Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on the Impact of Reusing Redundant Patches on Automatic Program Repair

Automatic program repair is one of the hottest topics in software engineering in recent years, but it still needs a lot of efforts to improve ability. Prophet is an efficient patch generation system that works with a set of successful human patches to rank the candidate patches in order of likely correctness [2]. However, when inspecting the Prophet's human patches manually, we find there are about 65 redundant patches (777 in total). In order to confirm whether these redundant patches affect the conclusion, we remove them and relaunch the experiment. Moreover, we try to add redundant patches to the original dataset randomly. Both the results show that in terms of Prophet and our experimental settings, the redundant patches do not have a significant impact on the first correct patches ranking results.
Alternative Fundraising: Success Factors for Blockchain-Based vs. Conventional Crowdfunding

Blockchain-based crowdfunding is an emerging economic phenomenon and a state-of-the-art strategy to finance ventures. It bears similarity to conventional crowdfunding, but has its own unique characteristics. Therefore the success factors that affect the outcome of traditional crowdfunding may have a different impact on blockchain-based crowdfunding. Despite that the number of blockchain-based crowdfunding campaigns has increased drastically in the past few years, there is a lack of good understanding of what the success factors are for them in comparison to the ones for conventional crowdfunding. Such understanding is crucial for companies to design their blockchain-based fundraising initiatives properly and facilitate potential investors to seek main signals and drivers of outstanding projects. Furthermore it could help regulators and market participants to understand how the existing regulatory framework applies to blockchain-based crowdfunding. Due to specific characteristics of blockchain-based crowdfunding, regulatory frameworks may require potential re-interpretation of requirements to allow an effective application of regulations. To fill this knowledge gaps, we have reviewed a set of relevant literature on success factors for conventional and blockchain-based crowdfunding. The result of this literature review sheds light on the directions for future research and development. The contribution of our work is a better understanding of the distinctions and similarities of blockchain-based crowdfunding compared to traditional crowdfunding.
Deep-learning for high dimensional sequential observations : application to continuous gesture recognition. (Modélisation par réseaux de neurones profonds pour l'apprentissage continu d'objets et de gestes par un robot)

Cette these a pour but de contribuer a ameliorer les interfaces Homme-machine. En particulier, nos appareils devraient repliquer notre capacite a traiter continument des flux d'information. Cependant, le domaine de l’apprentissage statistique dedie a la reconnaissance de series temporelles pose de multiples defis. Nos travaux utilisent la reconnaissance de gestes comme exemple applicatif, ces donnees offrent un melange complexe de poses corporelles et de mouvements, encodees sous des modalites tres variees. La premiere partie de notre travail compare deux modeles temporels de l’etat de l’art pour la reconnaissance continue sur des sequences, plus precisement l’hybride reseau de neurones -- modele de Markov cache (NN-HMM) et les reseaux de neurones recurrents bidirectionnels (BD-RNN) avec des unites commandees par des portes. Pour ce faire, nous avons implemente un environnement de test partage qui est plus favorable a une etude comparative equitable. Nous proposons des ajustements sur les fonctions de cout utilisees pour entrainer les reseaux de neurones et sur les expressions du modele hybride afin de gerer un large desequilibre des classes de notre base d’apprentissage. Bien que les publications recentes semblent privilegier l’architecture BD-RNN, nous demontrons que l’hybride NN-HMM demeure competitif. Cependant, ce dernier est plus dependant de son modele d'entrees pour modeliser les phenomenes temporels a court terme. Enfin, nous montrons que les facteurs de variations appris sur les entrees par les deux modeles sont inter-compatibles. Dans un second temps, nous presentons une etude de l'apprentissage dit «en un coup» applique aux gestes. Ce paradigme d'apprentissage gagne en attention mais demeure peu aborde dans le cas de series temporelles. Nous proposons une architecture construite autour d’un reseau de neurones bidirectionnel. Son efficacite est demontree par la reconnaissance de gestes isoles issus d’un dictionnaire de langage des signes. A partir de ce modele de reference, nous proposons de multiples ameliorations inspirees par des travaux dans des domaines connexes, et nous etudions les avantages ou inconvenients de chacun
Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images

