Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of muscle-invasive bladder cancer based on tumor stromal compartment

Purpose There has been a resurgence of interest in the tumor stroma in recent years. Whether the relative abundance of various stromal cells can be used as a classification system for muscle-invasive bladder cancer (MIBC) remain elusive.Methods We applied single-cell RNA-sequence (scRNA-seq) data from two MIBCs to identify stromal cell (CD45 negative cells) clusters and the marker gene set of each cell cluster. The single sample gene set enrichment analysis method is used to estimate the relative abundance of the cell clusters in each MIBC sample from The Cancer Genome Atlas. Subsequently, k-means clustering was performed to cluster the MIBCs. Prognosis, oncogenic pathway enrichment score, epithelial-mesenchymal transition (EMT) score, gene mutation frequency, and tumor infiltrating lymphocytes (TILs) were compared among the stromal component-based types of MIBC.ResultsIn the scRNA-seq analysis, a total of nine cell clusters mainly composed of four types of cells were identified. Cell clusters 0, 1, 2, 3, 4, and 7 were considered as bladder epithelial cells, the cells in clusters 5 and 8 were mainly recognized as stem cells and fibroblasts, and the cell cluster 6 was recognized were endothelial cells. The 408 MIBC samples were classified into three types. Type 1 and 3, the “stromal-sufficient” type I and II with higher stromal cells, and Type 2, the “stromal-desert” type with lower stromal cells. The stromal-desert type had significantly better overall survival. As the tumor progresses, the stromal component of the stromal-desert type increases and change into the stromal-sufficient type. Increased stromal cells may come from EMT. The enrichment scores of multiple oncogenic pathways in stromal-sufficient types were significantly higher than those in stromal-desert type. More TILs were found in stromal-sufficient type, but their function may be inhibited by stromal cells. In addition, the three stromal types of MIBC may have specific gene mutation characteristics.Conclusions We proposed a novel stromal component-based classification system to divided into MIBC three phenotype. The three types of MIBC differ in various biological characteristics. The progress of MIBC may be summarized as the process of gradually increasing stromal components and constructing a microenvironment suitable for cancer cells.
A user-friendly tool for metrical analysis of Sanskrit verse

: This paper describes the design and implementation of a tool that assists readers of metrical verse in Sanskrit (and other languages/literatures with similar prosody). It is open-source, and available online as a web application, as a command-line tool and as a software library. It handles both varṇavṛtta and mātrāvṛtta metres. It has many features for usability without placing strict demands on its users. These include allowing input in a wide variety of transliteration schemes, being fairly robust against typographical or metrical errors in the input, and “aligning” the given verse in light of the recognized metre. This paper describes the various components of the system and its user interface, and details of interest such as the heuristics used in the identiﬁer and the dynamic-programming algorithm used for displaying results. Although originally and primarily designed to help readers, the tool can also be used for additional applications such as detecting metrical errors in digital texts (its very ﬁrst version identiﬁed 23 errors in a Sanskrit text from an online corpus), and generating statistics about metres found in a larger text or corpus. These applications are illustrated here, along with plans for future improvements.
Hierarchical Core Maintenance on Large Dynamic Graphs

The model of k -core and its decomposition have been applied in various areas, such as social networks, the world wide web, and biology. A graph can be decomposed into an elegant k -core hierarchy to facilitate cohesive subgraph discovery and network analysis. As many real-life graphs are fast evolving, existing works proposed efficient algorithms to maintain the coreness value of every vertex against structure changes. However, the maintenance of the k -core hierarchy in existing studies is not complete because the connections among different k -cores in the hierarchy are not considered. In this paper, we study hierarchical core maintenance which is to compute the k -core hierarchy incrementally against graph dynamics. The problem is challenging because the change of hierarchy may be large and complex even for a slight graph update. In order to precisely locate the area affected by graph dynamics, we conduct in-depth analyses on the structural properties of the hierarchy, and propose well-designed local update techniques. Our algorithms significantly outperform the baselines on runtime by up to 3 orders of magnitude, as demonstrated on 10 real-world large graphs.
Miscommunication in Interactive Listeni An Evaluation of Listener Indecision an Cognitive Effort

com ommuMethod: An eye and computer mouse–tracking vi world paradigm was used to investigate how a list cognitive effort (local and global) and decision-ma processes were affected by a speaker’s use of amb that led to a miscommunication. Results: Experiments 1 and 2 found that an environ cue that made a miscommunication more or less s impacted listener language processing effort (eye-tra Experiment 2 also indicated that listeners may dev different processing heuristics dependent upon the s
NEOCLASSICAL THEORY OF GROWTH IN TEN SELECTED ASIAN COUNTRIES USING PANEL DATA

