Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Reduction of Variance and Overestimation of Deep Q-Learning

The breakthrough of deep Q-Learning on different types of environments revolutionized the algorithmic design of Reinforcement Learning to introduce more stable and robust algorithms, to that end many extensions to deep Q-Learning algorithm have been proposed to reduce the variance of the target values and the overestimation phenomena. In this paper, we examine new methodology to solve these issues, we propose using Dropout techniques on deep Q-Learning algorithm as a way to reduce variance and overestimation. We further present experiments on some of the benchmark environments that demonstrate significant improvement of the stability of the performance and a reduction in variance and overestimation.
The PGM-index

We present the first learned index that supports predecessor, range queries and updates within provably efficient time and space bounds in the worst case. In the (static) context of just predecessor and range queries these bounds turn out to be optimal. We call this learned index the Piecewise Geometric Model index (PGM-index). Its flexible design allows us to introduce three variants which are novel in the context of learned data structures. The first variant of the PGM-index is able to adapt itself to the distribution of the query operations, thus resulting in the first known distribution-aware learned index to date. The second variant exploits the repetitiveness possibly present at the level of the learned models that compose the PGM-index to further compress its succinct space footprint. The third one is a multicriteria variant of the PGM-index that efficiently auto-tunes itself in a few seconds over hundreds of millions of keys to satisfy space-time constraints which evolve over time across users, devices and applications. These theoretical achievements are supported by a large set of experimental results on known datasets which show that the fully-dynamic PGM-index improves the space occupancy of existing traditional and learned indexes by up to three orders of magnitude, while still achieving their same or even better query and update time efficiency. As an example, in the static setting of predecessor and range queries, the PGM-index matches the query performance of a cache-optimised static B+-tree within two orders of magnitude (83×) less space; whereas in the fully-dynamic setting, where insertions and deletions are allowed, the PGM-index improves the query and update time performance of a B+-tree by up to 71% within three orders of magnitude (1140×) less space.
Spectroscopy of the Young Stellar Association Price-Whelan 1: Origin in the Magellanic Leading Arm and Constraints on the Milky Way Hot Halo

We report spectroscopic measurements of stars in the recently discovered young stellar association Price-Whelan 1 (PW 1), which was found in the vicinity of the Leading Arm (LA) of the Magellanic Stream (MS). We obtained Magellan+MIKE high-resolution spectra of the 28 brightest stars in PW 1 and used The Cannon to determine their stellar parameters. We find that the mean metallicity of PW 1 is [Fe/H] = −1.23 with a small scatter of 0.06 dex and the mean RV is = 276.7 with a dispersion of . Our results are consistent in , , and [Fe/H] with the young and metal-poor characteristics (116 Myr and [Fe/H] = −1.1) determined for PW 1 from our discovery paper. We find a strong correlation between the spatial pattern of the PW 1 stars and the LA II gas with an offset of −10. °15 in and +1. °55 in . The similarity in metallicity, velocity, and spatial patterns indicates that PW 1 likely originated in LA II. We find that the spatial and kinematic separation between LA II and PW 1 can be explained by ram pressure from Milky Way (MW) gas. Using orbit integrations that account for the LMC and MW halo and outer disk gas, we constrain the halo gas density at the orbital pericenter of PW 1 to be and the disk gas density at the midplane at to be . We, therefore, conclude that PW 1 formed from the LA II of the MS, making it a powerful constraint on the MW–Magellanic interaction.
Optimizing target node set for the control energy of directed complex networks

