Miracle-dz/UniFAR-FTUs huggingface dataset:
Heuristic methods for the Traveling Salesman Problem with Drone

Once known to be used exclusively in military domain, unmanned aerial vehicles (UAV) have stepped up to become a part of new logistic method in commercial sector called "last-mile delivery". In this novel approach, small UAVs, also known as drones, are deployed in tandem with the trucks to deliver goods to customers. Under research context, it gives rise to a new variant of the traveling salesman problem (TSP), of which we call TSP with drone (TSP-D) [1]. In this paper, we propose two heuristics: route first cluster second, and cluster first route second, to solve the problem efficiently. A new mixed integer programming formulation is also introduced to handle the cluster step in both heuristics. We conduct an experiment, adapting different profit functions of the MIP model, in both heuristics, to many instances with different sizes and characteristics. The numerical analysis shows not only a significant savings compare to truck-only delivery but also a superior performance against the previous work.
Cognition After Coronary Artery Surgery Is Not Related to Postoperative Jugular Bulb Oxyhemoglobin Desaturation

During the early postoperative period after coronary artery bypass grafting (CABG) surgery, many patients experience jugular bulb oxyhemoglobin desaturation (Sjo2 < 50%). We sought to determine whether Sjo2 during cardiopulmonary bypass and the early postoperative period influenced long-term cognitive performance after CABG surgery. One hundred two patients completed a battery of cognitive tests the day before and 3 mo after CABG surgery. A General Cognitive Score was generated from these tests as an overall measure of cognitive function. Intraoperatively, Sjo2 was determined by intermittent blood sampling, and postoperatively, the area under the curve of Sjo2 < 50% and time was calculated from continuous reflectance oximetry. No significant correlations between cognitive performance and either intra- or postoperative Sjo2 were found. Preoperative cognitive performance was the main determinant of cognition at 3 mo (r2 = 0.83, P < 0.001), and palpable atheroma of the ascending aorta made a small, but significant, contribution to a decline in cognition (r2 = 0.018, P = 0.001). Implications Postoperative cerebral hypoperfusion, indicated by oxyhemoglobin desaturation < 50%, occurs in many patients after coronary artery bypass graft surgery. However, this measurement of hypoperfusion does not appear to influence long-term cognition after coronary artery bypass graft surgery.
A Systematic Review of the Effect of Expectancy on Treatment Responses to Acupuncture

Randomised controlled trials (RCTs) of acupuncture often find equivalent responses to real and placebo acupuncture despite both appearing superior to no treatment. This raises questions regarding the mechanisms of acupuncture, especially the contribution of patient expectancies. We systematically reviewed previous research assessing the relationship between expectancy and treatment responses following acupuncture, whether real or placebo. To be included, studies needed to assess and/or manipulate expectancies about acupuncture and relate these to at least one health-relevant outcome. Nine such independent studies were identified through systematic searches of Medline, PsycInfo, PubMed, and Cochrane Clinical Trials Register. The methodology and reporting of these studies were quite heterogeneous, meaning that meta-analysis was not possible. A descriptive review revealed that five studies found statistically significant effects of expectancy on a least one outcome, with three also finding evidence suggestive of an interaction between expectancy and type of acupuncture (real or placebo). While there were some trends in significant effects in terms of study characteristics, their generality is limited by the heterogeneity of study designs. The differences in design across studies highlight some important methodological considerations for future research in this area, particularly regarding whether to assess or manipulate expectancies and how best to assess expectancies.
Intensity Estimation of Spectroscopic Signals With an Improved Sparse Reconstruction Algorithm

One of the main measurements performed in a nuclear spectroscopy experiment is the activity of an unknown radioactive source. The use of digital apparatus and physical perturbations, known as pile-up effect, make this measurement difficult when the activity of the source is high. In recent contributions, the use of compressive sensing methods yielded good estimates of this activity. This letter presents an improvement of a previously described method. It takes into account the fact that the signal used for the activity estimation is sampled, and introduces another plug-in estimation to counterbalance the bias introduced by the sampling. Results on simulations and real data validate the proposed approach, but illustrate that a good fit between the dictionary used and the signal at hand is required.
Modeling Latent-Dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference

