Miracle-dz/UniFAR-FTUs huggingface dataset:
NF‐κB is involved in the survival of cerebellar granule neurons: association of IκBβ phosphorylation with cell survival

The NF-κB transcription factor consists of dimeric complexes belonging to the Rel family, which include p50, p52, p65 (RelA), RelB and c-Rel. NF-κB activity is tightly controlled by IκB proteins which bind to NF-κB preventing its translocation to the nucleus. Activation of NF-κB is most often mediated by IκB degradation, which permits NF-κB to enter the nucleus. We investigated the role of NF-κB in the survival of cerebellar granule neurons. We found that survival of these neurons in high potassium medium is blocked by three separate inhibitors of NF-κB activity: SN-50, N-tosyl-l-phenylalanine chloromethyl ketone and pyrrolidinedithiocarbamate, indicating that NF-κB is required for neuronal survival. Gel-shift assays reveal three complexes that bind to the NF-κB binding site in high potassium medium. Switching these cultures to low potassium medium, a stimulus that leads to apoptotic death, causes a reduction in the level of the largest complex, which contains p65. Overexpression of p65 by transfection inhibits low potassium-induced apoptosis, whereas overexpression of IκBα promotes apoptosis even in high potassium medium. Surprisingly, however, neither the level of endogenous p65 nor that of IκBα and IκBβ is altered by low potassium treatment. Similarly, no changes are seen in the nuclear or cytoplasmic levels of p50, p52, RelB and c-Rel. Phosphorylation of p65, which can lead to its activation, is unchanged. Phosphorylation of IκBβ is, however, reduced by low potassium treatment. Besides being necessary for high potassium-mediated neuronal survival, NF-κB is also involved in the survival-promoting effects of IGF-1 and cAMP as judged by the ability of SN-50 to inhibit the actions of these survival factors and the ability of these factors to inhibit the low potassium-induced alterations in the DNA-binding activity of NF-κB. Taken together, our results show that NF-κB may represent a point of convergence in the signaling pathways activated by different survival factors and that uncommon mechanisms might be involved in NF-κB-mediated survival of cerebellar granule neurons.
EFFICIENT IMPLEMENTATION OF CHINESE REMAINDER THEOREM IN MINIMALLY REDUNDANT RESIDUE NUMBER SYSTEM

The Chinese remainder theorem is widely used in many modern computer applications. This paper presents an eﬃcient approach to the calculation of the rank of a number, a principal positional characteristic that is used in the residue number system. The proposed method does not use large modulo addition operations as compared to a straightforward implementation of the Chinese re-mainder theorem algorithm. The rank of a number is equal to the sum of an inexact rank and a two-valued correction factor that only takes on values of 0 or 1. We propose a minimally redundant residue number system that provides a low computational complexity of the rank calculation. The eﬀectiveness of the novel method is analyzed regarding a conventional non-redundant residue number system. Owing to the extension of the residue code, the complexity of the rank calculation goes down from O ( k 2 ) to O ( k ) by adding the extra residue modulo 2 (where k equals the number of non-redundant residues).
Word-based Neural Prosody Modeling with ToBI

We present a neural model of American English intonation using the discrete tonal transcription system MAE-ToBI. The model uses the words and tonal sequences of the MAE-ToBI annotated portion of the Boston University Radio Speech Corpus. We took as a starting point Dainora’s probabilistic ﬁnite-state grammar of the tonal sequences of two speakers in the corpus. We extended Dainora’s grammar to cover all six speakers in the corpus and found that bigram probabilities and distinctions in distribution of tones over pre-nuclear and nuclear intermediate phrases showed the same patterns as Dainora’s results. To expand beyond her work, we built a word-based Long Short-Term Memory (LSTM) neural model that predicts the MAE-ToBI sequence within an intermediate phrase. We used both randomly initialized vector word embeddings and pre-trained word embeddings from BERT, a bidirectional transformer. BERT achieved 80.58%, 99.79%, and 90.74% accuracy in detecting pitch accent, intermediate, and intonational phrase boundaries. The results demonstrate that it is possible to predict prosody given only texts and discrete prosodic labels without acoustic information. The improvement with BERT demonstrates how the addition of unannotated text data to a small prosodically annotated corpus could be leveraged for prosodic modeling in low-resource languages.
Procrastination, Dependence and Social Loafing: Comparison in High/Low Task Visibility between Active/Passive Procrastinators

