Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impacts of Atmospheric and Surface Parameters on Long-Term Variations in the Planetary Albedo

Planetary albedo (PA; shortwave broadband albedo) and its long-term variations, which are controlled in a complex way by various atmospheric and surface properties, play a key role in controlling the global and regional energy budget. This study investigates the contributions of different atmospheric and surface properties to the long-term variations of PA based on 13 years (2003–15) of albedo, cloud, and ice coverage datasets from the Clouds and the Earth’s Radiant Energy System (CERES) Single Scanner Footprint edition 4A product, vegetation product from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface albedo product from the Cloud, Albedo, and Radiation dataset, version 2 (CLARA-A2). According to the temporal correlation analysis, statistical results indicate that variations in PA are closely related to the variations of cloud properties (e.g., cloud fraction, ice water path, and liquid water path) and surface parameters (e.g., ice/snow percent coverage and normalized difference vegetation index), but their temporal relationships vary among the different regions. Generally, the stepwise multiple linear regression models can capture the observed PA anomalies for most regions. Based on the contribution calculation, cloud fraction dominates the variability of PA in the mid- and low latitudes while ice/snow percent coverage (or surface albedo) dominates the variability in the mid- and high latitudes. Changes in cloud liquid water path and ice water path are the secondary dominant factor over most regions, whereas change in vegetation cover is the least important factor over land. These results verify the effects of atmospheric and surface factors on planetary albedo changes and thus may be of benefit for improving the parameterization of the PA and determining the climate feedbacks.
Reprints Available Directly from the Publisher Photocopying Permitted by License Only (c) 2000 Opa (overseas Publishers Association) N.v. Magnified Examination of Small Colorectal Polyps Using a Prototype Electronic Endoscope: Preliminary Experience

Magnifying electronic endoscopes are frequently used to evaluate the pit patterns of the colorectal mucosa, but such endoscopes suffer from a number of problems. For example, they tend to have long, hard tips and heavy controller sections. In addition, the magnified endoscopic images obtained are often quite coarse due to the small number of pixels in the charge-coupled device (CCD). As a result, at higher magnification ratios, the orientation of the field of view is easily lost. A newly developed prototype colorectal electronic endoscope (Toshiba Corporation, Tokyo) overcomes these problems. The length of the hard tip of the scope and the weight of the controller section are comparable to those of the TCE-3680MH (Toshiba Corporation). High-resolution magnified images can be obtained, because a 410,000-pixel CCD is employed. Two magnification methods are available, optical magnification and electronic zooming, permitting images to be magnified by a factor of up to 90-120 without losing the orientation of the field ofview. This newly developed magnifying electronic endoscope was found to be very useful, allowing us to observe the pit patterns of the colorectal mucosa in 82 small colorectal polyps measuring 7 mm or less in diameter.
Privacy in the Smart Grid: An Information Flow Analysis

Smart meters, smart devices, and gateways allowing automated control of in-home devices are linchpins in an ambitious vision of creating a Smart Grid that will increase efficiency, improve grid resilience and reliability, and reduce peak demand. The collection, retention, and use of detailed usage data, however, put individual privacy at risk. Utilities, commercial third parties, law enforcement agents, parties in civil litigation, and criminals can discern from usage patterns whether a home is occupied and, to some extent, what is occurring inside. The two-way communication channel also supports remote control of appliances to manage load. The ability to remotely control in-home electricity use through controlling devices within the home raises new security and privacy issues. The Smart Grid is developing rapidly. Smart Grid systems are generating, collecting, and processing information that is far more voluminous and revealing than traditional meter data. Decisions about how best to address the emerging privacy issues – whether through technical design, best practices, or regulation – lag behind development of the system infrastructure. This report documents the Smart Grid information flows and considers the laws and agencies that protect, or could protect, privacy in this new technological landscape. Legal sources of privacy protection are highly varied, ranging from state public utilities commissions to the Federal Trade Commission. The extent and level of privacy protection depends critically upon the route information takes from source to destination. Though state utilities regulators have traditionally played a strong role in protecting customer privacy, like other regulators, their jurisdiction is limited. Changes in the architecture of the energy grid that create new data flows and empower new players to handle data threaten to render some privacy provisions obsolete and others ineffective. Given the proliferation of data, industry players, and usage models, new laws and privacy-protecting technical designs are necessary to afford privacy, comparable to that enjoyed today, to users of tomorrow’s energy network. Considering privacy upfront, rather than after technologies are deployed, will help build privacy protections into the Smart Grid while supporting other energy policy goals.
Chimeric Antigen Receptor T Cell Therapy for Hepatocellular Carcinoma: Where Do We Stand?

