Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Series Prediction of E-nose Sensor Drift Based on Deep Recurrent Neural Network

Chemical sensors drift is a slow change in sensitivity that occurs in time which makes it difficult to construct an appropriate sensor drift treatment. The main purpose of this paper is to study a new methodology for time series prediction of chemical sensors drift based on LSTM (long short-term memory, LSTM) recurrent neural network, including data preprocessing and partition, network and training, network prediction. This technique can mine the deep information of sensors drift signals instead of manual extraction and match the complex nonlinearity much more exactly. It is proved that the proposed LSTM prediction model can make long-term, accurate prediction of chemical sensors baseline and drift.
Pining Control Algorithm for Complex Networks

In this paper, we quote the concept of resistance distance for Pinning control, and discuss the relationship between the upper bound of the minimum eigenvalue of the grounded Laplacian Matrix and the resistance distance of the Pinning control for one single node. Our simulation results in various networks like BA network, ER network, NW network etc. show that the ranking of the minimum eigenvalue of the grounded Laplacian Matrix is highly consistent with the ranking of resistance distance, much better than the degree ranking, which provides us another approach to estimate the node for Pinning control. In the case of controlling multiple $(l)$ nodes, we use the iterative algorithm to calculate the grounded Laplacian Matrix for better combination of the controlled nodes, through which the computation of NP-hard problem of controlling l nodes can be greatly reduced, and the effect of the iterative algorithm are presented by simulation calculations in different networks, and the smaller l the better effect.
Non-Linear Energy Harvesting Based Cooperative Spectrum Sharing Networks

In this paper, we investigate the performance of a non-linear energy harvesting based overlay spectrum sharing system. Herein, an energy-constrained secondary node acts as a cooperative relay to assist the transmission of primary user. In return for the benign cooperation, the secondary node enjoys the access to primary user’s spectrum for its own information transfer. Specifically, the secondary node first harvests the energy from the received radio-frequency signal of primary transmitter during a dedicated energy harvesting phase. In the subsequent information transfer phase, secondary node splits the harvested power to forward the primary data as well as its own information intended for another secondary user. For this analytical framework, we derive the expressions of outage probability for both primary and secondary networks. Numerical and simulation results are obtained to extract various useful insights and to validate our theoretical developments.
A Distributed Approach to Determine Acronym and Expansion Pairs using Apache Spark

We evaluate the performance of a distributed approach to recognize acronym and expansion pairs using Apache Spark. We train and evaluate the K-NN, Parallel SVM, and Decision Tree algorithms using Apache Spark and compare the performance of the three algorithms with SVM Light model, developed and trained on a single machine without using Spark. The MapReduce paradigm was implemented using Java programming and thus, no wrapping library such as Hadoop-Streaming Library was needed. The results reveal that the accuracy of the newly trained models on Spark is excellent, it gets around 99% for the training phase and around 98% for the testing phase. An additional pruning approach was also introduced and it successfully reduced the number of candidate of acronym and expansion pairs to approximately 13.3%.
Cyber Exposed: The Social Networks And The Life Satisfaction

The present study aims to identify the predictive relationships between the exhibition in the social networks, the self-esteem and the life satisfaction in Peruvian adolescents. The participants were 102 students (71 men (86.9 %) and 31 women (30.1 %) between 14 to 19 years of age $(\pmb{M}=\mathbf{16.93},\ \pmb{SD} =\pmb{.89})$. One concludes that to exhibit the personal life in social networks on the part of the adolescents predicts in a positive form the publications in social networks of the parents, also he predicts the self-esteem and the satisfaction with the life. Furthermore, the self-esteem predicts the life satisfaction. Finally, the mediation model confirmed of significant form with the Test of Sobel $(\mathbf{Test\ Statistic} =\mathbf{1.97},\ \pmb{SE}=\mathbf{.06},\ \pmb{p} < \mathbf{.05})$ that the exhibition in social networks on behalf of the adolescents (variable independent) predicts the life satisfaction (dependent variable) when mediated by self-esteem (mediating variable).
Contraction force versus action potential duration of cardiomyocytes: which methodology is more informative for selection of effective 1,4-dihydropyridine compounds in experimental research?