The energy needed in controlling a complex network is a problem of practical importance. Recent works have focused on the reduction of control energy via strategic placement of driver nodes, or by decreasing the cardinality of nodes to be controlled. However, how target nodes are arranged with respect to control energy has yet been explored. Here, we propose an iterative method based on stiefel manifold optimization of selectable target node matrix to reduce control energy. We derived the matrix derivative gradient needed for the search algorithm in a general way, and searched for target nodes which result in reduced control energy, given that driver nodes placement is fixed. Our findings reveal that when the path distances from driver nodes to target nodes are minimised, control energy is optimal. We also applied the algorithm to various model and real networks. The simulation results show that when compared to heuristic selection strategies of choosing target nodes, the control energy is reduced by a few orders of magnitude. Our work may be applicable to a social network voter model, where we are interested in optimizing the control effort needed to influence a fractional number of individuals' opinions.
Geometric CP violation and flavour predictions from orbifold compactification

We propose a theory for fermion masses and mixings in which an $A_4$ family symmetry arises naturally from a six-dimensional spacetime after orbifold compactification. The flavour symmetry leads to the successful "golden quark-lepton unification formula and the possibility of "geometrical" CP violation. The model reproduces oscillation parameters with good precision, giving sharp predictions for the CP violating phases of quarks and leptons, in particular $\delta^\ell \simeq +268 ^\circ$. The effective neutrinoless double-beta decay mass parameter is also sharply predicted as $\langle m_{\beta\beta}\rangle \simeq 2.65\ meV$.
VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination

Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking different opinions. To deal with this problem, SNSs have to use a lot of resources including people to clean the aforementioned content. In this paper, we propose a supervised learning model based on the ensemble method to solve the problem of detecting hate content on SNSs in order to make conversations on SNSs more effective. Our proposed model got the first place for public dashboard with 0.730 F1 macro-score and the third place with 0.584 F1 macro-score for private dashboard at the sixth international workshop on Vietnamese Language and Speech Processing 2019.
Design Smell Analysis for Developing and Established Open Source Java Software

Software design smells are design attributes which violate the fundamental design principles. Design smells are a key cause of design debt. Although the activities of design smell identification and measurement are predominantly considered in current literature, those which identify and communicate which design smells occur more frequently in newly developing software and which ones are more dominant in established software have been studied to a limited extent. This research describes a mechanism for identifying the design smells that are more prevalent in developing and established software respectively. A tool is provided which is used for design smell detection by analyzing large volumes of source code. More specifically, 164,609 Lines of Code (LoC) and 5,712 class files of six developing and 244,930 LoC and 12,048 class files of five established open-source Java software are analyzed. Obtained results show that out of the 4,020 occurrences of smells that were made for nine preselected types of design smells, 1,643 design smells were detected for developing software, which mainly consisted of four specific types of smells. For established software, 2,397 design smells were observed which predominantly consisted of four other types of smells. The remaining design smell was equally prevalent in both developing and established software. Desirable precision values ranging from 72.9% to 84.1% were obtained for the tool.
Deep Transfer Learning for Source Code Modeling

In recent years, deep learning models have shown great potential in source code modeling and analysis. Generally, deep learning-based approaches are problem-specific and data-hungry. A challenging issue of these approaches is that they require training from scratch for a different related problem. In this work, we propose a transfer learning-based approach that significantly improves the performance of deep learning-based source code models. In contrast to traditional learning paradigms, transfer learning can transfer the knowledge learned in solving one problem into another related problem. First, we present two recurrent neural network-based models RNN and GRU for the purpose of transfer learning in the domain of source code modeling. Next, via transfer learning, these pre-trained (RNN and GRU) models are used as feature extractors. Then, these extracted features are combined into attention learner for different downstream tasks. The attention learner leverages from the learned knowledge of pre-trained models and fine-tunes them for a specific downstream task. We evaluate the performance of the proposed approach with extensive experiments with the source code suggestion task. The results indicate that the proposed approach outperforms the state-of-the-art models in terms of accuracy, precision, recall and F-measure without training the models from scratch.
Carbon-deficient Red Giants