Hepatocellular carcinoma (HCC) remains a global health challenge that urgently calls for innovative therapeutic strategies. Chimeric antigen receptor T cell (CAR T) therapy has emerged as a promising avenue for HCC treatment. However, the therapeutic efficacy of CAR T immunotherapy in HCC patients is significantly compromised by some major issues including the immunosuppressive environment within the tumor, antigen heterogeneity, CAR T cell exhaustion, and the advanced risk for on-target/off-tumor toxicity. To overcome these challenges, many ongoing preclinical and clinical trials are underway focusing on the identification of optimal target antigens and the decryption of the immunosuppressive milieu of HCC. Moreover, limited tumor infiltration constitutes a significant obstacle of CAR T cell therapy that should be addressed. The continuous effort to design molecular targets for CAR cells highlights the importance for a more practical approach for CAR-modified cell manufacturing. This review critically examines the current landscape of CAR T cell therapy for HCC, shedding light on the changes in innate and adaptive immune responses in the context of HCC, identifying potential CAR T cell targets, and exploring approaches to overcome inherent challenges. Ongoing advancements in scientific research and convergence of diverse treatment modalities offer the potential to greatly enhance HCC patients’ care in the future.
Directions in Psychological

30 years since Marr described the computational level, the strategy of seeking computational-level explanations has grown in popularity. As probabilistic models have been applied to more aspects of cognition, it has become increasingly important to understand the implications of such computational-level analyses for algorithmic-and implementation-level analyses. Elucidating this relationship can inform both predictions across tasks and the theoretical constraints that hold between levels of analysis. On the empirical side, a fruitful strategy for identifying cognitive processes is to look for ways in which human behavior deviates from ideal solutions obtained from computational
Identification of Molecular Mechanisms in Radiation Cystitis: Insights from RNA Sequencing

Pelvic cancer survivors who were treated with radiation therapy are at risk for developing (hemorrhagic) radiation cystitis (RC) many years after completion of radiation therapy. Patients with RC suffer from lower urinary tract symptoms, including frequency, nocturia, pelvic pain, and incontinence. In advanced stages, hematuria can occur, potentially escalating to life-threatening levels. Current therapeutic options for RC are limited, partly due to ethical concerns regarding bladder biopsy in patients with fragile bladder tissue. This study aimed to leverage our established preclinical model to elucidate the molecular pathways implicated in radiation-induced tissue changes in the bladder. Female C57Bl/6 mice received a single dose of 40 Gy using CT-guided imaging and a two-beam irradiation approach using the SARRP irradiator. Bladders from irradiated and age-matched littermate controls were harvested at 1 week [n = 5/group] or 6 months [n = 5/group] after irradiation, RNA was harvested, and mRNA sequencing was performed at paired-end 150bp on the Illumina NovaSeq6000 with a target of 30 million reads per sample. Following RNA sequencing, thorough bioinformatics analysis was performed using iPathwayGuide v2012 (ADVAITA Bioinformatics). Findings of the RNA sequencing were validated using qPCR analysis. At 1 week post-irradiation, altered gene expression was detected in genes involved in DNA damage response, apoptosis, and transcriptional regulation. By 6 months post-irradiation, significant changes in gene expression were observed in inflammation, collagen catabolism, and vascular health. Affected pathways included the p53, JAK-STAT, and PI3K-Akt pathways. These findings were validated in vivo in bladder tissues from our preclinical model. This is the first study to determine the molecular changes in the bladder in response to radiation treatment. We have successfully pinpointed several pathways and specific genes that undergo modification, thereby contributing to the progression of radiation cystitis. These insights enhance our understanding of the pathophysiology of radiation cystitis and may ultimately pave the way to the identification of potential new therapeutic targets.
Bounded Next Value and Explanatory Identi cationLearning Automata , Patterns and Polynomials On-Line

This paper considers learning via predicting the next value { this concept is also known as \on-line learning" or \forecasting". The concept is combined with the limited memory model and has two variants: Exact NV-learning has a polynomial resource bound depending on the sizes of current input and the concept on long term memoryand on working space (or time); in addition the number of errors is limited by a polynomial in the concept size. Independent NV-learning has polynomial resource bounds depending on the size of the current input only on long term memoryand on working space (time). The following is shown: A class of functions is independently NV-learnable i(cid:11) it is uniformly computable in PSPACE. Exact NV-learning is a proper restriction of independent NV-learning. For the well-known classes of pattern languages, regular languages and polynomials, it is investigated under which variations of the resource bounds they are learnable or not learnable. Also an explanatory version of resource bounded learning is de(cid:12)ned. It is more powerful than next value learning. Furthermore while next value learning is closed under union, this type of explanatory learning has a proper (cid:3) This paper appeared in the Proceedings of the conference COLT 1997. The copyright of these proceedings is due to the Association of Computing Machinery and due to their policy the following note is given: Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or speci(cid:12)c permission.
AIS Electronic

