Miracle-dz/UniFAR-FTUs huggingface dataset:
Islamist Terrorism, Diaspora Links and Casualty Rates

We explore the widespread belief that Islamist groups are especially violent and also consider the possibility that links with transnational diasporas may reduce casualties by Islamist terrorist organizations. Analyzing between 77,000 and 82,000 terrorist incidents from 1970 to 2016, we find that attacks by Islamist groups produce fewer casualties than attacks by non-Islamist religious groups. We also find that Islamist groups with links to diaspora kin abroad commit lower casualty terrorist attacks. We explain these findings by arguing that Islamic diaspora communities impose tactical restraints on terrorist organizations with which they are linked. Diasporas constitute a major political audience and source of support for terrorist organizations and provide an audience outside the homeland that may be negatively affected by extremely violent attacks.
DECISION-MAKING IN UNCERTAINTY-INFUSED LEARNING SITUATIONS WITH EXPERIMENTS IN PHYSICS CLASSES

Evaluating experimental data calls for dealing with measurement uncertainty (MU); this is often reported as a learning obstacle to scientific concepts in lab work. We report on a study of 1,545 secondary school students who evaluated self-collected data in a physics lab. Results reveal a) that students are aware of MUs depending on data variability and b) that the odds of framing a correct hypothesis after the experiment decrease (OR=0.66[0.57;0.76], p<.001) when students are aware of MUs in their decision-making process. This contradicts studies that identified students' lack of awareness of MUs as an obstacle in the evaluation of experimental data, and it could be interpreted to mean that learners decide conservatively when evaluating uncertainty-infused learning situations because of a lack of knowledge about MUs.
Clustering of documents from a two-way viewpoint

Methods for high-dimensional data clustering represents a prolific research area in data mining, encouraging a large quantity of provisional solutions. In text mining and in the analysis of gene expression data, the idea of bidimensional clustering arose, in the sense of finding clusters of documents characterized by cluster of terms (and analogously, clusters of genes and clusters of different experimental conditions). Although we are often more interested in clustering one way of our data structure, however co-clustering seems to be convenient (both from an interpretative and a computational viewpoint). Here we try to frame the problem in a multidimensional data analysis perspective, referring to classic association and/or prediction indexes for contingency tables. Following previous works, we propose the use of a predictability index, Goodman and Kruskal τb, dealing with documentsby-terms tables. After a quick review of the wide literature related to two-way clustering, mainly developed in microarray analysis, we propose a new algorithm belonging to the genetic family, based on the optimization of the predictability index τb. We present the results of our proposal applied to well-known-in-literature data sets to test the effectiveness of our co-clustering algorithm in practice.
EURASIAN JOURNAL OF ECONOMICS AND FINANCE

This paper investigates the impacts of urbanization on the productivity of informal household businesses in urban places of Vietnam. Urbanization externalities are measured by the diversity of industry, which are disaggregated from formal and informal sources, and city size. Results from the paper show that informal household businesses in urban places benefit from clustering with formal enterprises but not from informal firms in other industries. Furthermore, the concentration in large and dense urban areas does not bring benefits to informal household businesses. The results call for more attention on the urban planning towards the informal sector in urban places of Vietnam.
Improving Learning-based Ego-motion Estimation with Homomorphism-based Losses and Drift Correction

Visual odometry is an essential problem for mobile robots. Traditional methods for solving VO mostly utilize geometric optimization. While capable of achieving high accuracy, these methods require accurate sensor calibration and complicated parameter tuning to work well in practice. With the rise of deep learning, there has been increased interest in the end-to-end, learning-based methods for VO, which have the potential to improve robustness. However, learning-based methods for VO so far are less accurate than geometric methods. We argue that one of the main issues is that the current ego-motion estimation task is different from other problems where deep learning has been successful such as object detection. We define a novel cost function for learning-based VO considering the mathematical properties of the group homomorphism. In addition to the standard L2 loss, we incorporate losses based on the identity, inverse and closure properties of SE(3) rigid motion. Furthermore, we propose to reduce the VO drift by estimating the drivable regions using semantic segmentation and incorporate this information into a pose graph optimization. Experiments on KITTI datasets show that the novel cost function can improve ego-motion estimation compared to the state-of-the-art and the drivable region-based correction further reduces the VO drift.
Coordination of Care for Homeless Individuals with Comorbid Severe Mental Disorders and Substance-Related Disorders

