Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis

In this paper, we introduce an unsupervised learning approach to automatically dis- cover, summarize, and manipulate artistic styles from large collections of paintings. Our method is based on archetypal analysis, which is an unsupervised learning technique akin to sparse coding with a geometric interpretation. When applied to deep image representations from a data collection, it learns a dictionary of archetypal styles, which can be easily visualized. After training the model, the style of a new image, which is characterized by local statistics of deep visual features, is approximated by a sparse convex combination of archetypes. This allows us to interpret which archetypal styles are present in the input image, and in which proportion. Finally, our approach allows us to manipulate the coefficients of the latent archetypal decomposition, and achieve various special effects such as style enhancement, transfer, and interpolation between multiple archetypes.
Quantitative analysis of internal texture for classification of pulmonary nodules in three-dimensional thoracic images

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in three-dimensional (3-D) thoracic images. This paper focuses on an approach for characterizing the internal texture which is one of important clues for differentiating between malignant and benign nodules. In this approach, each voxel was described in terms of shape index derived from curvatures on the voxel. The voxels inside the nodule were aggregated via shape histogram to quantify how much shape category was present in the nodule. Topological features were introduced to characterize the morphology of the cluster constructed from a set of voxels with the same shape category. The properties such as curvedness and CT density were also built into the representation. We evaluated the effectiveness of the topological and histogram features extracted from 3-D pulmonary nodules for classification of malignant and benign internal structures. We also compared the performance of the computerized classification with the experienced physicians. The classification performance based on the combined feature space reached the performance of the experienced physicians. Our results demonstrate the feasibility of using topological and histogram features for analyzing internal texture to assist physicians in making diagnostic decisions.
Visual Appearance Modulates Prediction Error in Virtual Reality

Different rendering styles induce different levels of agency and user behaviors in virtual reality environments. We applied an electroencephalogram-based approach to investigate how the rendering style of the users’ hands affects behavioral and cognitive responses. To this end, we introduced prediction errors due to cognitive conflicts during a 3-D object selection task by manipulating the selection distance of the target object. The results showed that, for participants with high behavioral inhibition scores, the amplitude of the negative event-related potential at approximately 50–250 ms correlated with the realism of the virtual hands. Concurring with the uncanny valley theory, these findings suggest that the more realistic the representation of the user’s hand is, the more sensitive the user becomes toward subtle errors, such as tracking inaccuracies.
GMP-Grade Manufacturing of T Cells Engineered to Express a Defined γδTCR

γ9δ2T cells play a critical role in daily cancer immune surveillance by sensing cancer-mediated metabolic changes. However, a major limitation of the therapeutic application of γ9δ2T cells is their diversity and regulation through innate co-receptors. In order to overcome natural obstacles of γ9δ2T cells, we have developed the concept of T cells engineered to express a defined γδT cell receptor (TEGs). This next generation of chimeric antigen receptor engineered T (CAR-T) cells not only allows for targeting of hematological but also of solid tumors and, therefore, overcomes major limitations of many CAR-T and γδT cell strategies. Here, we report on the development of a robust manufacturing procedure of T cells engineered to express the high affinity Vγ9Vδ2T cell receptor (TCR) clone 5 (TEG001). We determined the best concentration of anti-CD3/CD28 activation and expansion beads, optimal virus titer, and cell density for retroviral transduction, and validated a Good Manufacturing Practice (GMP)-grade purification procedure by utilizing the CliniMACS system to deplete non- and poorly-engineered T cells. To the best of our knowledge, we have developed the very first GMP manufacturing procedure in which αβTCR depletion is used as a purification method, thereby delivering untouched clinical grade engineered immune cells. This enrichment method is applicable to any engineered T cell product with a reduced expression of endogenous αβTCRs. We report on release criteria and the stability of TEG001 drug substance and TEG001 drug product. The GMP-grade production procedure is now approved by Dutch authorities and allows TEG001 to be generated in cell numbers sufficient to treat patients within the approved clinical trial NTR6541. NTR6541 will investigate the safety and tolerability of TEG001 in patients with relapsed/refractory acute myeloid leukemia, high-risk myelodysplastic syndrome, and relapsed/refractory multiple myeloma.
Ramp Distribution-Based Image Enhancement Techniques for Infrared Images