Colorectal cancer is the second most frequently diagnosed cancer in women and the third most frequently diagnosed cancer in men. At least 80%–95% of the colorectal cancers are evolved from intestinal polyps. Although colonoscopy is regarded as the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the level of hand-eye coordination and the operator skills. Thus, we are primarily motivated by the need for obtaining an early and accurate diagnosis of polyps in the colonoscopy images. In this paper, we employed the powerful object detection neural network “Mask R-CNN” to identify and segment polyps in the colonoscopy images. Also, we proposed an ensemble method to combine the two Mask R-CNN models with different backbone structures (ResNet50 and ResNet101) to enhance the performance. Mask R-CNNs in our model were first trained on COCO dataset, and then finely tuned using intestinal polyp dataset since a large number of annotated colonoscopy images are not easily accessible. In order to evaluate our proposed model, we used three open intestinal polyp datasets, CVC-ClinicDB, ETIS-Larib, and CVC-ColonDB. Our results show that our transfer learning-based ensemble model significantly outperforms state-of-the-art methods.
Proposal for Common Active 1.3- $\mu$ m Quantum Dot Electroabsorption Modulated DFB Laser

Opportunities for the monolithic integration of a novel common quantum dot (QD)-active electroabsorption modulated laser are explored. An electric-field and temperature-dependent spectroscopic study of optical absorption and gain are presented in the state-of-the-art 1.3- $\mu \text{m}$ In(Ga)As/GaAs QD active material. The unique gain/absorption spectral shape, attributed to the QD’s density of states, allows for a number of possible modulation schemes dependent upon the selected laser wavelength detuning from the gain peak. Intensity modulation and change in absorption, leading to negative chirp operation, are demonstrated via absorption spectroscopy and gain measurement of eight-layer-stack QD-active material. Such a device would be able to provide positive or negative chirp dependent upon modulation scheme and gain/Bragg wavelength detuning.
Achievable Rates of Multi-Carrier Modulation Schemes for Bandlimited IM/DD Systems

In this paper, we comprehensively investigate the achievable rates of selected band-limited intensity modulation schemes, which are important for optical wireless communication applications, while accounting for the specific nature of their signal construction (non-negative, real, and baseband), and imposing identical bandwidth and average optical power constraints. Furthermore, we identify/devise methods to effectively trade between these parameters. Three variants of orthogonal frequency division multiplexing (OFDM), namely, asymmetrically clipped optical OFDM (ACO-OFDM), spectrally and energy efficient OFDM (SEE-OFDM), and dc-biased optical OFDM (DCO-OFDM), and single-carrier pulse amplitude modulation are studied. The clipping noise in ACO-OFDM and SEE-OFDM is found to consume a large excess bandwidth. The detrimental effects of this excess bandwidth on the achievable rate are evaluated. For SEE-OFDM, the problem of optimal power allocation among its components is formulated and solved using the Karush–Kuhn–Tucker method. For DCO-OFDM, the clipping noise is modeled and incorporated in the analysis. Among the existing schemes, DCO-OFDM yields the best overall performance, due to its compact spectrum. In order to improve the achievable rate, we propose and analyze two improved distortionless variants, filtered ACO-OFDM and filtered SEE-OFDM (FSEE-OFDM), which yield better spectral efficiency than ACO-OFDM and SEE-OFDM, respectively. FSEE-OFDM, being the most spectrally efficient, outperforms all schemes.
Balanced Mini-Batch Training for Imbalanced Image Data Classification with Neural Network