ABSTRACT Psychiatric symptoms and alcohol and drug use disorders are often diagnosed in individuals who frequent shelters and programs for homelessness. Services are often provided in a fragmented, uncoordinated manner. This study evaluates a care coordination program in dual-diagnosis patients by comparing data on 50 patients treated with standard methods and 50 patients enrolled in the care coordination program. Clinical outcomes were measured with the Hamilton Depression Scale, Positive and Negative Syndrome scale, BASIS-32, service utilization, and alcohol use. We found that care coordination is a relatively low intensity but promising intervention that may improve clinical outcomes of homeless dual-diagnosis patients.
Query suggestions with global consistency on user click graph

概要 Users express their information needs in terms of queries in search engines to find some relevant documents on the Internet. However, users’ search queries are usually short, ambiguous and/or underspecified. Sometimes, users have been found to struggle formulating queries based on keywords given their limited vocabulary. To help users in formulating query, query suggestion by mining query logs plays an important role and has been attracted attention in the recent years. A query log is generally represented as a bipartite graph on a query set and a URL set. Most traditional approaches used the raw click frequency to weigh the link between a query and a URL on the click graph. In order to alleviate the spurious effects of raw click frequency, some entropy-biased model by incorporating raw click frequency with the inverse query frequency or inverse URL frequency was proposed as the weighting scheme for query representation. In this paper, we observe that popular query and URLs are very diverse in nature, and user click frequency can be considered as local property of the URL, and link structures of query and URLs from both sides of the bipartite graph can be considered as a global property on the click graph. Based on this understanding, we develop a weighting scheme to weight the link between a query and an URL in the bipartite click graph by incorporating the user click frequency, and the link structures of the query and URL from both sides with global consistency in a consistent manner. We conduct experiments on the AOL search engine query log dataset and evaluate the query suggestions by estimating the similarity between the user query and suggested query using the knowledge of the Dmoz open directory project. The results turns out that our global consistency scheme achieved better performance than the current entropy-biased model.
A Comparison of Syntactic Context Models

Distributional models of lexical semantics identify semantically similar words through contextual similarity. Previous studies have shown that syntactic contexts are especially good at finding (near) synonyms. In this paper, we compare models based on eight different syntactic dependency relations and we evaluate their separate and combined performance on a test set of Dutch nouns. Firstly, we analyze to what extent their results overlap. Secondly, we assess the overall performance of the models by looking at the average similarity of the words they return. And thirdly, we compare the specific semantic relations retrieved by the models. The analyses show that although models based on the subject and object relation give the most consistent results, it is the model based on adjective modification that gives the best results. It even outperforms the combined model at finding true synonyms.
The Effects of Long-Term Ubiquinol Intake on Improving the Quality of Life of Community Residents

Background: Ubiquinol is widely used as an anti-aging supplement. In this study, we evaluated the effects of long-term ubiquinol intake on improving or maintaining the quality of life (QOL) of community residents. Methods: 124 adult (aged 22-86) residents (36 male, 88 female) participated in this trial. Each consumed 100-120 mg of ubiquinol per day for 6 months (n=22) or 12 months (n=102). We measured serum ubiquinol levels and QOL scores by SF-36 at baseline and after ubiquinol intake. Results: In female participants, the SF-36 scores of role physical (RP), vitality (VT), social functioning (SF), mental health (MH), and mental component summary (MCS) increased significantly. On the other hand, significant score changes were not shown in male participants. After stratifying baseline serum ubiquinol levels by tertiles, we found a significant improvement of mental QOL scores in the low and middle ubiquinol level groups of female participants, but not in the highest tertile. Conclusions: This trial indicates that ubiquinol supplementation has effects on female participants’ psychological QOL, especially for those whose baseline serum ubiquinol levels are in the low to middle range. Key Words : ubiquinol, coenzyme Q10, energy production, quality of life, clinical trial
Intensity of expression of the CD117 antigen (c-kit) on myelomatous plasma cells