Shallow parsing is one of many NLP tasks that can be reduced to a sequence labeling problem. In this paper we show that the latent-dynamics (i.e., hidden substructure of shallow phrases) constitutes a problem in shallow parsing, and we show that modeling this intermediate structure is useful. By analyzing the automatically learned hidden states, we show how the latent conditional model explicitly learn latent-dynamics. We propose in this paper the Best Label Path (BLP) inference algorithm, which is able to produce the most probable label sequence on latent conditional models. It outperforms two existing inference algorithms. With the BLP inference, the LDCRF model significantly outperforms CRF models on word features, and achieves comparable performance of the most successful shallow parsers on the CoNLL data when further using part-of-speech features.
Cloud Consumer Oriented Utility Model Research in Cloud Computing

Cloud service, is being widely deployed on virtualized resources, with its dynamic scalability and availability in the past few years. With the exponential increase of such service, how to choose the suitable deployed service to satisfy the consumer's utility-maximization becomes crucial. In this paper we propose a mathematical model addressing the problem of the deployed service to maximize the consumer's utility. The proposed model efficiently matches with the characteristics of market-oriented platforms covering a wide range of deployed service selection problem. The efficiency of the proposed model is validated through simulation studies.
A Cross‐Linguistic Perspective on Syntactic Complexity in L2 Development: Syntactic Elaboration and Diversity

Syntactic and linguistic complexity have been studied extensively in applied linguistics as indicators of linguistic performance, development, and proficiency. Recent publications have equally highlighted the reductionist approach taken to syntactic complexity measurement, which often focuses on one or two measures representing complexity at the level of clause-linking or the sentence, but eschews complexity measurement at other syntactic levels, such as the phrase or the clause. Previous approaches have also rarely incorporated measures representing the diversity of syntactic structures in learner productions. Finally, complexity development has rarely been considered from a cross-linguistic perspective, so that many questions pertaining to the cross-linguistic validity of complexity measurement remain. This article reports on an empirical study on syntactic complexity development and introduces a range of syntactic diversity measures alongside frequently used measures of syntactic elaboration. The study analyzed 100 English and 100 French second language oral narratives from adolescent native speakers of Dutch, situated at 4 proficiency levels (beginner-advanced), as well as native speaker benchmark data from each language. The results reveal a gradual process of syntactic elaboration and syntactic diversification in both learner groups, while, especially in French, considerable differences between learners and native speakers reside in the distribution of specific clause types. [ABSTRACT FROM AUTHOR]
Integrating Seed Names and ngrams for a Named Entity List and Classifier

We present a method for building a named-entity list and machine-learned named-entity classifier from a corpus of Dutch n ewspaper text, a rule-based named entity recognizer, and labeled seed name lists taken from the internet. The seed names, labeled either as PERSON, LOCATION, ORGANIZATION, or ADJECTIVAL name, are looked up i n a 83-million word corpus, and their immediate contexts are stored as instances of their label. The latter 8-grams are us ed by a decision-tree learning algorithm that, after traini ng, (i) can produce high-precision labeling of instances to be added to the seed lists, and (ii) more generally labels new, unseen names. Unlabeled namedentity types are labeled with a precision of 61% and a recall of 56%; aiming at optimizing precision, an overall precision of 83% can be obtained (a top precision of 88% on PERSON). On free text, named-entity token labeling accuracy is 71%.
Automated Control of Interactions in Virtual Spaces: a Useful Task for Exploratory Creativity