Background. Identification of activity properties of new synthesized compounds is important to help choose the adequate research methodology. The goal of this experimental research was to determine the relationship between the chemical structure of 25 compounds of 1,4-dihydropyridine derivatives and their effects on the contraction force of guinea pig papillary muscles and the action potential (AP) duration. Methods. AP recordings were obtained with standard microelectrodes that were made from borosilicate glass capillaries, filled with 2.5 M KCl and connected to the high-input impedance amplification system. 1.0 Hz stimulation frequency was used. The contraction of papillary muscles was recorded by using a force transducer. Both signals (inotropic response and AP) were digitised by an A/D converter and registered by a computer specialised program. Results. The results have shown that most of 1,4-dihydropyridine derivatives possessed the negative inotropic action and had the negligible impact on the AP duration. It was identified that 1,4-dihydropyridine compound OSI 9719 (2-propoxyethyl-4-difloemethoxyphenyl-2-methyl-5-nitro-1,4-dihydro­pyridine-3carbo­xylate) had a prolonged duration of AP and simultaneously increased the force of contraction (p < 0.05). It was shown that the efficiency of 1,4-dihydropyridine derivatives depends on the nature (diflormethoxy-, propoxy-, ethoxycarbonyl-, amine, atom chlorine, ethylene glycol-, isopropoxy-) of the substituents and their spatial isomerisation in the phenyl and 1,4-dihydropyridine rings and the concentration of the compounds. Conclusion. Evaluation of the contraction force of guinea pig papillary muscles is a more informative method than evaluation of the action potential duration, conducting experimental research of the activity properties of 1,4-dihydropyridine derivatives.
Establishing an evaluation metric to quantify climate change image realism

With success on controlled tasks, deep generative models are being increasingly applied to humanitarian applications (Nie et al 2017 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (Berlin: Springer) pp 417–25, Yanardag et al 2017 Deep Empathy). In this paper, we focus on the evaluation of a conditional generative model that illustrates the consequences of climate change-induced flooding to encourage public interest and awareness on the issue. Because metrics for comparing the realism of different modes in a conditional generative model do not exist, we propose several automated and human-based methods for evaluation. To do this, we adapt several existing metrics and assess the automated metrics against gold standard human evaluation. We find that using Fréchet Inception Distance with embeddings from an intermediary Inception-v3 layer that precedes the auxiliary classifier produces results most correlated with human realism. While insufficient alone to establish a human-correlated automatic evaluation metric, we believe this work begins to bridge the gap between human and automated generative evaluation procedures, and to generate more realistic images of the future consequences of climate change.
The First Billion Years project: Finding infant globular clusters at z = 6

Aims. We aim to conduct an assessment of the demographics of substructures in cosmological simulations to identify low-mass stellar systems at high redshift, with a particular focus on globular cluster (GC) candidates. Methods. We explored a suite of high-resolution cosmological simulations from the First Billion Years Project (FiBY) at z ≥ 6. All substructures within the simulations have been identified with the SUBFIND algorithm. From our analysis, two distinct groups of objects emerge. We hypothesise that the substructures in the first group, which appear to have a high baryon fraction (fb ≥ 0.95), are possible infant GC candidates. Objects belonging to the second group have a high stellar fraction (f* ≥ 0.95) and show a potential resemblance to infant ultra-faint dwarf galaxies. Results. The high baryon fraction objects identified in this study are characterised by a stellar content similar to the one observed in present-day GCs, but they still contain a high gas fraction (fgas ∼ 0.95) and a relatively low amount of dark matter. They are compact systems, with densities higher than the average population of FiBY systems at the same stellar mass. Their sizes are consistent with recent estimates based on the first observations of possible proto-GCs at high redshifts. These types of infant GC candidates appear to be more massive and more abundant in massive host galaxies, indicating that the assembly of galaxies via mergers may play an important role in building several GC-host scaling relations. Specifically, we express the relation between the mass of the most massive infant GC and its host stellar mass as log(Mcl) = (0.31 ± 0.15) log (M*, gal + (4.17 ± 1.06). We also report a new relation between the most massive infant GC and the parent specific star formation rate of the form log(Mcl) = (0.85 ± 0.30) log (sSFR)+α that describes the data at both low and high redshift. Finally, we assess the present-day GC mass (GC number) – halo mass relation offers a satisfactory description of the behaviour of our infant GC candidates at high redshift, suggesting that such a relation may be set at formation.
Mask Combination of Multi-Layer Graphs for Global Structure Inference