Carbon-deﬁcient red giants (CDRGs) are a rare class of peculiar red giants, also called “weak G-band” or “weak-CH” stars. Their atmospheric compositions show depleted carbon, a low 12 C / 13 C isotopic ratio, and an overabundance of nitrogen, indicating that the material at the surface has undergone CN-cycle hydrogen-burning. I present Str¨omgren uvby photometry of nearly all known CDRGs. Barium stars, having an enhanced carbon abundance, exhibit the “Bond-Neﬀ eﬀect”—a broad depression in their energy distributions at ∼ 4000 ˚A, recently conﬁrmed to be due to the CH molecule. This gives Ba II stars unusually low Str¨omgren c 1 photometric indices. I show that CDRGs, lacking CH absorption, exhibit an “anti-Bond-Neﬀ eﬀect”: higher c 1 indices than normal red giants. Using precise parallaxes from Gaia DR2, I plot CDRGs in the color-magnitude diagram (CMD) and compare them with theoretical evolution tracks. Most CDRGs lie in a fairly tight clump in the CMD, indicating initial masses in the range ∼ 2 to 3 . 5 M ⊙ , if they have evolved as single stars. It is unclear whether they are stars that have just reached the base of the red-giant branch and the ﬁrst dredge-up of CN-processed material, or are more highly evolved helium-burning stars in the red-giant clump. About 10% of CDRGs have higher masses of ∼ 4 to 4 . 5 M ⊙ , and exhibit unusually high rotational velocities. I show that CDRGs lie at systematically larger distances from the Galactic plane than normal giants, possibly indicating a role of binary mass-transfer and mergers. CDRGs continue to present a major puzzle for our understanding of stellar evolution.
Extremal digraphs on Meyniel-type condition for hamiltonian cycles in balanced bipartite digraphs

Let $D$ be a strong balanced digraph on $2a$ vertices. Adamus et al. have proved that $D$ is hamiltonian if $d(u)+d(v)\ge 3a$ whenever $uv\notin A(D)$ and $vu\notin A(D)$. The lower bound $3a$ is tight. In this paper, we shall show that the extremal digraph on this condition is two classes of digraphs that can be clearly characterized. Moreover, we also show that if $d(u)+d(v)\geq 3a-1$ whenever $uv\notin A(D)$ and $vu\notin A(D)$, then $D$ is traceable. The lower bound $3a-1$ is tight.
Design and Evaluation of Low-Cost and Energy-Efficient Magneto-Inductive Sensor Nodes for Wireless Sensor Networks

A set of low-cost and energy-efficient sensor nodes are designed and implemented using magnetic induction (MI) communications for wireless sensor networks (WSN), which are especially suited for underwater and underground networks where the conventional mode of communication does not perform well. Hardware features of the sensor nodes include a three-dimensional MI coil antenna and its different configurations for transmit and receive operations, low-power circuits for sleep mode, several types of sensors, data storage, and best transmit/receive circuits selected to achieve the maximum communication range with low-power consumption. The material cost of a sensor node is less than <inline-formula><tex-math notation="LaTeX">$\$$</tex-math></inline-formula>100 USD at the prototyping stage and can be drastically reduced at volume production. Software design utilizes low-power modes of microcontrollers and power supply circuits, state machine implementation of sleep, receive, sensing, and transmit modes, range estimation from received signal strength indicators (RSSIs), and medium access protocols, such as carrier sense multiple access (CSMA). Extensive lab and field tests conducted with the sensor nodes demonstrate promising performance in terms of low-power consumption, communication range, range estimation, and robustness against mismatching of coil orientations, and networking capabilities. The current consumption is as low as 60 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>A in sleep mode, 0.49 <inline-formula><tex-math notation="LaTeX">$\text{m}$</tex-math></inline-formula>A in receive mode, and 253 <inline-formula><tex-math notation="LaTeX">$\text{m}$</tex-math></inline-formula>A in transmit mode. The sensor node achieves a maximum of 40-m range with 1 kb/s data rate at 125 kHz carrier frequency.
Driver Detection Based on Deep Learning