The main problem for Interactive Digital Storytelling is the inevitable conﬂict between author’s determinism and interactor’s freedom. This paper presents a computational solution for addressing this problem, and discusses how exploratory creativity (more than transfor-mational creativity) can play a role in solving the dilemma. Our proposal is based on the theoretical study of tabletop Role-Playing Games, Virtual Environments applications and structural analysis of narrative texts. It involves the implementation of automatic directors for the story and discourse levels useful for many story-based applications.
Superconductor-correlated metal-superconductor Josephson junctions: an optimized class for high speed digital electronics

It has long been conjectured that tuning the barrier of a Josephson junction to lie close to the metal-insulator transition will enhance the switching speed and provide optimal performance. We examine a new class of junctions, so-called SCmS junctions, where the barrier is a correlated metal (or insulator) close to the metal-insulator transition. We show that high I/sub c/R/sub N/ products and moderate temperature derivatives of I/sub c/ can be achieved when the thickness and metallicity of the barrier is properly tuned. We believe these junctions show promise for the fastest speed digital electronics operation.
Learning to Solve Multiple Goals

In many domains, the task can be decomposed into a set of independent sub-goals. Often, such tasks are too complex to be learned using standard techniques such as Reinforcement Learning. The complexity is caused by the learning system having to keep track of the status of all sub-goals concurrently. Thus, if the solution to one sub-goal is known when another sub-goal is in some given state, the known solution must be relearned when the status of the other sub-goal changes. This dissertation presents a modular approach to reinforcement learning that takes advantage of task decomposition to avoid unnecessary relearning. In the modular approach, modules are created to learn each sub-goal. Each module receives only those inputs relevant to its associated sub-goal, and can therefore learn without being affected by the state of other sub-goals. Furthermore, each module searches a much smaller space than that defined by all inputs considered together, thereby greatly reducing learning time. Since each module learns how to achieve a separate sub-goal, at any given time it may recommend an action different from that recommended by other modules. To select an action that best satisfies as many of the modules as possible, a simple arbitration strategy is used. One such strategy, explored in this dissertation, is called greatest mass which simply combines action utilities from all modules and selects the one with the largest combined utility. Since the modular approach limits and separates information given to the modules, the solution learned must necessarily differ from that learned by a standard, non-modular approach. However, experiments in a simple driving world indicate that while sub-optimal, the solution learned by the modular system only makes minor errors when compared with that learned by the standard approach. A complex task can thus be learned very quickly, using only small amounts of computational resources, with only small sacrifices in solution quality, using the modular approach.
IRAS 08281-4850 and IRAS 14325-6428: two A-type post-AGB stars with s-process enrichment

Aims. One of the puzzling findings in the study of the chemical evolution of (post-)AGB stars is why very similar stars (in terms of metallicity, spectral type, infrared properties, etc.) show a very different photospheric composition. We aim at extending the still limited sample of s-process enriched post-AGB stars, in order to obtain a statistically large enough sample that allows us to formulate conclusions concerning the 3rd dredge-up occurrence. Methods. We selected two post-AGB stars on the basis of IR colours indicative of a past history of heavy mass loss: IRAS 08281-4850 and IRAS 14325-6428. They are cool sources in the locus of the Planetary Nebulae (PNe) in the IRAS colour-colour diagram. Abundances of both objects were derived for the first time on the basis of high-quality UVES and EMMI spectra, using a critically compiled line list with accurate log(gf) values, together with the latest Kurucz model atmospheres. Results. Both objects have very similar spectroscopically defined effective temperatures of 7750 - 8000 K. They are strongly carbon and s-process enriched, with a C/O ratio of 1.9 and 1.6, and an [Is/Fe] of +1.7 and +1.2, for IRAS 08281-4850 and IRAS 14325-6428 respectively. Moreover, the spectral energy distributions (SEDs) point to heavy mass-loss during the preceding AGB phase. Conclusions. IRAS 08281-4850 and IRAS 14325-6428 are prototypical post-AGB objects in the sense that they show strong post 3rd dredge-up chemical enrichments. The neutron irradiation has been extremely efficient, despite the only mild sub-solar metallicity. This is not conform with the recent chemical models. The existence of very similar post-AGB stars without any enrichment emphasizes our poor knowledge of the details of the AGB nucleosynthesis and dredge-up phenomena. We call for a very systematic chemical study of all cool sources in the PN region of the IRAS colour-colour diagram.
DRAM Triggers Lysosomal Membrane Permeabilization and Cell Death in CD4+ T Cells Infected with HIV