Background: We have previously shown the expression of c-kit-a receptor with tyrosine kinase activity on plasma cells (PCs) of some multiple myeloma (MM) patients. The aim of the present study was to evaluate the frequency and intensity of c-kit expression on bone marrow (BM) myelomatous PCs. Material and methods: The study group consisted of 109 MM patients (8 at stage I, 22-II, 79-III acc. to D.S. with monoclonal protein: IgG-69, IgA-25, IgM-1, BJ-12 and non-secretory-2). The control group was 10 healthy subjects. Immunophenotyping was performed on freshly collected BM samples using triple staining combination of the CD138/CD117/CD38 monoclonal antibodies analysed by flow cytometry (Cytoron Absolute and FACSCalibur – Becton Dickinson). PCs were indentified acc. to their strong reactivity for CD38, CD138 (syndecan-1) and their typical light scatter distribution. The antigen expression intensity was calculated as relative fluorescence intensity (RFI) and for direct quantitative analysis the QuantiBRITE test (Becton Dickinson) was applied. The mean channels of phycoerythrin fluorescence were defined and antibody bounding capacity (ABC) was then calculated using QuantiCALC software. Results: In 37 patients (=33%) myeloma PCs showed CD117 expression. Out of all nucleated BM cells the mean proportion of PCs with CD117 expression was 25.7±20.3%, median 21%. Values of RFI ranged from 8.4 to 20 in particular MM patients (12.4±3.2 median 11.6) and the number of CD117 binding sites (ABC) on MM plasma cells ranged from 1443 to 6217 (3262±1410, median 3109). A correlation was found between RFI and ABC values (r=0.87). In 72 patients myeloma cells did not express CD117 and mean proportion of all BM cells with CD117 expression was 3±2%, median 2.7%. Normal PCs did not express CD117. In BM of healthy persons the mean proportion of CD117+ cells was 2.4±0.7, median 1.8%. Morphological analysis of MGG stained BM aspirate smears revealed that the percentage of BM PCs in MM patients with PCs positivity for CD117 was 36±24%, median 35%, while a corresponding value in MM patients with PCs negative for CD117 was 41±23%, median 38%. In patients with CD117 + myeloma a positive correlation was found between proportion of CD117 + bone marrow cells and percentage of PCs in BM smears (r=0.95). No differences were seen in occurrence of CD117 expression depending on stage of disease and monoclonal protein isotype. Conclusions: In one third of MM patients CD117 antigen could be considered as a „tumour associated marker” and it may be of value for the identification of the malignant clone in minimal residual disease. The intensity of c-kit expression on PCs varies among particular c-kit positive MM patients and the differences in expression level may be as big as many times. It may be rationale to consider usefulness of therapy with tyrosine kinase inhibitors in the management of MM patients with c-kit positive plasma cell proliferation.
Flux Channeling in YBa2Cu3O7 Superlattices