The purpose of this study included: To investigate the correlation of active / traditional procrastination motivation, dependent personality and social loafing phenomenon, also clarify whether such passive procrastinators like traditional ones with similar motive and test of active / passive procrastinators with different dependent personality and social loafing phenomenon. Explore the relation of group task assignment with social loafing phenomenon. Control group task assignment to compare the social loafing phenomenon variety of active / passive procrastinators with the high / low task visibility. The subjects sampled 221 elementary students. The research tools include procrastination behavior scale, active procrastination scale, traditional procrastination scale, task visibility scale, dependent personality scale, social loafing phenomenon scale and statistical analysis by structural equation model, multi-group analysis, t-test, and ANOVA. The results were: the model of active and passive procrastinator did not have a significant difference. Active procrastinators had higher pressure preference, ability to deadline, and intent to delay than passive procrastinators, but with a similar degree of antipathy task and fear of failure and with the passive procrastinators. Passive procrastinators with the low visibility of the task resulted in higher social loafing phenomenon instead of active procrastinators. Implications for theory, practice and research are discussed.
If mathematical psychology did not exist we might need to invent it: A comment on theory building in psychology

It is commonplace, when discussing the subject of psychological theory, to write articles from the assumption that psychology differs from physical sciences in that we have no theories that would support cumulative, incremental science. In this brief paper I discuss one counterexample, namely Shepard's (1987) law of generalization and the various Bayesian extensions that it inspired over the last three decades. Using Shepard's law as a running example I argue that psychological theory building is not a statistical problem; mathematical formalism is theoretically beneficial; measurement and theory have a complex relationship; rewriting old theory can yield new insights; and finally, that theoretical growth can drive empirical work. Though generally suggesting that the tools of mathematical psychology are valuable to the psychological theorist, the paper also comments on some limitations to this approach.
A Quantum Computer in a “Chinese Room”

Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer would obtain quite different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number.
Towards Better Ad Experience: Click Prediction Leveraging Sequential Networks Derived Speciﬁcally From User Search Behaviors

We propose a sequential modeling approach to improve click prediction for search engine advertising. Unlike previous studies leveraging advertisement content and their relevance-to-query information, we employ only users’ search behavioral features such as users’ query texts and actual click records of both organic search results and advertisements. By leveraging long short-term memory (LSTM) networks, we successfully modeled users’ sequential search behaviors and fully utilized them in click predictions. Through experiments conducted with large-scale search log data obtained from an actual commercial search engine, we demonstrated that our method combining users’ current and previous search behaviors reaches better prediction performance than baseline methods.
A Hinge-Free, Non-Restrictive, Lightweight Tethered Exosuit for Knee Extension Assistance During Walking

In individuals with motor impairments such as those post-stroke or with cerebral palsy, the function of the knee extensors may be affected during walking, resulting in decreased mobility. We have designed a lightweight, hinge-free wearable robot combining soft textile exosuit components with integrated rigid components, which assists knee extension when needed but is otherwise highly transparent to the wearer. The exosuit can apply a wide range of assistance profiles using a flexible multi-point reference trajectory generator. Additionally, we implemented a controller safety limit to address the risk of hyperextension stemming from the hinge-free design. The exosuit was evaluated on six healthy participants walking uphill and downhill on a treadmill at a 10° slope with a set of joint power-inspired assistance profiles. A comparison of sagittal plane joint angles between no exosuit and exosuit unpowered conditions validated the device transparency. With positive power assistance, we observed reduction in average positive knee biological power during uphill walking (left: 17.5±3.21%, $p=0.005$ ; right: 23.2±3.54%, ${p} =0.008$ ). These initial findings show promise for the assistive capability of the device and its potential to improve the quality of gait and increase mobility in clinical populations.
Feeding Chemoreceptor Sites in the Crayfish Procambarus Clarkii (Girard)

Eine Reihe von Experimenten ergab, das P. clarkii in der Lage ist, Nahrung zu entdecken selbst nach Ausschaltung des Sehvermogens, der Antennulen, Antennen, wie auch des Dactylus der Chelipeden und der ersten Schreitbeinpaare. Die Nahrungslokalisation ist stark beeintrachtigt, wenn die Dactyli der ersten Schreitbeinpaare mit 'Duco cement' abgedeckt werden. Es wird angenommen, das die Atmungsroute und die Mundwerkzeuge bei der Nahrungslokalisation uber einige Entfernung beteiligt sind. Diese Annahme kann jedoch noch nicht experimentell belegt werden.
Deep active learning for multiple object tracking