A novel image enhancement method for infrared (IR) images is presented. The proposed method consists of two parts considering the characteristics of high-dynamic-range IR images. First, we attempt to enhance image contrast by introducing the ramp distribution that increases with a constant slope in an ordered histogram domain. The ramp-distributed histogram is incorporated into an optimization problem with a sorted histogram of the input image to calculate a modified histogram. Second, to deal with blurred effects on IR images, we propose a relative edge-strength index for high-boost filtering to effectively suppress noise in relatively uniform regions. Compared with various conventional and state-of-the-art algorithms, the proposed method shows highly competitive performance.
Prevalence of human papillomavirus in oral squamous cell carcinoma: A rural teaching hospital-based cross-sectional study.

BACKGROUND Human papillomavirus (HPV) is a well-established oncogenic agent in the pathogenesis of cervical carcinoma. Its role in the oncogenesis of tumors such as oral squamous cell carcinoma (OSCC) is not clear. Globally, approximately 3% of OSCCs are positive for HPV. Studies conducted in India indicate its prevalence from as low as 0% to as high as 74%. However, a recent Indian study on leukoplakia failed to find any evidence of HPV involvement. This motivated us to reexamine the HPV status in OSCC. AIM To evaluate the prevalence of HPV in OSCC. SETTINGS AND DESIGN This was a rural teaching hospital-based cross-sectional study. SUBJECTS AND METHODS Sixty histopathologically confirmed samples of OSCC were used for the study. Genomic DNA was isolated from frozen, surgically-resected specimens. HPV positivity was tested by polymerase chain reaction-based method using GP5+/6+ primers in the L1 consensus region of the viral genome. RESULTS None of the samples were HPV positive. CONCLUSIONS Results of this study indicate that the association between HPV and OSCC may be overestimated. Hence, multicentric studies covering diverse geographical and socioeconomic groups are needed to delineate the profile of HPV infectivity and OSCC in the Indian subcontinent.
Clinical, Dopaminergic, and Metabolic Correlations in Parkinson Disease: A Dual-Tracer PET Study

Purpose Neuroimaging indicators of Parkinson disease have been developed and applied in clinical practices. Dopaminergic imaging reflects nigrostriatal dopaminergic dysfunction, and metabolic network imaging offers disease-related metabolic changes at a system level. We aimed to elucidate the association between Parkinsonian symptoms and neuroimaging, and interactions between different imaging techniques. Methods We conducted a dual-tracer PET study for the combined assessments of dopaminergic binding (11C-CFT) and glucose metabolism (18F-FDG) in 103 participants with Parkinson disease (65 male and 38 female subjects). The detailed clinical rating scores were systematically collected in all members. The interactions among dopaminergic bindings, metabolic changes, and clinical manifestations were evaluated at voxel, regional, and network levels. Results Striatal DAT binding correlated with akinesia-rigidity (P < 0.001) but not with tremor; the metabolic PET imaging, nonspecific to the dopaminergic dysfunction, disclosed a set of brain regions correlating with the cardinal symptoms, including tremor. In addition, the unilateral symptom correlated with the contralateral nigrostriatal dopamine loss, but with bilateral metabolic changes, suggesting their differences in the application of disease-related mechanistic studies. Further imaging-imaging correlation study revealed that dopaminergic dysfunction correlated with widely distributed metabolic changes in Parkinson disease, and the modest correlations supported the findings on the clinical-imaging correlation. Conclusions In this dual-tracer PET study, we demonstrated the robust interactions among dopaminergic dysfunction, metabolic brain changes and clinical manifestations at voxel, regional, and network levels. Our findings might promote the understanding in the proper application of dopaminergic and metabolic PET imaging in Parkinson disease and offer more evidence in support of Parkinsonian pathophysiological mechanisms.
Cn continuous modelling of smooth contact surfaces using NURBS and application to 2D problems