We report unusual effect of channeled magnetic flux motion in YBa<inf>2</inf>Cu<inf>3</inf>O<inf>7-</inf><inf>δ</inf>/PrBa<inf>2</inf>Cu<inf>3</inf>O<inf>7-</inf><inf>δ</inf>superlattices grown by pulsed laser deposition. Magneto-optical imaging reveals that flux moves along a set of parallel and perpendicular lines, while optical microscopy does not show any features on the surface that may be linked to this effect. In contrast, scanning electron microscopy registers sub-micron fractures in the superlattices corresponding to these flux lines, but the magnetic flux channels are much wider than the width of these fractures. To further clarify the origin of flux channels, electrical transport measurements on the superlattices have been performed. Their current-voltage characteristics reveal the presence of distinctive branches related to the flux motion along the selective channels, following which magnetic flux crosses sample in a shortest and least resistive way. The application of very large current overheated the superlattice along these channels, evaporating superconducting material and exposing, wider than in the superconductor, fractures in the substrate. It is concluded that motion of flux in the channels is controlled not only by the presence of nano-fractures in YBa<inf>2</inf>Cu<inf>3</inf>O<inf>7-</inf><inf>δ</inf>/PrBa<inf>2</inf>Cu<inf>3</inf>O<inf>7-</inf><inf>δ</inf>, but also by stress developed in the superconducting material due to the fracturing of the substrate.
Data-Driven Construction of Chemical Reaction Network Graph Using Constrained LASSO

This paper proposes a data-driven method for the automatic construction of chemical reaction network graph. For a given set of chemical species measurements and their (approximate) time derivatives, the proposed method first estimates the nonlinear dynamic model of the reaction using an L1 penalty type sparse identification approach called ‘constrained least absolute shrinkage and selection operator’ (Con-strained LASSO). Using the estimated model, a network graph construction approach that takes into account the dynamical constraints (e.g. non-negativity and law of mass action kinetics) of chemical reaction networks is then presented. Simulation result is also presented to illustrate the proposed method.
Performance analysis of Vehicle-to-Vehicle communications for critical tasks in autonomous driving

In this paper we present a series of experiments in order to gain insight about the performance of ITS-G5A V2V communications in critical scenarios for autonomous driving. Critical tasks such T-intersection managing in semi-urban environments or elevation changes in two ways inter-urban roads have been identified as challenging scenarios in which traditional sensor based approaches may fail. For this purpose, we designed a set of experimental tests in real environments with automated vehicles equipped with GPS, ITS-G5 compliant V2V communications, cameras and radars. Cameras and radars range is compared to that of the V2V communications in the designed critical scenarios and conclusions are drown. Packets Delivery Ratios (PDR) and the Complementary Cummulative Distribution Function (CCDF) of the Update Delay (UD) are used as metrics to evaluate the quality of the communications and to analyse the requirements of the possible automated driving applications. The obtained results show that ITS-G5 V2V communications offer better performance than on-board sensors in all cases, only being affected by occlusions with big obstacles such as buses or trucks.
iPCS: intersection Platoon Control Scheme for Non-Traffic Light Community

The Cooperative Intersection Management (CIM) attracts more and more attention recently. It combines Vehicle- to-Everything (V2X) communications and motion control technology to improve the traffic efficiency of intersection. Existing intersection control schemes mainly aim at managing single intersection efficiency, yet ignore the multiple intersections optimization. In this paper, we propose an intersection Platoon Control Scheme (iPCS) for non-traffic light community to optimize the throughput and ensure fairness in multi- intersections scale. The passing sequences are scheduled taking into account the traffic flow of adjacent road segments and vehicle’s state in previous intersections. Traffic performance on mobility and message processing delay is evaluated by LTE-V2X based simulators. The results show that the iPCS shortens the average waiting time by as much as 39% compared with other existing intersection management scheme.
Monocular Depth Estimation Using Synthetic Images With Shadow Removal*

Learning based monocular depth estimation has become popular in recent years. However, training of reliable depth estimators requires large volumes of ground truth depth data, which is expensive to obtain. To overcome this challenge, we propose a novel monocular depth estimation system that adopts pixel-perfect synthetic image training. The model is trained with synthetic data but inferred with realistic images by applying image domain adaptation. Considering realistic constraints, such as shadow regions, which cause the performance drops in depth estimation, the system adopts a dedicated module to remove such ambient shadows from images to guarantee premier performance in the task. Experimental results, with both synthetic and realistic benchmarks, indicate the efficacy and the advantages of the system compared to existing state-of-the-art technologies.
Flexible capacity addition case study at reduced grid tariff without security of supply

