Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dual Process Reinforcement Learning Account for Sequential Decision Making and Skill Learning

It has been extensively argued that there are two distinct processes governing behaviour: Goal Directed and Habitual. There have been multiple attempts at establishing a mechanism that governs which behaviour should be in charge of making sequential decisions leading to a distant reward. This thesis is organized in three parts. First we explore a simple task of action selection in a rather complex biophysical model of Basal Ganglia. We implement a probabilistic inference task and show that the model learns to establish a representation of reward contingencies. We then implement a model of Parkinson’s disease for patients with and without medication as an exploration of how the disease and its drug might act in Basal Ganglia. Next, we move on to complex sequential tasks and present a framework that unifies three distinct dichotomies: Goal Directed versus Habitual behaviour, Explicit versus Implicit Learning and ModelBased versus Model-Free Reinforcement Learning. This framework suggests a hierarchical organization of each mechanism of the dichotomies in two forms: first, we suggest that the Goal-Directed controller plays a dominant role in behaviour which is then taken over by habitual behaviour across multiple trials as the chunk size increases. Second, we suggest that the most granular actions are executed habitually whereas more abstract level actions are goal directed. We suggest that a goal directed behaviour occurs with the engagement of attention as opposed to habitual behaviour which is more automatic, thereby linking the dichotomy to Explicit versus Implicit learning. We then present the idea that behaviour or execution can be organized in opposite directions to each other with attention playing a role of the switch. The final part is a computational implementation of the unified theory presented previously. We show that a hierarchical organization of Goal-Directed and Habitual Behaviour implemented using Model Based and Model Free Reinforcement Learning is successfully able to fare well against the respective pure forms and that such an organization is suitable for explaining motor skill acquisition. We present a possible functional network of interacting brain areas in the frontal cortex with the final values of states and actions represented in the striatum – following which the Basal Ganglia model explored earlier could be used to perform action selection. We implement the Parkinson’s model in simulations of a grid world and suggest a qualitative parallel with the observed literature.
Long-term avalanche hazard assessment with a Bayesian depth-averaged propagation model

Abstract While performing statistical–dynamical simulations for avalanche predetermination, a propagation model must reach a compromise between precise description of the avalanche flow and computation times. Crucial problems are the choice of appropriate distributions describing the variability of the different inputs/outputs and model identifiability. In this study, a depth-averaged propagation model is used within a hierarchical Bayesian framework. First, the joint posterior distribution is estimated using a sequential Metropolis–Hastings algorithm. Details for tuning the estimation algorithm are provided, as well as tests to check convergence. Of particular interest is the calibration of the two coefficients of a Voellmy friction law, with model identifiability ensured by prior information. Second, the point estimates are used to predict the joint distribution of different variables of interest for hazard mapping. Recent developments are employed to compute pressure distributions taking into account the rheology of snow. The different steps of the method are illustrated with a real case study, for which all possible decennial scenarios are simulated. It appears that the marginal distribution of impact pressures is strongly skewed, with possible high values for avalanches characterized by low Froude numbers. Model assumptions and results are discussed.
A Review on Big Data Management and NoSQL Databases in Digital Forensics

: The huge growth in the Internet and the emerging of the new web technologies and the trend toward come with a new challenges, new applications and new concepts of database such as NoSQL databases which is recently becomes a very popular as alternative to the relational databases mostly in dealing with large data which is one of the most common features of web today, in the providence of high availability and scalability to the distributed systems which need fast access time and also can’t tolerate any down time during failures and have been used heavily by the big organizations and web companies as Digital forensics data is much complex and heterogeneous in that it can be structured, unstructured and semi-structured data. Relational database management systems (RDBMS) typically expose a query interface based on SQL (Structured Query Language). The RDBMS are mainly for management of structured data and hard to scale out to the ever growing size of data sets. This paper reviews the features of NoSQL database technologies as an alternative to RDBMS for management of Big Data. It evaluates and performance of a RDBMS in comparison with two NoSQL database systems. Addressing the concepts of NoSQL, the movement, motivations and needs behind it, and reviews the types of NoSQL databases and the issues concerning to these databases mainly areas of application and the security issues compared with traditional relational databases.
Design of Ultra Wideband Band-pass (UWB) Filter Using Microstrip Line and Complementary Split Ring Resonators (CSRRs)