Driver detection is an essential part of monitoring and identifying drivers' bad driving behavior and preventing traffic accidents. The complicated background of the cockpit, the different light and dark light and the changeable driver pose bring great difficulties to the driver detection. In this paper, a driver detection algorithm based on improved Faster R-CNN is proposed based on RGB images captured by vehicle-mounted cameras. The residual structure is introduced into the ZF network to design the ZF-Net network, in order to improve the accuracy and maintain the real-time performance. LRN (Local Response Normalization) is replaced by BN (Batch Normalization), which simplifies parameter adjustment and accelerates network convergence. Experiments on a self-built driver image database demonstrate the effectiveness of the driver detection method.
Performance Analysis of a Full-Duplex Energy Harvesting Cognitive Wireless Network

The performance of a full-duplex cooperative cognitive network is investigated under Nakagami-m fading. The secondary source and the relays are energy-constrained nodes and harvest energy from the primary network to transmit their information. The direct link between source and destination in the secondary network is considered as useful information at destination where joint-decoding is applied upon the signals received from source and relay. We derive an approximated closed-form expression for the outage probability of the proposed scheme. Results show that the proposed scheme outperforms the half-duplex and direct transmission schemes in the particular scenarios under consideration. Moreover, the performance in terms of outage probability of the proposed scheme is further improved through the use of multiple relays.
Traitement de l’hypertension artérielle pulmonaire

La presente invention concerne l’utilisation du 4-(4-methylpiperazin-1-yl-methyl)-N-[4-methyl-3-(4-pyridin-3-yl)pyrimidin-2-ylamino)phenyl]-benzamide ou de l’un de ses sels pharmaceutiquement acceptables ou d’un pyrimidylaminobenzamide de formule I dans laquelle les radicaux et les symboles sont tels que definis dans ce document, ou de l’un de ses sels pharmaceutiquement acceptables, pour la fabrication d’un medicament destine au traitement de l’hypertension arterielle pulmonaire (HAP), specialement chez les patients pour lesquels un traitement anterieur de la HAP a echoue.
Segmentation of Total Cell Area in Brightfield Microscopy Images

Segmentation is one of the most important steps in microscopy image analysis. Unfortunately, most of the methods use fluorescence images for this task, which is not suitable for analysis that requires a knowledge of area occupied by cells and an experimental design that does not allow necessary labeling. In this protocol, we present a simple method, based on edge detection and morphological operations, that separates total area occupied by cells from the background using only brightfield channel image. The resulting segmented picture can be further used as a mask for fluorescence quantification and other analyses. The whole procedure is carried out in open source software Fiji.
Performance Analysis of FSO under Turbulent Channel Using OSTBC

Free Space Optics (FSO) plays a vital role in modern wireless communications due to its advantages over fiber optics and RF techniques where a transmission of huge bandwidth and access to remote places become possible. The specific aim of this research is to analyze the Bit-Error Rate (BER) for FSO communication system when the signal is sent the over medium of turbulence channel, where the fading channel is described by the Gamma-Gamma model. The signal quality is improved by using Optical Space-Time Block- Code (OSTBC) and then the BER will be reduced. Optical 2×2 Alamouti scheme required 14 dB bit energy to noise ratio (Eb/N0) at 10-5 bit error rate (BER) which gives 3.5 dB gain as compared to no diversity scheme. The results show that using Multiple-Input-Multiple-Output (MIMO) technique represented by Alamouti scheme gives the improved BER performance as compared with no diversity (Single-Input-Single-Output (SISO)) technique.
How to motivate consumers to test electric vehicles : An approach to design consumer interfaces to foster innovation adoption

Electric mobility is a current example of an innovative technology facing innovation resistance by consumers despite being an important strategy towards sustainable mobility. In this study the design of a consumer web interface incorporating elements from behavior change research to support overcoming resistance towards innovations, is introduced. An experimental study is conducted to test the influence of the proposed design strategies that are implemented in a rental car booking website. Results show a significantly positive effect of leveraging social influence and providing targeted information about the innovation on consumers’ decision to test an innovation.
Human Gait Recognition Based on Deterministic Learning and Data Stream of Microsoft Kinect