Context The computer vision community relies on deep learning algorithms that have been trained to perform various tasks and learned the necessary concepts on large amounts of data. However, if one needs to target new tasks, it is necessary to redevelop an appropriate dataset which is costly as the annotation process is expensive. In the case of video data, this is particularly relevant for multiple object tracking which requires ﬁne-grained annotations in both spatial and temporal domains, in contrast with object detection and semantic segmentation for example which require labeling only in the spatial dimension. The aim of active learning is to assist the costly human annotation while at the same time continuously improving the task performance. Application wise, the study follows the work performed in the ANR project MOHICANS 1 (2015-2019) which aims to extract high-quality observations from dense crowds in order to facilitate the understanding of these complex, hyper-connected systems and to prevent dangerous jamming or turbulence phenomena. We propose to the prospective student to work on a deep learning algorithm that would improve the state of the art in the ﬁeld of dense pedestrian tracking.
A Reinforced Improved Attention Model for Abstractive Text Summarization

In recent times, RNN-based sequence-to-sequence attentional models have achieved good performance on abstractive summarization. However, numerous problems regarding repetition, incoherence, and exposure bias are encountered when applying these models. In this work, we propose a novel architecture that augments the standard sequence-to-sequence attentional model and a new training method combining reinforcement learning. We evaluate our proposed method on the CNN/Daily Mail dataset. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.
A New Approach for Predicting Antioxidant Property of Herbal Extracts

This study was conducted in order to determine if the herbal extract having antioxidant property. The major princible is to find out if there is any relationship between electrical current conductivity and antioxidant property of the ten Malaysian medicinal plants. The objective of this study achieved by measuring the antioxidant property of the herbal extracts, as well as to measure the ability of the herbal extracts to conduct an electrical current. Ten herbs were selected randomly, Strobilaunthus crispa, Pereskia sacharosa, Ehretia laevis, Plectranthus ambinicus, Orthosiphon staminous, Androghophis panculata, Persicaria odorata, Clinacantus nutans, Curry leaves and Frankincense. They were identified and prepared for extraction. The methanol extracts were obtained by ultrasonification extraction method. The antioxidant property was carried out using 1, 1-diphenyl-2-picrylhydrazy (DPPH) reagent. The pH was measured using a pH meter and the phytochemical elements were also tested. Finally the electrical current conductivity was measured using a basic electrical circuit connection and the calculations of each herbal extract were obtained. The results show that all the extracts have significant amount of electrical conductivity ranging from (0.06 – 0.12 Ω·cm). All the extracts consist of high antioxidant property and that these extracts contain abundant amount of elements. This study found that there is a relationship between the electrical current conductivity and the antioxidant property. This introduces a possible new tool, which is electrical current conductivity as a predictor for the antioxidant property.
Trust in Parliament: Trends Over Time

The study of trust as a political phenomenon or cause of concern runs back over two millennia. “When Tzŭ Kung asked what were the essentials of government, the Master replies: “Sufficient food, sufficient forces and the confidence of the people. ” (Confucius, c. 500 BC, p. 571 ). Yet, despite over two thousand years of research, investigations remain “controversial and often inconclusive” (Newton, 2007, p. 11).
FINANCING OF THE PROGRAMME TO COMBAT LAND DEGRADATION ON THE "PLATEAU ADJA" IN BENIN

Land degradation, caused by water and wind action and reinforced by human activities, reduces agricultural yield and income and lowers economic growth, especially in poor countries such as Benin . The objective of this research is to estimate the funding for the programme to combat land degradation on the Adja Plateau. The Contingent Valuation Method (CVM) was used to obtain the total amount of households’willingness to pay (WTP) using the probit model. Then the tobit model wastested using the two-stage method to control the sample selection bias of 267 households. The estimation resultsgive an average WTP of 1557 FCFA / month / hectare. For a cropping season of 201,739 hectares in 2019, the total WTP is estimated at 3,769,291,476 FCFA/year.
Industrial Policies, Patterns of Learning and Development: an Evolutionary Perspective

This work discusses the role of industrial policies within an evolutionary view of innovation and learning as drivers of economic development. Building on the notions of technological paradigms and trajectories, it links the processes of catching-up with the dynamics of capability accumulation within and across firms. In turn such processes are embedded in broader national systems of innovation wherein industrial policies play a pivotal role.
Inter-surface maps via constant-curvature metrics

We propose a novel approach to represent maps between two discrete surfaces of the same genus and to minimize intrinsic mapping distortion. Our maps are well-defined at every surface point and are guaranteed to be continuous bijections (surface homeomorphisms). As a key feature of our approach, only the images of vertices need to be represented explicitly, since the images of all other points (on edges or in faces) are properly defined implicitly. This definition is via unique geodesics in metrics of constant Gaussian curvature. Our method is built upon the fact that such metrics exist on surfaces of arbitrary topology, without the need for any cuts or cones (as asserted by the uniformization theorem). Depending on the surfaces' genus, these metrics exhibit one of the three classical geometries: Euclidean, spherical or hyperbolic. Our formulation handles constructions in all three geometries in a unified way. In addition, by considering not only the vertex images but also the discrete metric as degrees of freedom, our formulation enables us to simultaneously optimize the images of these vertices and images of all other points.
Prototype for Indoor Air Quality Monitoring