We propose a novel method of training neural networks for industrial image classification that can reduce the effect of imbalanced data in supervised training. We considered visual quality inspection of industrial products as an image-classification task and attempted to solve this with a convolutional neural network; however, a problem of imbalanced data emerged in supervised training in which the neural network cannot optimize parameters. Since most industrial products are not defective, samples of defective products were fewer than those of the non-defective products; this difference in the number of samples causes an imbalance in training data. A neural network trained with imbalanced data often has varied levels of precision in determining each class depending on the difference in the number of class samples in the training data, which is a significant problem in industrial quality inspection. As a solution to this problem, we propose a balanced mini-batch training method that can virtually balance the class ratio of training samples. In an experiment, the neural network trained with the proposed method achieved higher classification ability than that trained with over-sampled or undersampled data for two types of imbalanced image datasets.
A Study on Radar Target Detection Based on Deep Neural Networks

Target detection is one of the most important radar applications widely used in practice. Target detection can be regarded as a kind of classification, which distinguishes whether the signal undertested consists of an echo from a target (target present) or just corresponds to the noise (target absent). The deep neural network (DNN) is a popular topic for classification and has successfully been applied in different fields of science. Recently, many researchers have proposed DNNs for radar applications. In this article, we analyze a possible application of DNN- for target detection in radar, DNN-based detectors are designed, and the performance of the detector is demonstrated by comparison with traditional target detectors.
Joint Spatial and Discrete Cosine Transform Domain-Based Counter Forensics for Adaptive Contrast Enhancement

Contrast enhancement (CE) is a common post-processing step in image forgery to create visually convincing tampered images. However, the artifacts embedded during this process can be captured to determine the presence of CE. To overcome these artifacts, we propose a novel counter-forensic technique using adaptive CE as an enhancement operation, whereas previous works only deal with global CE. We derive an optimization formulation, which enhances the attacked image using the L2 distance in both the spatial and DCT domains. The proposed algorithm suppresses the detectable artifacts, thereby reducing the CE detection performance. Furthermore, the formulation also preserves natural spatial statistics using Huber Markov random field. A major advantage of working jointly in both the domains is that the complementary information can be leveraged while suppressing the artifacts in both the domains. We evaluate the proposed method using various visual quality metrics and against the state-of-the-art CE detectors. In our experiments, we observe a reduction of more than 17% in accuracy for a false positive rate of 1% for deep learning as well as steganalysis-DCT feature-based detectors. We also show that the proposed model generates high visual quality images.
Adipokines and Aging: Findings From Centenarians and the Very Old

Adipose tissue, which was once considered as a simple energy storage depot, is now recognized as an active endocrine organ that regulates the whole-body energy homeostasis by secreting hundreds of bioactive substances termed adipokines. Dysregulation of adipokines is a key feature of insulin resistance and a metabolic syndrome associated with obesity. Adipokine dysregulation and insulin resistance are also associated with energy-deprivation conditions, such as frailty in old age. Previous studies have demonstrated that preserved insulin sensitivity and low prevalence of diabetes are the metabolic peculiarities of centenarians, suggesting the possible role of adipokine homeostasis in healthy longevity. Among the numerous adipokines, adiponectin is regarded as unique and salutary, showing negative correlations with several age- and obesity-related metabolic disturbances and a positive correlation with longevity and insulin sensitivity among centenarians. However, large-scale epidemiological studies have implied the opposite aspect of this adipokine as a prognostic factor for all-cause and cardiovascular mortality in patients with heart failure or kidney disease. In this review, the clinical significance of adiponectin was comparatively addressed in centenarians and the very old, in terms of frailty, cardiovascular risk, and mortality.
Maximum Power Point Controller for Large-Scale Photovoltaic Power Plants Using Central Inverters Under Partial Shading Conditions