Productive HIV infection of CD4+ T cells leads to a caspase-independent cell death pathway associated with lysosomal membrane permeabilization (LMP) and cathepsin release, resulting in mitochondrial outer membrane permeabilization (MOMP). Herein, we demonstrate that HIV infection induces damage-regulated autophagy modulator (DRAM) expression in a p53-dependent manner. Knocking down the expression of DRAM and p53 genes with specific siRNAs inhibited autophagy and LMP. However, inhibition of Atg5 and Beclin genes that prevents autophagy had a minor effect on LMP and cell death. The knock down of DRAM gene inhibited cytochrome C release, MOMP and cell death. However, knocking down DRAM, we increased viral infection and production. Our study shows for the first time the involvement of DRAM in host-pathogen interactions, which may represent a mechanism of defense via the elimination of infected cells.
On the Improvement of Singing Voice Separation for Monaural Recordings Using the MIR-1K Dataset

Monaural singing voice separation is an extremely challenging problem. While efforts in pitch-based inference methods have led to considerable progress in voiced singing voice separation, little attention has been paid to the incapability of such methods to separate unvoiced singing voice due to its in harmonic structure and weaker energy. In this paper, we proposed a systematic approach to identify and separate the unvoiced singing voice from the music accompaniment. We have also enhanced the performance of separating voiced singing via a spectral subtraction method. The proposed system follows the framework of computational auditory scene analysis (CASA) which consists of the segmentation stage and the grouping stage. In the segmentation stage, the input song signals are decomposed into small sensory elements in different time-frequency resolutions. The unvoiced sensory elements are then identified by Gaussian mixture models. The experimental results demonstrated that the quality of the separated singing voice is improved for both the unvoiced and voiced parts. Moreover, to deal with the problem of lack of a publicly available dataset for singing voice separation, we have constructed a corpus called MIR-1K (multimedia information retrieval lab, 1000 song clips) where all singing voices and music accompaniments were recorded separately. Each song clip comes with human-labeled pitch values, unvoiced sounds and vocal/non-vocal segments, and lyrics, as well as the speech recording of the lyrics.
Image Matching with Multi-order Features

We propose a novel image matching method that may incorporate first-, second- and third-order features. These features are defined by a feature point, an edge linking two feature points and a triangle connecting three feature points, respectively. Taking them as vertices, the matching method constructs a weighted bipartite graph for computing the maximum weight matching solution by the Kuhn-Munkres algorithm. In second- and third-order cases, we design a Hungarian decoder to get the final matching between feature points. Experimental results show that the method can achieve relatively good performances on video sequences, even similar or superior to some of the state-of-the-art.
Interferon-Inducible Ubiquitin E2, Ubc8, Is a Conjugating Enzyme for Protein ISGylation