Energy intensive industries are sensitive both to the reliability and to the costs of their energy supply system. With renewable energy becoming more affordable, their weather dependent over- and under production will cause more volatile and higher spot price, but fees prevent the roll-out of Power-to-Gas. In this paper, we differentiate the new flexible loads of energy conversion and storage like Power-to-X or batteries from the regular loads of the core activity of industries and we design the tariff system of flexible loads in such a way to be financially attractive, by abandoning a security of supply that they actually do not need. In a previous work, the technical functionality of a load management system solving the grid congestion issues was described. Here we aggregate the yearly energy balance and the associated costs in six case studies to verify that the roll-out of the new flexible loads is economically viable. The financial attractiveness of the roll-out of new flexible loads with reduced tariff system and future drop in technology price is verified in all these customers and the tariff reduction for conditional loads is the decisive factor of the profitability in four of them.
A Screening System for Mild Cognitive Impairment Based on Neuropsychological Drawing Test and Neural Network

Alzheimer’s disease and the other type of dementia have become one of the most serious global issues and the fifth leading cause of death worldwide nowadays. Therefore, early detection of the disease is crucial in order to improve the quality of life of the patients and to decrease the burden of their caregiver and clinicians. Mild cognitive impairment (MCI) is a prodromal stage of progressing to Alzheimer’s disease which should be focus on. In this paper, we have proposed a screening system based on the Rey-osterrieth Complex Figure, a neuropsychological test, that can automatically assist the clinicians to detect whether the subject is MCI or not. A data-driven deep learning approach is implemented in this work. Convolution autoencoder is designed initially to extract features from the input image. The features learned by the encoder are then used for further training the classifier. In order to validate the performance of our work, 59 MCI subjects and 59 healthy controls are recruited under the approval of institutional review board from the National Taiwan University Hospital. The performance of our proposed model is evaluated by using 10-fold cross-validation and it is repeated five times. As a result, a mean area under the receiver operating characteristic curve score of 0.851 and 0.810 of accuracy are achieved.
Hybrid GNSS/INS/UWB Positioning for Live Demonstration Assisted Driving

In Cooperative Intelligent Transport Systems, precise and reliable positioning and navigation is a key feature for assisted or autonomous driving. This contribution presents experimental results from a pilot demonstration involving different stakeholders for assisted driving in a smart city scenario. A low-cost multi-sensor fusion positioning prototype which integrates GNSS, INS and UWB measurements, with communication capabilities has been deployed into a 5G communication architecture to test real-time positioning and communication capabilities in delay constraint/safety applications. A description of the prototype and experimental performance results are provided.
Tensor Decomposition based Spatiotemporal Modeling for Distributed Thermal Processes

A tensor decomposition (TD) based method is proposed for modeling two spatial-dimensional (2-D) distributed thermal processes. Firstly TD is executed to the spatiotemporal tensor to accomplish time/space separation. Spatial basis functions (SBFs) and temporal coefficients are extracted. Then the least-squares support vector machine (LS-SVM) approach is used to learn the system dynamics in a low-dimensional temporal domain. Finally, the temperature distribution can be reconstructed by time/space synthesis. The proposed method well preserves the sensor array structure during constructing SBFs. The couplings across temporal and each spatial dimensions are also sufficiently captured. Experiments of a 2-D distributed thermal process inside of a curing oven are used to verify the effectiveness of the proposed method.
Curing of Hot-Carrier Induced Damage by Gate-Induced Drain Leakage Current in Gate-All-Around FETs

Gate oxide aging in a gate-all-around (GAA) FET fabricated on a bulk substrate was successfully cured by gate-induced drain leakage (GIDL) current. High level of GIDL current flows during the off-state cures the gate oxide aging by hot-carrier injection (HCI). The recovery behaviors were analyzed by tracing changes in the device parameters. Especially, it was found that the curing was not associated with the electric field or created holes under the bias conditions for GIDL, but was associated with the elevated temperature. The GIDL recovery requires neither extra apparatus for Joule heating nor circuit modification. This work can be useful for various FETs which have high off-state current.