The central inverter topology based on three-level converters is widely used in large-scale photovoltaic (PV) power plants because of its simple and reliable structure and high efficiency. However, when two PV arrays are connected in series for higher dc-link voltage, the single maximum power point tracking (MPPT) operation cannot function properly when there is partial shading between the arrays. This paper presents a configuration where the two PV arrays are connected in series together with an auxiliary power converter feeding a neutral-point-clamped central inverter. This configuration ensures maximum power production from both PV arrays during partial shading conditions, achieving the MPPT operation for both PV arrays. The proposed converter together with their controls are verified by simulation and experiment.
Dissociating Empathy From Perspective-Taking: Evidence From Intra- and Inter-Individual Differences Research

Humans have the capacity to share others' emotions, be they positive or negative. Elicited by the observed or imagined emotion of another person, an observer develops a similar emotional state herself. This capacity, empathy, is one of the pillars of social understanding and interaction as it creates a representation of another's inner, mental state. Empathy needs to be dissociated from other social emotions and, crucially, also from cognitive mechanisms of understanding others, the ability to take others' perspective. Here, we describe the conceptual distinctions of these constructs and review behavioral and neural evidence that dissociates them. The main focus of the present review lies on the intraindividual changes in empathy and perspective-taking across the lifespan and on interindividual differences on subclinical and clinical levels. The data show that empathy and perspective-taking recruit distinct neural circuits and can be discerned already during early and throughout adult development. Both capacities also vary substantially between situations and people. Differences can be systematically related to situational characteristics as well as personality traits and mental disorders. The clear distinction of affect sharing from other social emotions like compassion and from cognitive perspective-taking, argues for a clear-cut terminology to describe these constructs. In our view, this speaks against using empathy as an umbrella term encompassing all affective and cognitive routes to understanding others. Unifying the way we speak about these phenomena will help to further research on their underlying mechanisms, psychopathological alterations, and plasticity in training and therapy.
Forecasting of total daily solar energy generation using ARIMA: A case study

In this paper, a well known statistical modeling method named ARIMA has been used to forecast the total daily solar energy generated by a solar panel located in a research facility. The beauty of the ARIMA model lies in its simplicity and it can only be applied to stationary time series. So our time series data, which is seasonal and non-stationary, is transformed into a stationary one for applying the ARIMA model. The model is developed using sophisticated statistical techniques. The optimum model is chosen and validated using Akaike information criterion (AIC) and residual sum of squares (SSE). Error analysis is done to demonstrate the efficiency of the proposed method. The accuracy of the developed model can be further increased, which is subject to future research.
Peptide Super-Agonist Enhances T-Cell Responses to Melanoma

Recent immunotherapeutic approaches using adoptive cell therapy, or checkpoint blockade, have demonstrated the powerful anti-cancer potential of CD8 cytotoxic T-lymphocytes (CTL). While these approaches have shown great promise, they are only effective in some patients with some cancers. The potential power, and relative ease, of therapeutic vaccination against tumour associated antigens (TAA) present in different cancers has been a long sought-after approach for harnessing the discriminating sensitivity of CTL to treat cancer and has seen recent renewed interest following cancer vaccination successes using unique tumour neoantigens. Unfortunately, results with TAA-targeted “universal” cancer vaccines (UCV) have been largely disappointing. Infectious disease models have demonstrated that T-cell clonotypes that recognise the same antigen should not be viewed as being equally effective. Extrapolation of this notion to UCV would suggest that the quality of response in terms of the T-cell receptor (TCR) clonotypes induced might be more important than the quantity of the response. Unfortunately, there is little opportunity to assess the effectiveness of individual T-cell clonotypes in vivo. Here, we identified effective, persistent T-cell clonotypes in an HLA A2+ patient following successful tumour infiltrating lymphocyte (TIL) therapy. One such T-cell clone was used to generate super-agonist altered peptide ligands (APLs). Further refinement produced an APL that was capable of inducing T-cells in greater magnitude, and with improved effectiveness, from the blood of all 14 healthy donors tested. Importantly, this APL also induced T-cells from melanoma patient blood that exhibited superior recognition of the patient's own tumour compared to those induced by the natural antigen sequence. These results suggest that use of APL to skew the clonotypic quality of T-cells induced by cancer vaccination could provide a promising avenue in the hunt for the UCV “magic bullet. ”
Reconstitution and NMR Characterization of the Ion-Channel Accessory Subunit Barttin in Detergents and Lipid-Bilayer Nanodiscs