This paper presents a strategy for the finite element implementation of Cn continuous contact surfaces for deformable bodies undergoing finite deformations, whereby n represents an arbitrary level of continuity. The proposed novel approach avoids the non‐physical oscillations of contact forces which are induced by the traditional enforcement of kinematic contact constraints via faceted surfaces discretizing the interacting boundaries. In particular, for certain problems, the level of continuity may influence the rate of convergence significantly within a non‐linear solution scheme. A hierarchical tree data structure is proposed for an efficient search algorithm to find the neighbour elements on adaptively refined meshes, which are involved in the smoothing process of a particular finite element. The same data structure is used for the automatic detection of the contact surfaces of a body. Three representative numerical examples demonstrate the increased rate of convergence, the ability to trace the actual surface more accurately and the prevention of pressure jumps of the proposed method. Copyright © 2003 John Wiley & Sons, Ltd.
The epithelioid BAP1‐negative and p16‐positive phenotype predicts prolonged survival in pleural mesothelioma

Mesothelioma is a relatively uncommon but highly malignant neoplasm. Most patients die of disease within 1 year of diagnosis, but some have prolonged survival. Prospective identification of these longer‐term survivors may help to guide treatment. We therefore sought to investigate the role of p16 immunohistochemistry (IHC) both alone and in combination with other markers as a potential predictor of prolonged survival in mesothelioma.
Impact of integrated nutrient management on tomato yield under farmers field conditions

Field trials were conducted in farmer's field of Kandhamal district of Odisha, India to assess the impact of integrated nutrient management (INM) on the performance of tomato crop during rabi (2014) and kharif (2015) season. Before conducting trials technological gap between actual and potential productivity were analyzed by interviewing growers to find out the major causes for low yield. Overall gap in use of fertilizers was recorded 64.90% whereas overall mean gap in technology was 43.83%. On farm experiments on INM were conducted by applying FYM (10t ha−1) (NPK) (150: 80: 60 kg ha−2) followed by dipping seedling roots in 1% Azotobacter solution for 15 min and foliar spray with 20 ppm ferrous ammonium sulphate after 30, 45 and 75 days of transplantation. The plant height, root length, number of primary branches, average fruit weight increased in INM plots as compared to farm practice. The increment in yield was found to be 28.84 and 33.86% during rabi and kharif season respectively. The maximum marketable yield obtained in INM plot during kharif and rabl seasons was 1025 q ha−1 and 955 q ha−1 respectively, whereas as farm practice yielded 740 q ha−1 and 713 q ha−1 during the same seasons. The percent loss from total production was recorded 8.5% and 8.8% in control plot and only 4.9% and 5.7% in INM plot durmg rabi and kharif season respectively. The higher fruit weight and lower incidence of disease and pest were observed in INM field in comparison to farm practice. The benefit cost ratio with INM treatment was recorded 4.39 and 4.29 in rabi and kharif season respectively against the benefit cost ratio of 3.10 and 2.94 in control plot during the same respective seasons.
High-definition video coding with super-macroblocks

A high definition video coding technique using super-macroblocks is investigated in this work. Our research is motivated by the observation that the macroblock-based partition in H.264/AVC may not be efficient for high definition video since the maximum macroblock size of 16 x 16 is relatively small against the whole image size. In the proposed super-macboblock based video coding scheme, the original block size MxN in H.264 is scaled to 2Mx2N. Along with the super-macroblock prediction framework, a low-complexity 16 x 16 discrete cosine transform (DCT) is proposed. As compared with the 1D 8 x 8 DCT, only 16 additions are added for a 1D 16 points 16 x 16 DCT. Furthermore, an adaptive scheme is proposed for the selection the best coding mode and best transform size. It is shown by experimental results that the super-macroblock coding scheme can achieve a higher coding gain.
Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders

Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.
Meditation Effects on the Control of Involuntary Contingent Reorienting Revealed With Electroencephalographic and Behavioral Evidence

Prior studies have reported that meditation may improve cognitive functions and those related to attention in particular. Here, the dynamic process of attentional control, which allows subjects to focus attention on their current interests, was investigated. Concentrative meditation aims to cultivate the abilities of continuous focus and redirecting attention from distractions to the object of focus during meditation. However, it remains unclear how meditation may influence attentional reorientation, which involves interaction between both top-down and bottom-up processes. We aimed to investigate the modulating effect of meditation on the mechanisms of contingent reorienting by employing a rapid serial visual presentation (RSVP) task in conjunction with electrophysiological recording. We recruited 26 meditators who had an average of 2.9 years of meditation experience and a control group comprising 26 individuals without any prior experience of meditation. All subjects performed a 30-min meditation and a rest condition with data collected pre- and post-intervention, with each intervention given on different days. The state effect of meditation improved overall accuracy for all subjects irrespective of their group. A group difference was observed across interventions, showing that meditators were more accurate and more efficient at attentional suppression, represented by a larger Pd (distractor positive) amplitude of event related modes (ERMs), for target-like distractors than the control group. The findings suggested that better attentional control with respect to distractors might be facilitated by acquiring experience of and skills related to meditation training.
Early Text Classification Using Multi-Resolution Concept Representations

