Abstract

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for

Abstract

Systems of differential equations with polynomial right-hand sides are very common in applications. In particular, when restricted to the positive orthant, they appear naturally (according to the law of mass-action kinetics) in ecology, population dynamics, as models of biochemical interaction networks, and models of the spread of infectious diseases. On the other hand, their mathematical analysis is very challenging in general; in particular, it is very difficult to answer questions about the long-term dynamics of the variables (species) in the model, such as questions about persistence and extinction. Even if we restrict our attention to mass-action systems, these questions still remain challenging. On the other hand, if a polynomial dynamical system has a weakly reversible single linkage class (WR1 ) realization, then its long-term dynamics is known to be remarkably robust: all the variables are persistent (i.e., no species goes extinct), irrespective of the values of the parameters in the model. Here we describe an algorithm for finding WR1 realizations of polynomial dynamical systems, whenever such realizations exist.

Abstract

The leading order production of an SM singlet-like scalar has primarily been realized through the gluon fusion process by mixing with the SU(2)_L scalar doublet of the model. The dominant part of the physical state, i.e., the singlet component, does not have any role in its direct production. Focusing on such a state with a mass smaller than the SM-like Higgs scalar, we calculate the dominant next-to-leading (NLO) order corrections to its production cross-section. With these improved cross-sections, the present and future LHC limits may become somewhat more stringent

Abstract

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between

Abstract

The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein–protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To

Abstract

The thermodynamics of the hydrophobic effect, as measured primarily through the temperature dependence of solubility, is reviewed, and then a class of models that incorporate the basic mechanism of hydrophobicity is described. These models predict a quantitative relation between the free energy of hydrophobic hydration and the strength of the solvent-mediated attraction between pairs of solute molecules. It is remarked that the free energy of attraction being just of the order of the thermal energy kT may be important for the effective operation of the hydrophobic effect in proteins. Deviations from pairwise additivity of hydrophobic forces are also briefly discussed.

Abstract

Fact-to-text generation can allow for the generation of high-quality, informative texts such as Wikipedia articles. Cross-lingual fact-to-text generation (XF2T) involves using facts available in a language, typically English, and generating texts in a different language based on these facts. This is particularly relevant for low and medium-resource languages, which have relatively structured informative content. This work explores the problem of XF2T for generating long text from given facts with a specific focus on generating factually grounded content. Unfortunately, previous work either focuses on cross-lingual facts to short text or monolingual graph to text generation. In this paper, we propose a novel solution to the multi-sentence XF2T task, which addresses these challenges by training multilingual Transformer-based models with coverage prompts and rebalanced beam search, and further improving the quality by defining task-specific reward functions and training on them using reinforcement learning. Keywords: XF2T, text generation, cross-lingual, NLG evaluation, low resource NLG

Abstract

Residential energy feedback refers to is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback studies based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental

Abstract

Measuring subjective experience of time helps the designer of the product to understand the usability of the product and the user experience. Time is not given it’s due importance in HCI and particularly in the usability design of the products. Though it is evident that cognitive load impacts time/duration estimation, thereby the user creates a positive or negative valence towards the product, there is no much research done to measure the usability of the products with cognitive load and duration judgement as dependent variables. This paper focuses on empirical investigation of prospective duration judgement as a measure to assess the cognitive load experienced by the users while using the product, one-on-one mentoring mobile app. We consider Jakob Nielsen’s heuristics and meCUE questionnaire as the anchors for our empirical investigation. Index Terms—Human-Computer Interaction, Duration Judge- ment, Usability, User Experience,

Abstract

In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute h- index of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. We also compare h-index of a paper with nine h-index like metrics. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.