Barttin is an accessory subunit of ClC-K chloride channels expressed in the kidney and the inner ear. Main functions of ClC-K/barttin channels are the generation of the cortico-medullary osmotic gradients in the kidney and the endocochlear potential in the inner ear. Mutations in the gene encoding barttin, BSND, result in impaired urinary concentration and sensory deafness. Barttin is predicted to be a two helical integral membrane protein that directly interacts with its ion channel in the membrane bilayer where it stabilizes the channel complex, promotes its incorporation into the surface membrane and leads to channel activation. It therefore is an attractive target to address fundamental questions of intermolecular communication within the membrane. However, so far inherent challenges in protein expression and stabilization prevented comprehensive in vitro studies and structural characterization. Here we demonstrate that cell-free expression enables production of sufficient quantities of an isotope-labeled barttin variant (I72X Barttin, capable to promote surface membrane insertion and channel activation) for NMR-based structural studies. Additionally, we established purification protocols as well as reconstitution strategies in detergent micelles and phospholipid bilayer nanodiscs. Stability, folding, and NMR data quality are reported as well as a suitable assignment strategy, paving the way to its structural characterization.
Recommending Web Advertisements based on Long-Short Term User Interest

This paper reports the results of a study carried out to develop a system to recommend web advertisements to users based on their latent interests in an online real time bidding environment. As part of this work, we describe an approach which could be used to help predict the latent interest of users by analyzing their long and short term interests based on a large dataset of user web browsing histories. The proposed approach was tested in an experiment study with 32 different websites. Overall, this approach, which separated the user browsing history into sections representing their long and short term interests resulted in significantly higher predictive performance than when a singular section of user browsing history was used to represent the overall interests of users. In addition, we examined the effect of using different category levels as features to represent long and short term interest.
Linkage Mapping and Genome-Wide Association Studies of the Rf Gene Cluster in Sunflower (Helianthus annuus L.) and Their Distribution in World Sunflower Collections

Commercial hybrid seed production in sunflower currently relies on a single cytoplasmic male sterility (CMS) source, PET1 and the major fertility restoration gene, Rf1, leaving the crop highly vulnerable to issues with genetic bottlenecks. Therefore, having multiple CMS/Rf systems is important for sustainable sunflower production. Here, we report the identification of a new fertility restoration gene, Rf7, which is tightly linked to a new downy mildew (DM) resistance gene, Pl34, in the USDA sunflower inbred line, RHA 428. The Rf7 gene was genetically mapped to an interval of 0.6 cM on the lower end of linkage group (LG) 13, while Pl34 was mapped 2.1 cM proximal to the Rf7. Both the genes are located in a cluster of Rf and Pl genes. To gain further insights into the distribution of Rf genes in the sunflower breeding lines, we used a genome-wide association study (GWAS) approach to identify markers associated with the fertility restoration trait in a panel of 333 sunflower lines genotyped with 8,723 single nucleotide polymorphism (SNP) markers. Twenty-four SNP markers on the lower end of LG13 spanning a genomic region of 2.47 cM were significantly associated with the trait. The significant markers were surveyed in a world collection panel of 548 sunflower lines and validated to be associated with the Rf1 gene. The SNP haplotypes for the Rf1 gene are different from Rf5 and the Rf7gene located in the Rf gene cluster on LG13. The SNP and SSR markers tightly flanking the Rf7 gene and the Pl34 gene would benefit the sunflower breeders in facilitating marker assisted selection (MAS) of Rf and Pl genes.