: The explosive penetration of mobile devices is one of the most prominent trends in e-business. Although the importance of mobile channel has prompted growing literature, little is known about the revenue implications of customer visit toward mobile channel. This study examines (1) the differential effect of mobile visits in affecting firm revenue (i.e. mobile vs. desktop visits), and (2) which type of mobile visits are more effective (i.e., direct vs. search engine and referral traffic; visits for high vs. low involvement products). We collect an unique objective daily data from a leading online travel agency in China. With a vector autoregressive (VAR) method, we find that, compared with desktop channel, mobile channel visits have shorter carryover effect, but larger short-term effect on firm revenues. Further, mobile channel has larger short-term effect on firm revenues for search engine traffic and lower involvement products. Our findings provide important theoretical contributions and notable implications for mobile commerce strategy.
De(cid:12)ning Image Content with Multiple Regions-of-Interest

With the proliferation of multimedia, the web and digital imaging, there now exists a high demand for intelligent tools for image management, most importantly indexing, search and retrieval, commonly referred to as "query-by-image-content". Existing systems often make use of global attributes such as overall color distributions which ignore the actual composition of the image in terms of internal structures. In this paper we present an image retrieval system predicated on the principle that it is the user who is most quali(cid:12)ed to specify the "content" in an image and not the computer. Therefore, the user is asked to provide salient "regions-of-interest" or ROIs and specify the importance of their spatial relationships in the query image. This technique leads to acceptable retrievals (equal if not better than global-based searches) and provides an intuitive interface that is more in tune with the user's notion of \content", thus providing a more powerful image retrieval tool.
Modulation filtering using an optimization approach to spectrogram reconstruction

Summary Modulations across time and frequency are known from previous studies to play a signiﬁcant role for speech intelligibility. Hence, well-chosen manipulations of modulations via an accurate tool to systematically modify the modulation content of a signal might be useful for the improvement of speech intelligibility. This study investigates modulation ﬁltering in a time-frequency representation of the signal (e.g., a spectrogram), using a novel approach for reconstructing a signal from its modiﬁed representation. It is suggested that this synthesis is regarded as an optimization problem, where the variables are the time samples of the output signal and where the cost function to minimize is the diﬀerence between the target spectrogram and the current spectrogram. This approach is made feasible, with regard to the large number of variables involved, by use of a limited-memory optimization algorithm. This study presents basic results regarding temporal modulation ﬁltering and discusses the novel method and its possibilities of improvement.
Multioutput least square SVR-based multivariate EWMA control chart: The performance evaluation and application

Abstract Autocorrelation leads to a bias estimator of standard control charts. It is important to develop control chart that allows autocorrelation and to evaluate its performance. The objective of this paper is to evaluate the performance of multioutput least square support vector regression (MLS-SVR)-based multivariate exponentially weighted moving average (MEWMA) control chart for monitoring multivariate autocorrelated data. For first order of vector autoregressive (VAR) and first order of vector moving average data, the proposed control chart tends to yield stable in-control average run length at about 200. The proposed control chart becomes more insensitive due to the increase of MEWMA smoothing parameter. In the real application, the proposed method is successfully applied to monitor water turbidity and chlorine residual data in the drinking water manufacturing process.
Dominant Resource Fairness : Fair Allocation of Multiple Resource Types

In this report I will first describe the work of Ghodsi et. al and their contributions. I will explain the notion of Dominant Resource Fairness in policies for the allocation of multi-type resources, and describe the analysis of its valuable properties. In the second part I will discuss my attempts in extending these results in 3 new directions: analysis of DRF in the discrete case (when only whole tasks can be executed), resource utilization of DRF solutions, and DRF with assignment constraints.
Knowledge-Based Systems

Biosanitary experts around the world are directing their efforts towards the study of COVID-19. This effort generates a large volume of scientific publications at a speed that makes the effective acquisition of new knowledge difficult. Therefore, Information Systems are needed to assist biosanitary experts in accessing, consulting and analyzing these publications. In this work we develop a study of the variables involved in the development of a Question Answering system that receives a set of questions asked by experts about the disease COVID-19 and its causal virus SARS-CoV-2, and provides a ranked list of expert-level answers to each question. In particular, we address the interrelation of the Information Retrieval and the Answer Extraction steps. We found that a recall based document retrieval that leaves to a neural answer extraction module the scanning of the whole documents to find the best answer is a better strategy than relying in a precise passage retrieval before extracting the answer span.
Variational-and Metric-based Deep Latent Space for Out-of-Distribution Detection