ABSTRACT Protein ISGylation is unique among ubiquitin-like conjugation systems in that the expression and conjugation processes are induced by specific stimuli, mainly via the alpha/beta interferon signaling pathway. It has been suggested that protein ISGylation plays a special role in the immune response, because of its interferon-signal dependency and its appearance only in higher eukaryotic organisms. Here, we report the identification of an ISG15-conjugating enzyme, Ubc8. Like other components of the protein ISGylation system (ISG15, UBE1L, and UBP43), Ubc8 is an interferon-inducible protein. Ubc8 clearly mediates protein ISGylation in transfection assays. The reduction of Ubc8 expression by small interfering RNA causes a decrease in protein ISGylation in HeLa cells upon interferon treatment. Neither UbcH7/UbcM4, the closest homologue of Ubc8 among known ubiquitin E2s, nor the small ubiquitin-like modifier E2 Ubc9 supports protein ISGylation. These findings strongly suggest that Ubc8 is a major ISG15-conjugating enzyme responsible for protein ISGylation upon interferon stimulation. Furthermore, we established an assay system to detect ISGylated target proteins by cotransfection of ISG15, UBE1L, and Ubc8 together with a target protein to be analyzed. This method provides an easy and effective way to identify new targets for the ISGylation system and will facilitate related studies.
Molybdenum and Chromium Concentrations in Breast Milk from Japanese Women

Molybdenum (Mo) and chromium (Cr) in 79 Japanese breast milk samples were measured by inductively coupled plasma-mass spectrometry. For Mo, 51 samples (64.6%) showed less than 5 ng/ml and only 12 samples (15.2%) showed more than 10 ng/ml. The range and median were <0.1 to 25.91 and 3.18 ng/ml respectively. For Cr, 38 samples (48.1%) showed less than 1 ng/ml, 20 samples (25.3%) showed 1 to 2 ng/ml, and only six samples (7.6%) showed more than 5 ng/ml. The range and median were <0.1 to 18.67 and 1.00 ng/ml respectively.
Evaluating uncertain location-based spatial queries

Location-Based Spatial Queries (LBSQ) are becoming more and more useful in location-based services such as those provided by cell-phones, Wireless LAN and GPS technologies. They support the access to information resources by taking into account the spatial context of the user when submitting the query, and the spatial location of the searched resources (instances). In fact, in a LBSQ the key-selection condition is generally a constraint on the distance of the resources in the database (instances) from the user location. One deficiency of current approaches is the fact that they do not manage the uncertainty that often characterizes the knowledge of either the user location or the searched instances. A model for representing and evaluating uncertain Location-Based Spatial Queries is proposed in which besides location uncertainty also the spatial condition can be imprecise. A two-step evaluation procedure of LBSQs is outlined based on a filter and on a refinement phase.
Impact of Acoustic Radiation Force Impulse Imaging in Clinical Practice of Patients after Orthotopic Liver Transplantation

Background Acoustic radiation force impulse (ARFI) elastography is a reliable diagnostic device for quantitative non-invasive assessment of liver fibrosis in patients with chronic liver disease. The aim of our prospective study was to evaluate the impact of ARFI in patients after orthotopic liver transplantation (OLT). Therefore, we compared ARFI shear wave velocities with clinical features, non-invasive markers, and the histology of patients following OLT. Material/Methods Post-transplant patients underwent a clinical examination and blood samples were taken. B-mode and Doppler ultrasound (US) of the portal vein and the hepatic artery were performed. Subsequently, a minimum of 10 valid ARFI values were measured in the left and right liver lobe. Liver biopsy was performed if indicated. Results Between May 2012 and May 2014, 58 Patients after OLT were included in the prospective study. Laboratory markers and aspartate aminotransferase-to-platelet ratio index (APRI) correlated with ARFI values (r=0.44, p<0.001). The histological (n=22) fibrosis score (Ludwig) was significantly correlated with the ARFI of the biopsy site (r=0.55, p=0.008). The mean shear-wave velocities were significantly increased in advanced fibrosis (F≤2 1.57±0.57 m/s; F≥3 2.85±0.66 m/s; p<0.001), obstructive cholestasis and active viral hepatitis. The area under the receiver operating characteristic (AUROC) curves for the accuracy of ARFI were 74% (F≥1), 73% (F≥2), 93% (F≥3), and 80% (=F4). Conclusions ARFI elastography correlates well with laboratory values and with noninvasive and invasive markers of fibrosis in patients after OLT. In this regard, elevated ARFI-velocities should be interpreted with caution in the context of obstructive cholestasis and active viral disease.