Attention to the theories of economic growth has intensified when differences between output growth rates of industrialized countries on one hand and the amount and quality of economic growth of these countries with developing countries on the other hand emerged. Also, when the growth rate of industrial production in some countries declined, many economists considered the reasons and these two, led to design of numerous growth patterns. In this study we have tested the neoclassical growth theory in ten Asian countries using panel data over 2000-2009. At the end, relevant tests showed that in regard to the F obtained, models of panel data is more suitable for this series of data. Additionally, for final analysis and use of fixed or random effects, Hausman test showed that fixed effects are more valid. Finally, the results demonstrated that the degree of freedom had a negative effect and both labor and capital accumulation had a direct impact on economic growth in these countries; therefore planning on these fields will have great effect on economic growth.
Running head: MOOD CONTAGION IN SOCIAL NETWORKS Sharing the load: Contagion and Tolerance of Mood in Social Networks

The relations between self and others are fluid and constantly changing but exert a profound influence on our identity and emotional experiences. Indeed, human emotions are frequently and intensely social, and the people with whom we interact can alter our momentary mood. But does emotional ‘contagion’ extend over prolonged periods of hours to days, and if so, how does it propagate through interconnected groups? Answering this question is empirically challenging, since mood similarity in connected individuals can arise through multiple mechanisms (social influence, social selection, and shared external causation), making causal inferences hard to draw. We address this challenge using temporally high-resolution, longitudinal data from two independent, bounded social networks during periods of high communal activity and low external contact. Adolescent study participants (N=79) completed daily mood (n=4724) and social interaction (n=1775) ratings during residential performance tours of classical music lasting five to seven days. Analyses using statistical network models show that in both networks, adolescent musicians became reciprocally more similar in mood to their interaction partners. The observed contagion effect was greater for negative than for positive mood. That is, while one may ‘catch’ a friend’s bad mood, the friend may feel less negative in the process. These results suggest a mechanism for emotional buffering and the ‘cost’ of social support. We found no evidence for social selection based on mood. Indeed, participants were remarkably tolerant of their peers’ mood fluctuations, and showed no evidence of altering their patterns of social interaction accordingly.
Party survival in parliament: Explaining party durability in lower-house parliaments

Approximately 70 per cent of the parties emerging in the post-SecondWorldWar era failed to keep their seats in parliament.Party survival is an important issue, especially in parliamentary democracies,where parties are the means through which voters’ preferences are linked to government policy outputs. Using an event history modeling framework, and data from 37 democracies, covering 830 parties, this article analyses two questions regarding party durability. First, when do parties fail? Second, which parties survive longer? The article shows that most parties fail at the beginning of their lifespan, and disappear before the end of their fourth term in parliament. Moreover, it is found that moderate policy position, distinct ideology and participation in governing coalitions increase the duration of party survival, even when controlling for party size.This article contributes to the extensive literature about the electoral benefits of ideological moderation and distinct policy positions by showing the long-term benefits of these factors. Moreover, the long-term benefit of party participation in government in terms of survival overcomes the short-term cost of ruling.
Critical Issues in Combining Disparate Sources of Information to Estimate the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure

We develop a hybrid meta-analytic approach to estimating uncertainty from combining information on risk from a small number of studies. This method is most useful when pooling risk estimates among a limited number of studies with little observed variation compared to their sampling error. It is based on integrating features of both Frequentist and Bayesian approaches to meta-analysis. We present an example of mortality risk due to long-term exposure to ambient fine particulate matter obtained from cohort studies conducted in the United States and Europe. Study-specific pooled risk estimates are then further combined with risk information from other sources of particulate exposure at much higher concentrations, such as active and second hand smoking, to extrapolate risk from low concentrations observed in the outdoor air pollution cohort studies to the higher levels observed in developing countries. We estimate the relative risk for each study and source of particulate pollution at the range in study-specific exposure and treat these estimates as input data in order to characterize the shape of the exposure-response function. In this manner we are able to derive the form of the risk function without direct access to primary data. We thus derive a risk model that covers the entire global range in exposure. Uncertainty in risk predictions are obtained using simulation methods that incorporate uncertainty in the input source-specific risk estimates.
The impact of tick sizes on trader behavior: Evidence from cryptocurrency exchanges