This document contains the following: 1) the mathematical formulation of the KL loss; 2) the technical details of our experiments, including the values of the hyperparameters that we used, and the details for the training of the deep nets; 3) results on each benchmark in the OOD-detection task (in the paper we reported only the average results across those benchmarks); 4) empirical evaluation of different features for the reconstruction weighting; 5) details regarding the datasets used in the paper; 6) implementation and hardware details; 7) an intuitive explanation for the effect of the overall proposed loss; 8) details of the auto-encoders used for the reconstruction step.
Crowd Evacuation using C

Abstract — In this work, we pro ose afor simulating and studying crowd evacuatiapply it to study the evacuation of a soccer stauses 2D cellular automata defined over murepresent different levels of the simulated pedestrian moves from a given grid cellanalyzing multiple movement options and rwith other pedestrians.Validation tests wdemonstrating that the proposed model is crowd evacuation successfully. Keywords: Crowd Evacuation; Cellular AStadium I. I NTRODUCAO A simulacao de evacuacao de multidoedo computador permite o estudo detalhado,comportamento da multidao ao tentar abandem uma situacao de emergencia [1] e [2]. Em [3], os autores ressaltam que ummodelos tipicamente utilizados em evacuacao e o baseado em automatos celula ellular Automata: Simulation in a So Bento Cavalcante-Neto, Creto Augusto Vidal, Teo Departamento de Computacao Universidade Federal do Ceara Fortaleza, CE, Brasil imb@lia.ufc. br, cvidal@lia.ufc. br, teofilo.dutra@gmail. 1. Visao geral das principais etapas do modelo proposto
CoGenT++: an extensive and extensible data environment for computational genomics

MOTIVATION CoGenT++ is a data environment for computational research in comparative and functional genomics, designed to address issues of consistency, reproducibility, scalability and accessibility. DESCRIPTION CoGenT++ facilitates the re-distribution of all fully sequenced and published genomes, storing information about species, gene names and protein sequences. We describe our scalable implementation of ProXSim, a continually updated all-against-all similarity database, which stores pairwise relationships between all genome sequences. Based on these similarities, derived databases are generated for gene fusions--AllFuse, putative orthologs--OFAM, protein families--TRIBES, phylogenetic profiles--ProfUse and phylogenetic trees. Extensions based on the CoGenT++ environment include disease gene prediction, pattern discovery, automated domain detection, genome annotation and ancestral reconstruction. CONCLUSION CoGenT++ provides a comprehensive environment for computational genomics, accessible primarily for large-scale analyses as well as manual browsing.
Embodying cognition: a proposal for visualising mental representations in virtual environments

This paper examines the possibility of visualising the most abstract knowledge in virtual environments: mental representations that are involved in the basic cognitive processes. Metaphorisation is a key tool for creating virtual environments capable of embodying what is in the mind. The aim of these environments is to improve the learning and rehabilitation of users with cognitive disabilities. We propose to design symbolic environments in which concepts are converted into a bodily experience by means of the metaphorical projection from the abstract to the physical domain. Our proposal is illustrated by the description of a case study: the representation of categories in a virtual environment for blind children.
From polynomial time queries to graph structure theory

In a fundamental article on query languages for relational databases, Chandra and Harel [2] asked in 1982 whether there is a language that expresses precisely those queries which can be answered in polynomial time. Gurevich [10] later rephrased the question in the language of finite model theory, asking whether there is a logic that captures polynomial time. Despite serious efforts in the late 1980s and the 1990s, the question is still wide open. It is considered to be one of the main open problems in database theory and finite model theory. Recently, there has been a renewed interest in the question. New languages have been proposed [1, 4, 5] and old ones reconsidered [3, 12], and a number of partial results stating that certain languages capture polynomial time on large and natural classes of structures have been obtained [6, 8, 9, 11]. My talk will be a survey of the state of the art in the "quest for a logic capturing polynomial time. " The focus will be on positive results for restricted classes of structures. This will lead us on an excursion to modern graph structure theory, and in particular to Robertson and Seymour's Graph Minor Theory [13]. Besides the references cited in the abstract, the following list contains a reference to the short survey [7].
A schedulable garbage collection for embedded applications in CLI

Common language infrastructure (CLI) has been introduced as a core technology of Microsoft . NET. It enables "writing in multiple languages, running in multiple platforms" by providing virtual execution system (VES), common intermediate language, and common type system etc. The advantages of using CLI, including portability, compactness, and interoperability, could benefit the productivity of application software development and deployment. However, for embedded real-time systems, the applications' time-constraints cannot be satisfied easily due to several features of CLI runtime environment, such as thread priority, thread scheduling, garbage collection etc. In this paper, we aim to have a garbage collection mechanism applicable on real-time applications in CLI and other virtual machine environments. We achieve the goal by making the pause time of garbage collection operations predictable, and the invocation of garbage collection and applications schedulable. A cost model based on measured WCET is established to predict the execution time and overhead of garbage collection operations.