The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple “views” of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.
Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm

The use of the unmanned aerial vehicles is rapidly growing in ever wider range of applications where military use is among the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles control is the path planning problem that refers to establish the optimal route from the start position to the target, where optimality can be defined in numerous ways. Path planning represents a multi-objective constrained hard optimization problem. In this paper, we adjusted a recent swarm intelligence brain storm optimization algorithm for finding the unmanned combat aerial vehicle optimal path considering fuel consumption and safety degree. The proposed method was tested and compared to eleven different methods from literature. Based on the simulation results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases and has potential for further improvements.
Calibrating Deep Convolutional Gaussian Processes

The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions. Previous work on combining CNNs with Gaussian processes (GPs) has been developed under the assumption that the predictive probabilities of these models are well-calibrated. In this paper we show that, in fact, current combinations of CNNs and GPs are miscalibrated. We proposes a novel combination that considerably outperforms previous approaches on this aspect, while achieving state-of-the-art performance on image classification tasks.
Ranking causal influence of financial markets via directed information graphs

A non-parametric method for ranking stock indices according to their mutual causal influences is presented. Under the assumption that indices reflect the underlying economy of a country, such a ranking indicates which countries exert the most economic influence in an examined subset of the global economy. The proposed method represents the indices as nodes in a directed graph, where the edges' weights are estimates of the pair-wise causal influences, quantified using the directed information functional. This method facilitates using a relatively small number of samples from each index. The indices are then ranked according to their net-flow in the estimated graph (sum of the incoming weights subtracted from the sum of outgoing weights). Daily and minute-by-minute data from nine indices (three from Asia, three from Europe and three from the US) were analyzed. The analysis of daily data indicates that the US indices are the most influential, which is consistent with intuition that the indices representing larger economies usually exert more influence. Yet, it is also shown that an index representing a small economy can strongly influence an index representing a large economy if the smaller economy is indicative of a larger phenomenon. Finally, it is shown that while inter-region interactions can be captured using daily data, intra-region interactions require more frequent samples.
A Phase II Study of Pelareorep (REOLYSIN®) in Combination with Gemcitabine for Patients with Advanced Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with 1 and 5-year survival rates of ~18% and 7% respectively. FOLFIRINOX or gemcitabine in combination with nab-paclitaxel are standard treatment options for metastatic disease. However, both regimens are more toxic than gemcitabine alone. Pelareorep (REOLYSIN®), a proprietary isolate of reovirus Type 3 Dearing, has shown antitumor activity in clinical and preclinical models. In addition to direct cytotoxic effects, pelareorep can trigger antitumor immune responses. Due to the high frequency of RAS mutations in PDAC, we hypothesized that pelareorep would promote selective reovirus replication in pancreatic tumors and enhance the anticancer activity of gemcitabine. Chemotherapy-naïve patients with advanced PDAC were eligible for the study. The primary objective was Clinical Benefit Rate (complete response (CR) + partial response (PR) + stable disease (SD) ≥ 12 weeks) and secondary objectives include overall survival (OS), toxicity, and pharmacodynamics (PD) analysis. The study enrolled 34 patients; results included one partial response, 23 stable disease, and 5 progressive disease. The median OS was 10.2 months, with a 1- and 2-year survival rate of 45% and 24%, respectively. The treatment was well tolerated with manageable nonhematological toxicities. PD analysis revealed reovirus replication within pancreatic tumor and associated apoptosis. Upregulation of immune checkpoint marker PD-L1 suggests future consideration of combining oncolytic virus therapy with anti-PD-L1 inhibitors. We conclude that pelareorep complements single agent gemcitabine in PDAC.