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a data space whose inner relationships are encoded by multi-layer graphs. We aim at properly exploiting the information originating from each layer to infer the global structure underlying the signals. We thus present a novel method for combining the multiple graphs into a global graph using mask matrices, which are estimated through an optimization problem that accommodates the multi-layer graph information and a signal representation model. The proposed mask combination method also estimates the contribution of each graph layer in the structure of signals. The experiments conducted both on synthetic and real-world data suggest that integrating the multi-layer graph representation of the data in the structure inference framework enhances the learning procedure considerably by adapting to the quality and the quantity of the input data.
Giant Osteoma of the Frontoethmoidal Sinus: A Case Report

Giant or large osteomas of the skull are uncommon. This is a report of a 14-year old girl presenting with a giant osteoma of the frontoethmoidal sinus. The osteoma was completely excised at limited craniectomy. There has been no recurrence at 2 years of follow up. (Nig J Surg Res 2001; 3: 188 – 190) KEY WORDS: Skull, Giant Osteoma
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri. org/nlp4if-shared-task/.
Exploiting Data Skew for Improved Query Performance

Analytic queries enable sophisticated large-scale data analysis within many commercial, scientific and medical domains today. Data skew is a ubiquitous feature of these real-world domains. In a retail database, some products are typically much more popular than others. In a text database, word frequencies follow a Zipf distribution with a small number of very common words, and a long tail of infrequent words. In a geographic database, some regions have much higher populations (and therefore data measurements) than others. Current systems do not make the most of caches for exploiting skew. In particular, a whole cache line may remain cache resident even though only a small part of the cache line corresponds to a popular data item. In this article, we propose a novel index structure for repositioning data items to concentrate popular items into the same cache lines. The net result is better spatial locality, and better utilization of limited cache resources. We develop a theoretical model for analyzing the cache utilization, and implement database operators that are efficient in the presence of skew. Our experimental evaluation on real and synthetic data shows that exploiting skew can significantly improve in-memory query performance. In some cases, our techniques can speed up queries by over an order of magnitude.
Vehicle Detection and Counting from VHR Satellite Images: Efforts and Open Issues

Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learning-based models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO, You Only Look Once) architectures were investigated. These networks were adapted, trained and validated on a data set including 87k vehicles, annotated using an interactive semi-automatic tool developed by the authors. Experimental results show that both segmentation and detection models could achieve a precision rate higher than 85 % with a recall rate also high (76.4 % and 71.9 % for Tiramisu and YOLO respectively).
New RLL Code with Improved Error Performance for Visible Light Communication

In this letter, a novel run-length limited (RLL) code is reported. In addition to providing a strict DC-balance and other relevant features for visible light communication (VLC) applications, the proposed 5B10B code presents enhanced error correction capabilities. Theoretical and simulation results show that the proposed code outperforms its standard enforced counterparts in terms of bit error ratio while preserving low-complexity requirements.
Characterisation of geodesic self-dual regular surface triangulations

We consider triangulations of closed surfaces in which every vertex is incident to exactly $d$ edges. These triangulations can be identified with subgroups of the triangle group $\langle a,b,c\mid a^2,b^2,c^2,(ab)^3,(ac)^2,(bc)^d\rangle$ that intersect $\langle a,b\rangle$, $\langle a,c\rangle$, and $\langle b,c\rangle$ trivially. The term geodesic duality refers to an external symmetry introduced by Wilson in 1979. Our main result is the characterisation of all subgroups corresponding to geodesic self--dual regular triangulations, together with a complete enumeration for $d < 10$.
Neutron stars: New constraints on asymmetric dark matter

We study an impact of asymmetric dark matter on properties of the neutron stars and their ability to reach the two solar masses limit, which allows us to present a new range of masses of dark matter particles and their fractions inside the star. Our analysis is based on the observational fact of the existence of three pulsars reaching this limit and on the theoretically predicted reduction of the neutron star maximal mass caused by the accumulation of dark matter in its interior. We also demonstrate that light dark matter particles with masses below 0.2 GeV can create an extended halo around the neutron star leading not to decrease, but to increase of its visible gravitational mass. By using recent results on the spatial distribution of dark matter in the Milky Way, we present an estimate of its fraction inside the neutron stars located in the Galaxy center. We show how the detection of a 2Msun neutron star in the most central region of the Galaxy will impose an upper constraint on the mass of dark matter particles of ~ 60 GeV. Future high precision measurements of the neutron stars maximal mass near the Galactic center, will put a more stringent constraint on the mass of the dark matter particle. This last result is particularly important to prepare ongoing, and future radio and X-ray surveys.
Linear Mixed Models for Comparing Dynamic Treatment Regimens on a Longitudinal Outcome in Sequentially Randomized Trials.