The purpose of this thesis is to analyse data reliability collected by do-it-yourself indoor air quality monitoring solution and give a comparison overview with a commercial off-the-shelf product called “Smart Home Weather Station” by Netamo. As a result of this thesis a prototype was constructed that was able to collect and store real time readings of temperature, humidity, CO2 and air pressure. The prototype used ESP32 microcontroller together with 2 additional sensors SCD30 and BMP280. All data was stored in a cloud-based database using MQTT bridge for connection. Measured readings resembled reference data gathered by Netamo and despite minor offsets in the data the prototype was concluded reliable.
POY version 4: phylogenetic analysis using dynamic homologies

We present POY version 4, an open source program for the phylogenetic analysis of morphological, prealigned sequence, unaligned sequence, and genomic data. POY allows phylogenetic inference when not only substitutions, but insertions, deletions, and rearrangement events are allowed (computed using the breakpoint or inversion distance). Compared with previous versions, POY 4 provides greater flexibility, a larger number of supported parameter sets, numerous execution time improvements, a vastly improved user interface, greater quality control, and extensive documentation. We introduce POY’s basic features, and present a simple example illustrating the performance improvements over previous versions of the application.
The Winter’s Tale: Season of Birth Impacts on Children in China

This paper examines the effect of season of birth on height and cognitive and noncognitive skills of Chinese children. We find that the child’s season of birth has a significant impact on the height of girls aged less than 5 years in agricultural households: girls born in winter are 0.4 standard deviations shorter compared with girls born in other seasons. We find, however, that this relative height differential does not translate to deficits in cognitive and noncognitive skills when girls are adolescents aged 10–15 years. We show that compensating investments by parents, manifested through higher parental expectations regarding educational attainment for poorly endowed winter-born girls, may be an explanation for why the initial height disadvantage does not have lasting negative implications when girls are older.
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language

In recent years, sentiment analysis has gained popularity as it is essential to moderate and analyse the information across the internet. It has various applications like opinion mining, social media monitoring, and market research. Aspect Based Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sentiment expressed. Significant contributions have been made in ABSA, but this progress is limited only to a few languages with adequate resources. Telugu lags behind in this area of research despite being one of the most spoken languages in India and an enormous amount of data being created each day. In this paper, we create a reliable resource for aspect based sentiment analysis in Telugu. The data is annotated for three tasks namely Aspect Term Extraction, Aspect Polarity Classification and Aspect Categorisation. Further, we develop baselines for the tasks using deep learning methods demonstrating the reliability and usefulness of the resource.
Adding Types, But Not Tokens, Affects Property Induction

The extent to which we generalize a novel property from a sample of familiar instances to novel instances depends on the sample composition. Previous property induction experiments have only used samples consisting of novel types (unique entities). Because real-world evidence samples often contain redundant tokens (repetitions of the same entity), we studied the effects on property induction of adding types and tokens to an observed sample. In Experiments 1-3, we presented participants with a sample of birds or flowers known to have a novel property and probed whether this property generalized to novel items varying in similarity to the initial sample. Increasing the number of novel types (e.g., new birds with the target property) in a sample produced tightening, promoting property generalization to highly similar stimuli but decreasing generalization to less similar stimuli. On the other hand, increasing the number of tokens (e.g., repeated presentations of the same bird with the target property) had little effect on generalization. Experiment 4 showed that repeated tokens are encoded and can benefit recognition, but are subsequently given little weight when inferring property generalization. We modified an existing Bayesian model of induction (Navarro, Dry & Lee, 2012) to account for both the information added by new types and the discounting of information conveyed by tokens.
Extracting and Classifying Events from Social Media Posts for Life Story Generation

People use social media, in particular Facebook, to share stories about themselves and the things that interest them. However, a Facebook user’s posts by themselves cannot provide a concise narrative of events to tell a complete life story. Story generators can be designed to utilize these events extracted from posts that users share about themselves into a life story. Before this can be achieved, the story generator needs to be able to classify posts based on their textual content. Such techniques are already available in email, where it is possible to classify messages into categories. In this paper, we describe our approach in using automated classifiers to categorize a user’s posts, focusing on those that describe travelling, eating, dining and celebrating events. We then show the performance of our classifiers. Once events are identified, we extract details from these posts and store them into an event model. We end our paper with a short discussion of our story generation process that utilizes the topical and temporal relations that exist among the events.