ABSTARCT In order to fulfill the need of miniaturized electronic filter that can be a part of automotive radar system is proposed in this paper. The suggested filter has two quarter wavelength long parallel coupled line sections with an attachment of Complimentary Split Ring Resonators (CSRRs) and stepped impedance open circuit stub which are connected in shunt at the center. This arrangement of components provides sharp selectivity for the range of 22 GHz to 29 GHz. Proposed filter is a combination of two already designed UWB filters with precised parameters. Therefore it is bound to have low insertion loss and better out of band performance.
Iris recognition with compact zero-crossing-based coding

We propose an iris recognition technique using transformation of the iris image into a binary sequence that represents zero-crossing points of the filtered image by way of Laplacian of Gaussians. Novel iris localization and occlusion detection methods are developed to transform the iris image into the sequence of 1D stripes. The proposed enrollment procedure includes an independent selection of iris stripes among a number of enrollment images to minimize the recognition errors. The eyeball spontaneous rotation is corrected at the verification stage. The methodology was tested with a local database of 180 different irises, revealing the EER (Equal Error Rate) at the level of 0.03%.
High Contribution of Sea Salt Aerosols on Atmospheric Particles Measured at an Urban Tropical Location in Reunion Island

PM10 was monitored during 2008-2011 period at LUT and BON, two urban tropical stations close to each other at Saint-Pierre city, in Reunion Island (south-western Indian Ocean). During the study period, notable PM10 concentrations are observed at BON close to the coasts. At LUT, a more inland site, the daily PM10 concentration range between 13 and 70 μg/m3. Importantly, the limit value for the protection of the human health is systematically exceeded at BON while it is never exceeded at LUT. Also, the quality objective (QO: 30 μg/m3, on annual average) and the limit value for the human health protection (LV: 40 μg/m3, on annual average) are exceeded at BON each year during 2008-2011, while at LUT no regulatory values are exceeded. Nitrogen dioxide, an atmospheric tracer of anthropogenic activities was also monitored at LUT and BON. The mean diurnal NO2 variation is of the same level and order of magnitude and exhibits a similar pattern at both stations suggesting that distinct sources influence the PM10 at LUT and at BON. Chemical analysis was performed on daily filters sampled in September-November 2011 at the two stations to determine the sea salt contribution on PM10 across Saint-Pierre city. It showed that the sea salt contribution to the PM10 is 55% at BON in September 2011. The sea salt particles are therefore the main cause for the exceedances of the regulatory values of PM10 recorded at BON. The results importantly suggest that the notable PM10 concentrations measured at this urban marine site might have some but minor impact on human health.
Attenuated Change Blindness for Exogenously Attended Items in a Flicker Paradigm

When two scenes are alternately displayed, separated by a mask, even large, repeated changes between the scenes often go unnoticed for surprisingly long durations. Change blindness of this sort is attenuated at “centres of interest” in the scenes, however, supporting a theory of change blindness in which attention is necessary to perceive such changes (Rensink, O'Regan, & Clark, 1997). Problems with this measure of attentional selection - via verbally described “centres of interest” - are discussed, including worries about describability and explanatory impotence. Other forms of attentional selection, not subject to these problems, are employed in a “flicker” experiment to test the attention-based theory of change detection. Attenuated change blindness is observed at attended items when attentional selection is realized via involuntary exogenous capture of visual attention - to late-onset items and colour singletons - even when these manipulations are uncorrelated with the loci of the changes, and are thus irrelevant to the change detection task. These demonstrations ground the attention-based theory of change blindness in a type of attentional selection which is understood more rigorously than are “centres of interest”. At the same time, these results have important implications concerning the nature of exogenous attentional capture.
A PREDICTIVE MODEL OF EDGE EFFECTS

Edge effects are among the most extensively studied ecological phenomena, yet we lack a general, predictive framework to understand the patterns and variability observed. We present a conceptual model, based on resource distribution, that predicts whether organismal abundances near edges are expected to increase, decrease, or remain unchanged for any species at any edge type. Predictions are based on whether resources are found predominantly in one habitat (decreased abundance in preferred habitat, increase in non-preferred), divided between habitats (predicts an increase near both edges), spread equally among habitats (predicts a neutral edge response), or concentrated along the edge (increase). There are several implications of this model that can explain much of the variability reported in the edge literature. For instance, our model predicts that a species may show positive, negative, and neutral responses, depending on the edge type encountered, which explains some intraspecific variability observed .
Polysemy and Homonymy and their Importance for the Study of Word Meaning