A dynamic treatment regimen (DTR) is a pre-specified sequence of decision rules which maps baseline or time-varying measurements on an individual to a recommended intervention or set of interventions. Sequential multiple assignment randomized trials (SMARTs) represent an important data collection tool for informing the construction of effective DTRs. A common primary aim in a SMART is the marginal mean comparison between two or more of the DTRs embedded in the trial. This manuscript develops a mixed effects modeling and estimation approach for these primary aim comparisons based on a continuous, longitudinal outcome. The method is illustrated using data from a SMART in autism research.
Energy-Efficient Resource Allocation for Secure NOMA-Enabled Mobile Edge Computing Networks

Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.
Kernel Graph Attention Network for Fact Verification

This paper presents Kernel Graph Attention Network ( KGAT ), which conducts more ﬁne-grained evidence selection and reasoning for the fact veriﬁcation task. Given a claim and a set of potential supporting evidence sentences, KGAT constructs a graph attention network using the evidence sentences as its nodes and learns to verify the claim integrity using its edge kernels and node kernels, where the edge kernels learn to propagate information across the evidence graph, and the node kernels learn to merge node level information to the graph level. KGAT reaches a comparable performance (69.4%) on FEVER, a large-scale benchmark for fact veriﬁcation. Our experiments ﬁnd that KGAT thrives on veriﬁcation scenarios where multiple evidence pieces are required. This advantage mainly comes from the sparse and ﬁne-grained attention mechanisms from our kernel technique.
Sequential metric dimension for random graphs

Abstract In the localization game on a graph, the goal is to find a fixed but unknown target node $v^\star$ with the least number of distance queries possible. In the jth step of the game, the player queries a single node $v_j$ and receives, as an answer to their query, the distance between the nodes $v_j$ and $v^\star$ . The sequential metric dimension (SMD) is the minimal number of queries that the player needs to guess the target with absolute certainty, no matter where the target is. The term SMD originates from the related notion of metric dimension (MD), which can be defined the same way as the SMD except that the player’s queries are non-adaptive. In this work we extend the results of Bollobás, Mitsche, and Prałat [4] on the MD of Erdős–Rényi graphs to the SMD. We find that, in connected Erdős–Rényi graphs, the MD and the SMD are a constant factor apart. For the lower bound we present a clean analysis by combining tools developed for the MD and a novel coupling argument. For the upper bound we show that a strategy that greedily minimizes the number of candidate targets in each step uses asymptotically optimal queries in Erdős–Rényi graphs. Connections with source localization, binary search on graphs, and the birthday problem are discussed.
Deformed covariance in spherically symmetric vacuum models of loop quantum gravity: Consistency in Euclidean and self-dual gravity

Different versions of consistent canonical realizations of hypersurface deformations of spherically symmetric space-times have been derived in models of loop quantum gravity, modifying the classical dynamics and sometimes also the structure of space-time. Based on a canonical version of effective field theory, this paper provides a unified treatment, showing that modified space-time structures are generic in this setting. The special case of Euclidean gravity demonstrates agreement also with existing operator calculations.
Towards Best Practice in Explaining Neural Network Decisions with LRP

Within the last decade, neural network based predictors have demonstrated impressive — and at times superhuman — capabilities. This performance is often paid for with an intransparent prediction process and thus has sparked numerous contributions in the novel field of explainable artificial intelligence (XAI). In this paper, we focus on a popular and widely used method of XAI, the Layer-wise Relevance Propagation (LRP). Since its initial proposition LRP has evolved as a method, and a best practice for applying the method has tacitly emerged, based however on humanly observed evidence alone. In this paper we investigate — and for the first time quantify — the effect of this current best practice on feedforward neural networks in a visual object detection setting. The results verify that the layer-dependent approach to LRP applied in recent literature better represents the model’s reasoning, and at the same time increases the object localization and class discriminativity of LRP.