulfuron toxicity on sugar beet, in terms of phytotoxicity, photosynthetic physiology, and antioxidative defense system.
Consistent prior distributions for transformed models

As recognised by Box and Cox (1964), any transformation of one’s data generally changes the level and range of the observations. For consistency, the joint prior distribution of (λ, θ), the transformation and standard model parameters respectively, should reflect this fact. A general approach to the problem of constructing suitable prior distributions in the presence of a transformation parameter is put forward, with particular regard to the case of vague prior knowledge about the location and range of the data. Approximations, based on a given method of analysis for the standard model, to the posterior distributions of λ and λ | θ are derived.
Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-recidivism Policies in Colombia

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well-defined “retrospective intervention effect” based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.
Fusion of Local and Global Detectors for PHD Filter-Based Multi-Object Tracking

: In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.
Spam Filtering using Support Vector Machine

The traditional anti-spam techniques like Black and White List is not up to the mark in current scenario. The goal of Spam Classification is to distinguish between spam and legitimate mail message. But with the popularization of the Internet, it is challenging to develop spam filters that can effectively eliminate the increasing volumes of unwanted mails automatically before they enter a user's mailbox. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, we evaluate the performance of Non Linear SVM based classifiers with various kernel functions over Enron Dataset.
Feature-driven Question Answering With Natural Language Alignment

Question Answering (QA) is the task of automatically generating answers to natural language questions from humans, serving as one of the primary research areas in natural language human-computer interaction. This dissertation focuses on English fact-seeking (factoid) QA, for instance: when was Johns Hopkins founded? 1 The key challenge in QA is the generation and recognition of indicative signals for answer patterns. In this dissertation I propose the idea of feature-driven QA, a machine learning framework that automatically produces rich features from linguistic annotations of answer fragments and encodes them in compact log-linear models. These features are further enhanced by tightly coupling the question and answer snippets via monolingual alignment. In this work monolingual alignment helps question answering in two aspects: aligning semantically similar words in QA sentence pairs (with the ability to recognize paraphrases and entailment) and aligning natural language words with knowledge base relations (via web-scale data mining). With the help of modern search engines, database and machine learning tools, the proposed method is able to efficiently search through billions of facts in the web space and optimize from millions of linguistic signals in the feature space. QA is often modeled as a pipeline of the form: question (input) → information retrieval (“search”) → answer extraction (from either text or knowledge base) → answer (output). This dissertation demonstrates the feature-driven approach applied through1January 22, 1876