This paper analyses the effect of a tick size on a major cryptocurrency exchange where spreads are unconstrained, assets have limited fundamental value and tick sizes are extremely small, which facilitates undercutting. Using a unique high frequency dataset surrounding a significant increase in tick sizes on the cryptocurrency exchange Kraken, we find that undercutting decreases, leading to traders posting more and larger limit orders. Transaction costs and short-term volatility both decrease, contrary to previous findings in equity markets. We show that when spreads are unconstrained, market quality can be improved by increasing extremely small tick sizes. Our findings contribute to the optimal tick size debate, with particular implications for cryptocurrency and foreign exchange markets, where tick sizes are typically very small. JEL classification: G14 G15 C580
Synthesizing Tabular Data using Conditional GAN

In data science, the ability to model the distribution of rows in tabular data and generate realistic synthetic data enables various important applications including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of discrete and continuous columns, building such a model is a non-trivial task. Continuous columns may have multiple modes, while discrete columns are sometimes imbalanced, making modeling difficult. To address this problem, I took two major steps. (1) I designed SDGym, a thorough benchmark, to compare existing models, identify different properties of tabular data and analyze how these properties challenge different models. Our experimental results show that statistical models, such as Bayesian networks, that are constrained to a fixed family of available distributions cannot model tabular data effectively, especially when both continuous and discrete columns are included. Recently proposed deep generative models are capable of modeling more sophisticated distributions, but cannot outperform Bayesian network models in practice, because the network structure and learning procedure are not optimized for tabular data which may contain non-Gaussian continuous columns and imbalanced discrete columns. (2) To address these problems, I designed CTGAN, which uses a conditional generative adversarial network to address the challenges in modeling tabular data. Because CTGAN uses reversible data transformations and is trained by re-sampling the data, it can address common challenges in synthetic data generation. I evaluated CTGAN on the benchmark and showed that it consistently and significantly outperforms existing statistical and deep learning models. Thesis Supervisor: Kalyan Veeramachaneni Title: Principal Research Scientist Laboratory for Information and Decision Systems
The Microwave Spectrum of Oxazole

The dipole moments and the quadrupole coupling constants of the normal and the three mono-deuterated species of oxazole have been measured. The dipole moment of 1.50 ± 0.03 D is found to deviate by 10.8° from the external bisector of the CNC angle towards the C(2) carbon atom. The principal values of the quadrupole coupling tensor are determined as Xzz = -4.04 ± 0.02 MHz and Xxx - 1-66 ± 0.02 MHz along the axes in the molecular plane, so that the gradient perpendicular to this plane is Xyy = 2.38 MHz. The direction of the largest gradient deviates by 5.7° from the external bisector of the CNC angle towards the carbon atom C(4). An attempt is made to correlate these results and the geometry of oxazole with the electron distribution in this molecule
Catalytic Membrane and their Photocatalytic Properties

A solution method was developed to synthesized heteronanostructure of Ag nanoparticles on titanate nanowires, and then flexible photocatalytic Films were made by these nanowires. The nanowire is about 50 nm in diameter, several hundreds micrometers in length. The size of the Ag nanoparticle grafted on the nanowire is about 50 to 100 nm. The nanowire Film showed excellent photocatalytic activity to decompose the methamidophos and satisfactory antibacterial effect to kill Escherichia coli.
Cancer Biotechnology

Artificial intelligence (AI) is the technology that is used for understanding the data and objects which usually requires human intelligence but with more accuracy and speed. Explosion of data and need for accurate analysis in a short duration, led to the development of AI, which can help learn and process the object or data under consideration. AI is being used for several advanced technology and security purposes such as data analysis, image processing, data security, fraud detection, speech recognition, chatbots and for algorithm trading [1]. Lately, the use of AI in healthcare has become imperative due to large amount of data in the healthcare sector and for effective diagnosis and prognosis of complex diseases such as cancer and diabetes. Cancer is one such menace to human health and one of the leading causes of death among both men and women globally. According to the World Health Organization ‘WHO’, approximately 9.6 million deaths occurred due to cancer in 2018. As Figure 1 suggests, low and middle income countries are worst affected by cancer with about 70% of cancer deaths reported in the developing countries [2].
Multidisciplinary Rehabilitation of a Patient With Neuromyelitis Optica

* Corresponding Author: Ghodsiyeh Joveini, PhD. Candidate Address: Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran. Tel: +98 (21) 22228051 E-mail: joveiniot@yahoo.com Background and Objectives: Neuro Myelitis Optica (NMO) is a rare progressive and disabling autoimmune disease. The disabling consequences of the disease affect many aspects of the patients and their family life. multidisciplinary rehabilitation can be very effective in promoting quality of life and slowing disease progression by working with different disciplines. The aim of this study is to report the effects of multidisciplinary rehabilitation on the performance and quality of life of a patient with NMO and her family.
Mark My (Foreign) Words : Text-internal and -external Functions of Multilingualism in Dramas