Gait is an important biometric technology for human identification at a distance. This study focuses on gait features obtained by Microsoft Kinect and proposes a new model-based gait recognition method by combining deterministic learning theory and the data stream of Kinect. Deterministic learning theory is employed to capture the gait dynamics underlying Kinect-based gait parameters. Spatial–temporal gait features can be represented as the gait dynamics underlying the trajectories of spatial–temporal parameters, which can implicitly reflect the temporal changes of silhouette shape. Kinematic gait features can be represented as the gait dynamics underlying the trajectories of kinematic parameters, which can represent the temporal changes of body structure and dynamics. Both spatial–temporal and kinematic cues can be used separately for gait recognition using the smallest error principle. They are fused on the decision level to improve the gait recognition performance. Additionally, we discuss how to eliminate the effect of view angle on the proposed method. The experimental results indicate that encouraging recognition accuracy can be achieved.
Analyse exploratoire des génomes bactériens

Cette these porte sur l'analyse exploratoire in silico des genomes bacteriens et plus particulierement sur plusieurs aspects de l'organisation et de l'evolution des chromosomes bacteriens. Le memoire est divise en quatre parties. La premiere vise a realiser un etat de l'art des recents developpements de la genomique et de l'apport de la bio informatique a la recherche en microbiologie. La deuxieme partie concerne notre travail sur la structure des chromosomes bacteriens. Nous avons etabli plusieurs regles concernant l'organisation des chromosomes bacteriens, et plus particulierement la distribution et la disposition relative des genes au long du chromosome. Nous y decrivons egalement les biais associes a la traduction chez bacillus subtilis, en relation avec l'usage du code, l'usage des oligonucleotides et des aminoacides et la stabilite de la structure du messager. Nous avons egalement etudie les biais associes a la replication dans plusieurs genomes et nous sommes arrives a la conclusion que ces biais sont qualitativement ubiquistes dans le monde bacterien. La troisieme partie de cette these concerne l'etude de l'evolution des chromosomes bacteriens. Nous commencons par decrire la distribution de longues repetitions dans les chromosomes bacteriens et leur role possible dans la dynamique des genomes. A la suite de ce travail nous proposons un modele de transfert horizontal de l'ADN (sans homologie) pour les bacteries competentes. Enfin, nous decrivons une etude de la distribution des sites de restriction dans plusieurs genomes de phages et de bacteries. Cette etude nous a amene a soutenir l'hypothese parasitaire pour les systemes de restriction par opposition a l'hypothese de defense communement admise. Enfin, le memoire se termine par la description des methodologies mathematiques et informatiques les plus importantes mises en oeuvre dans l'ensemble de ces travaux, notamment l'identification de mots biaises, l'identification de repetitions longues et l'analyse discriminante lineaire.
Multi-User Processing for Ray-Based Channels

The performance of linear multi-user multiple-input multiple-output (MU-MIMO) systems has been extensively studied for classical statistical channel models. In contrast, there is little analysis for ray-based models, which are physically motivated, feature prominently in standards and have been experimentally validated. Thus, we present a novel analytical framework for zero forcing (ZF) and maximal ratio combining (MRC) applicable to such models. Specifically, using a central result for averaging in the angular domain, we derive accurate expressions for ZF signal-to-noise ratio and MRC signal, interference and noise powers. The remarkably simple expressions offer the following insights into the effects of the propagation environment. While ZF is robust to parameters such as cluster and subray angular spreads, MRC interference is highly sensitive to them. We show that the performance scales linearly with the number of antennas, and that it degrades with narrow angular spreads and as the propagation moves toward the antenna end-fire. Finally, by evaluating the variance of the MRC interference, we observe that an approximation to the MRC SINR widely used for classical statistical models, is inaccurate in ray-based channels.