In this article we examine the concepts of polysemy and homonymy. After a broad overview of the topic we focus on the treatment of several examples in dictionaries and indicate how listing problems can arise. We look at how polysemy and honomymy are dealt with in Chinese a language rich in ambiguous words full of connotations and associations and we look at some of the ensuing problems facing Chinese dictionary writers and suggest a user friendly model for ambiguous lexical entries. We explore how several English words such as the polysemous preposition 'over' can be dealt with in terms of image schemas and how this indicates a structured system in the mental lexicon. Vyvyan Evan's treatment of 'time' is also examined, his argument as to how it might be arranged in semantic memory and his conclusions about the lexicon having systematic semantic structure. We conclude by conceding that more work is required before the issues addressed in this paper can be unambiguously resolved.
Antitrust: Commission fines Google €4.34 billion for illegal practices regarding Android mobile devices to strengthen dominance of Google's search engine

Commissioner Margrethe Vestager, in charge of competition policy, said: "Today, mobile internet makes up more than half of global internet traffic. It has changed the lives of millions of Europeans. Our case is about three types of restrictions that Google has imposed on Android device manufacturers and network operators to ensure that traffic on Android devices goes to the Google search engine. In this way, Google has used Android as a vehicle to cement the dominance of its search engine. These practices have denied rivals the chance to innovate and compete on the merits. They have denied European consumers the benefits of effective competition in the important mobile sphere. This is illegal under EU antitrust rules. "
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition

In this paper we propose an end-to-end trainable deep neural network model for egocentric activity recognition. Our model is built on the observation that egocentric activities are highly characterized by the objects and their locations in the video. Based on this, we develop a spatial attention mechanism that enables the network to attend to regions containing objects that are correlated with the activity under consideration. We learn highly specialized attention maps for each frame using class-specific activations from a CNN pre-trained for generic image recognition, and use them for spatio-temporal encoding of the video with a convolutional LSTM. Our model is trained in a weakly supervised setting using raw video-level activity-class labels. Nonetheless, on standard egocentric activity benchmarks our model surpasses by up to +6% points recognition accuracy the currently best performing method that leverages hand segmentation and object location strong supervision for training. We visually analyze attention maps generated by the network, revealing that the network successfully identifies the relevant objects present in the video frames which may explain the strong recognition performance. We also discuss an extensive ablation analysis regarding the design choices.
Deep Learning in Physical Layer Communications

DL has shown great potential to revolutionize communication systems. This article provides an overview of the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Therefore, we categorize the applications of DL in physical layer communications into systems with and without block structures. For DL-based communication systems with the block structure, we demonstrate the power of DL in signal compression and signal detection. We also discuss the recent endeavors in developing DL-based end-to-end communication systems. Finally, potential research directions are identified to boost intelligent physical layer communications. Introduction
Real-Time Millimeter-Wave MIMO Channel Sounder for Dynamic Directional Measurements

In this paper, we present a novel real-time multiple-input multiple-output (MIMO) channel sounder for the 28 GHz band. Until now, most investigations of the directional characteristics of millimeter-wave channels have used mechanically rotating horn antennas. In contrast, the sounder presented here is based on a phased array structure that performs fast electronic steering of the beams in the horizontal and vertical domains. This approach drastically shortens the measurement times for measurements that are directionally resolved both at the transmitter (TX) and the receiver (RX), and the measurement time per location is reduced from minutes or hours to milliseconds. This not only enables measurement of more TX–RX locations for a better statistical validity but also allows to perform directional analysis in dynamic environments. The sounder also has high phase stability, which, in conjunction with the short measurement time, leads to a low phase drift between TX and RX. This in turn enables phase-coherent sounding of all beam pairs even when TX and RX have no cabled connection for synchronization, and thus avoids any delay ambiguity. Furthermore, the phase stability over time enables complex RX waveform averaging to improve the signal-to-noise ratio during high path loss measurements. The paper discusses the system design as well as the measurements performed for verification of the sounder performance. Furthermore, we present sample results from double directional measurements in dynamic environments.
Document-level machine translation quality estimation