This paper proposes a theoretically-driven framework for the analysis of the dramatic stylisation of speech-styles marked as foreign. In particular, it highlights the dramatic function of the (con-)figuration of multilingualism in theatre and investigates the relation of dramatic, text-internal functions and social, text-external functions of multilingualism. The paper combines theoretical concepts of multilingualism from (socio-)linguistics and literary theory in order to develop an analytical framework for the interpretation of multilingual passages in selected dramas. It tests this framework by analysing two comedies that originated in the Habsburg Monarchy before 1848, namely Der böse Geist Lumpacivagabundus (1833) by Johann Nepomuk Nestroy and Fidlovačka aneb Žádný hněv a žádná rvačka (1834) by Josef Kajetán Tyl. Despite belonging to different literary traditions—Austrian-German and Czech, respectively—they can be read in parallel: Both include a passage situated in contemporary Prague and arranged around a parlour discourse, shaped by various stylisations of foreign speech.
MTab4DBpedia: Semantic Annotation for Tabular Data with DBpedia

Semantic annotation for tabular data with knowledge graphs is a process of matching table elements to knowledge graph concepts, then annotated tables could be useful for other downstream tasks such as data analytic, management, and data science applications. Nevertheless, the semantic annotations are complicated due to the lack of table metadata or description, ambiguous or noisy table headers, and table content. In this paper, we present an automatic semantic annotation system designed for the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019), called MTab4DBpedia, to generate semantic annotations for table elements with DBpedia concepts. In particular, our system could generate Cell-Entity Annotation ( CEA ), Column-Type Annotation ( CTA ), Column Relation-Property Annotation ( CPA ). MTab4DBpedia combines joint probability signals from different table elements and majority voting to solve the matching challenges on data noisiness, schema heterogeneity, and ambiguity. Results on SemTab 2019 show that our system consistently obtains the best performance for the three matching tasks: the 1 st rank all rounds (the four rounds), and all tasks (the three tasks) of SemTab 2019. Additionally, this paper also provides our reﬂections from a participant’s perspective and insightful analysis and discussion on the general benchmark for tabular data matching.
Planning for Flood Evacuation: A Location-allocation Model with Shelter Capacity Constraints

The optimal shelter locations and flow allocations for the flood evacuation planning are examined in this paper. It is assumed that the authority can control the traffic in certain part of the network while the evacuees select which shelter to go and by which route. The location-allocation problem is formulated as bi-level program, interpreted as Stackelberg game, consisting of the leader (authority) and the follower (evacuees). The authority determines the location of shelters to minimize the total evacuation time, and evacuees decide on shelter and route to evacuate simultaneously. The upper level sub-problem is modeled as location problem to represent the authority’s decision on shelter location. To represent evacuees’ decision, a combined distribution and assignment (CDA) model is proposed as lower level sub-problem. Due to difficulty of bi-level problem 1 Lecturer, Department of Teacher Training in Civil Engineering, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok. 2 Associate Professor, Civil and Environmental Engineering Department, College of Engineering, Utah State University. * Corresponding Author, Tel.0-2913-2500, Ext.3247, E-mail: ssk@kmutnb.ac. th Received May 29, 2009, Accepted July 28, 2009
Reproducing Generalizing Hamiltonian Monte Carlo with Neural Networks

As part of the ICLR 2018 Reproducibility Challenge, we reproduce the results from Learning to Hamiltonian Monte Carlo (L2HMC; Lévy et al., 2018), a general-purpose method to train Markov chain Monte Carlo (MCMC) kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. We reproduce the core part of the work, namely automatic training of MCMC samplers applied to simple yet challenging distributions where Hamiltonian Monte Carlo (HMC) tends to fail. We show that most experiments conducted in the paper are correct and further propose an additional test to better understand subtle design decisions made by the authors. The code for all experiments is available online on Github. 1
About the Potential of active User-Chatbot-Customiza-tion for the User Experience Design

Chatbots have become a worldwide phenomenon and find application in many different domains. However, their inability to reliably succeed in social contexts (e.g., on Social Network Sites, in therapy) significantly influences the user’s experience. To improve user experience developers and scholars have mostly concentrated on strategies that originate from the developers’ side. Less attention has been paid to the active involvement of the user and to potential customization options. Several psychological mechanisms suggest active customization to facilitate user engagement with the technology and potentially cushion conversational issues that still widely exist in today’s chatbot interactions. Re-viewing existing literature and related empirical findings on this topic, we advocate the position that active User-Chatbot-Customization has great potential for user experience design and the improvement of user engagement in particular. Going forward, certain theoretical, empirical and methodological aspects discussed here must be overcome in order to further exploit this potential. Directions for future research are outlined.