Assessing Machine Translation (MT) quality at document level is a challenge as metrics need to account for many linguistic phenomena on different levels. Large units of text encompass different linguistic phenomena and, as a consequence, a machine translated document can have different problems. It is hard for humans to evaluate documents regarding document-wide phenomena (e.g. coherence) as they get easily distracted by problems at other levels (e.g. grammar). Although standard automatic evaluation metrics (e.g. BLEU) are often used for this purpose, they focus on n-grams matches and often disregard document-wide information. Therefore, although such metrics are useful to compare different MT systems, they may not reflect nuances of quality in individual documents. Machine translated documents can also be evaluated according to the task they will be used for. Methods based on measuring the distance between machine translations and post-edited machine translations are widely used for task-based purposes. Another task-based method is to use reading comprehension questions about the machine translated document, as a proxy of the document quality. Quality Estimation (QE) is an evaluation approach that attempts to predict MT outputs quality, using trained Machine Learning (ML) models. This method is robust because it can consider any type of quality assessment for building the QE models. Thus far, for document-level QE, BLEU-style metrics were used as quality labels, leading to unreliable predictions, as document information is neglected. Challenges of document-level QE encompass the choice of adequate labels for the task, the use of appropriate features for the task and the study of appropriate ML models. In this thesis we focus on feature engineering, the design of quality labels and the use of ML methods for document-level QE. Our new features can be classified as document-wide (use shallow document information), discourse-aware (use information about discourse structures) and consensus-based (use other machine translations as pseudo-references). New labels are proposed in order to overcome the lack of reliable labels for document-level QE. Two different approaches are proposed: one aimed at MT for assimilation with a low requirement, and another aimed at MT for dissemination with a high quality requirement. The assimilation labels use reading comprehension questions as a proxy of document quality. The dissemination approach uses a two-stage post-editing method to derive the quality labels. Different ML techniques are also explored for the document-level QE task, including the appropriate use of regression or classification and the study of kernel combination to deal with features of different nature (e.g. handcrafted features versus consensus features). We show that, in general, QE models predicting our new labels and using our discourse-aware features are more successful than models predicting automatic evaluation metrics. Regarding ML techniques, no conclusions could be drawn, given that different models performed similarly throughout the different experiments.
Gonadal transcriptome sequencing of the critically endangered Acipenser dabryanus to discover candidate sex-related genes

Background Acipenser dabryanus, an endemic Chinese species, has been listed as a first-class protected animal in China. Sturgeons are among the oldest and most primitive group of existing fish in the world and occupy a special place in the evolutionary history of fish. Thus, a study of the reproduction and sex differentiation of sturgeon will be of great value for fish as well as the whole vertebrate group. Methods In this study, we conducted comparative analysis of the testes and ovaries transcriptomes of A. dabryanus to screen for sex-differentiation and sexual development-related genes. Results The transcriptome sequencing of six cDNA libraries generated 265 million clean reads, encompassing 79 Gb of sequences. The N50 and mean length of the identified 91,375 unigenes were 1,718 and 989 bp, respectively. A total of 6,306, 9,961, 13,170, 15,484, and 23,588 unigenes were annotated in the clusters of orthologous groups, gene ontology categories, Kyoto Encyclopedia of Genes and Genomes Pathway, euKaryotic orthologous groups, and NCBI non-redundant protein databases, respectively. A total of 5,396 differentially expressed genes were found between the two sexes, with 1,938 predicted to be up-regulated in ovaries and 3,458 in testes. A total of 73 candidate genes known to be involved in sex differentiation and sexual development were searched in the transcriptome of A. dabryanus of which 52 showed significant similarity. We highlighted six genes that are differentially expressed between the two sexes and may play important roles in sex differentiation and gonad maintenance. In addition, 24,271 simple sequence repeats (SSRs) and 550,519 single-nucleotide polymorphisms (SNPs) were detected. Discussion This work represents the first transcriptome study comparing the ovary and testis in A. dabryanus. The putative differentially expressed genes between the gonads provide an important source of information for further study of the sex-differentiation related genes and the sex-differentiation mechanism in sturgeons. The SSRs or SNPs identified in this study will be helpful in the discovery of sex-related markers in A. dabryanus.
Bullshit-sensitivity predicts prosocial behavior

Bullshit-sensitivity is the ability to distinguish pseudo-profound bullshit sentences (e.g. “Your movement transforms universal observations”) from genuinely profound sentences (e.g. “The person who never made a mistake never tried something new”). Although bullshit-sensitivity has been linked to other individual difference measures, it has not yet been shown to predict any actual behavior. We therefore conducted a survey study with over a thousand participants from a general sample of the Swedish population and assessed participants’ bullshit-receptivity (i.e. their perceived meaningfulness of seven bullshit sentences) and profoundness-receptivity (i.e. their perceived meaningfulness of seven genuinely profound sentences), and used these variables to predict two types of prosocial behavior (self-reported donations and a decision to volunteer for charity). Despite bullshit-receptivity and profoundness-receptivity being positively correlated with each other, logistic regression analyses showed that profoundness-receptivity had a positive association whereas bullshit-receptivity had a negative association with both types of prosocial behavior. These relations held up for the most part when controlling for potentially intermediating factors such as cognitive ability, time spent completing the survey, sex, age, level of education, and religiosity. The results suggest that people who are better at distinguishing the pseudo-profound from the actually profound are more prosocial.