Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic significance of systemic immune-inflammation index-based nomogram for early stage hepatocellular carcinoma after radiofrequency ablation.

Background Radiofrequency ablation (RFA) is the recommended treatment for early stage hepatocellular carcinoma (HCC), and the prognostic value of systemic immune-inflammation index (SII) in early stage HCC is not discussed. Therefore, the purpose of the study is to explore the prognostic value of SII based on lymphocyte, neutrophil, and platelet counts in patients with HCC after RFA. Methods We retrospectively evaluated the prognostic value of the SII in training and validation cohorts, and then established an effective nomogram for HCC after RFA based on SII. The C-index, and area under the time-dependent receiver operating characteristic curve (t-AUC) were used to evaluate the discrimination and calibration value of the nomogram. Results An optimal cut-off value for the SII of 324.55×109 stratified the patients with HCC into high- and low-SII groups. Univariate and multivariate analyses revealed that SII was an independent predictor for overall survival (OS) and recurrence-free survival (RFS). Moreover, SII was an independent prognostic factor for early-stage HCC with normal alpha-fetoprotein (AFP) levels. The t-AUC of the SII was higher for OS and RFS than for neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR). A high preoperative SII was associated with multiple tumors, larger tumors, and higher levels of AFP. A well-discriminated and calibrated nomogram was constructed to predict the probability of 1-, 2-, 3-, and 5-year RFS with C-indexes of 0.80, which was significantly higher than that obtained with other prognostic clinical indexes. Conclusions The SII is an independent prognostic factor affecting the survival outcomes of patients with early-stage HCC. The comprehensive nomogram based on SII presented in this study is a promising model for predicting RFS in HCC patients after RFA.
Efficacy of acceptance and commitment therapy (ACT) on depression, pain acceptance, and psychological flexibility in married women with breast cancer: a pre- and post-test clinical trial

Abstract Objective: Breast cancer is the most common cancer in women worldwide. Many of these patients suffer from multiple psychological symptoms. The present study aimed to investigate the impact of acceptance and commitment therapy (ACT) on depression, pain acceptance, and psychological flexibility in married women with breast cancer. Methods: The present study was a pre- and post-test clinical trial with intervention and control groups. The research population consisted of women with breast cancer referred to the Ayatollah Yasrebi and Shahid Beheshti Hospitals in Kashan in 2018. Through a purposive sampling method, 40 women were selected and randomly divided into two groups, namely, intervention (n = 20) and control (n = 20). The applied tools included the Depression, Anxiety and Stress Scale (DASS-21), Chronic Pain Acceptance Questionnaire 8 (CPAQ-8), and Acceptance and Action Questionnaire - II (AAQ-II). Data were analyzed by SPSS 16 using descriptive statistics and analysis of variance (ANOVA). Results: The results showed that ACT treatment significantly reduced the mean scores of depression compared to the control group (F = 107.72, p < 0.001). The mean scores of pain acceptance (F = 9.58, p < 0.05) and psychological flexibility (F = 10.61, p < 0 .05) significantly increased in comparison with the control group. Conclusion: ACT can be considered as an effective therapeutic approach to reduce depression and increase pain acceptance and psychological flexibility in women with breast cancer. These changes appear to be due to improved acceptance of thoughts and feelings associated with cancer and increased psychological flexibility, which is the primary goal of ACT treatment. Clinical trial registration: Iranian Registry of Clinical Trials (IRCT), IRCT20190518043620N1.
Elastic Weight Consolidation (EWC): Nuts and Bolts

In this report, we present a theoretical support of the continual learning method \textbf{Elastic Weight Consolidation}, introduced in paper titled `Overcoming catastrophic forgetting in neural networks'. Being one of the most cited paper in regularized methods for continual learning, this report disentangles the underlying concept of the proposed objective function. We assume that the reader is aware of the basic terminologies of continual learning.
Upper and lower bounds on the size of $B_{k}[g]$ sets

A subset A of the integers is a Bk[g] set if the number of multisets from A that sum to any fixed integer is at most g. Let Fk,g(n) denote the maximum size of a Bk[g] set in {1, . , n}. In this paper we improve the best-known upper bounds on Fk,g(n) for g > 1 and k large. When g = 1 we match the best upper bound of Green with an improved error term. Additionally, we give a lower bound on Fk,g(n) that matches a construction of Lindström while removing one of the hypotheses.
Optimal Energy Management of a Hybrid Electric Helicopter for Urban Air-Mobility

In this study, a hybrid electric helicopter for air-taxi operations is considered. The drivetrain includes a turboshaft engine and two electric machines, fed by a lithium ion battery. In previous works, some of the authors developed a simple but thorough modelling approach for the electric path of the power system that was validated by means of experimental data from literature. In this investigation simulation results from the Gas-turbine Simulation Program (GSP) commercial environment were used to develop and validate an off design model for the turboshaft. The second innovative contribution of the paper is the application of Dynamic Programming to four different missions of the helicopter to quantify the fuel saving potentiality of hybridization and also as benchmark for future online control strategies. A constraint was considered in the investigation to allow at any time the electric backup in case of engine failure. The results of DPM showed that it is possible to obtain a reduction of fuel burn from 12% to 24% (with respect to using only the engine to move the rotor) depending on the specification of the mission and the state of health of the battery. Moreover, it was proved that charging the battery on board is not necessary in this kind of application.
Adjusting for Unmeasured Confounding in Marginal Structural Models with Propensity-Score Fixed Effects

Marginal structural models are a popular tool for investigating the effects of timevarying treatments, but they require an assumption of no unobserved confounders between the treatment and outcome. With observational data, this assumption may be difficult to maintain, and in studies with panel data, many researchers use fixed effects models to purge the data of time-constant unmeasured confounding. Unfortunately, traditional linear fixed effects models are not suitable for estimating the effects of time-varying treatments, since they can only estimate lagged effects under implausible assumptions. To resolve this tension, we a propose a novel inverse probability of treatment weighting estimator with propensity-score fixed effects to adjust for time-constant unmeasured confounding in marginal structural models of fixed-length treatment histories. We show that these estimators are consistent and asymptotically normal when the number of units and time periods grow at a similar rate. Unlike traditional fixed effect models, this approach works evenwhen the outcome is onlymeasured at a single point in time as is common inmarginal structural models. We apply these methods to estimating the effect of negative advertising on the electoral success of candidates for statewide offices in the United States. *Thanks to Adam Glynn for extensive discussions and feedback. We also thank Gary King for generous comments. Any errors remain our own. Comments welcome. †Associate Professor, Department of Government, Harvard University. Email: mblackwell@gov.harvard. edu. Web: www.mattblackwell. org ‡Graduate student, Department of Government, Harvard University. Email: syamauchi@g.harvard. edu. Web: https://soichiroy.github. io 1 ar X iv :2 10 5. 03 47 8v 2 [ st at . M E ] 9 J un 2 02 1
Did I delete my cookies? Cookies respawning with browser fingerprinting

Stateful and stateless web tracking gathered much attention in the last decade, however they were always measured separately. To the best of our knowledge, our study is the first to detect and measure cookie respawning with browser and machine fingerprinting. We develop a detection methodology that allows us to detect cookies dependency on browser and machine features. Our results show that 1,150 out of the top 30, 000 Alexa websites deploy this tracking mechanism. We further uncover how domains collaborate to respawn cookies through fingerprinting. We find out that this technique can be used to track users across websites even when third-party cookies are deprecated. Together with a legal scholar, we conclude that cookie respawning with browser fingerprinting lacks legal interpretation under the GDPR and the ePrivacy directive, but its use in practice may breach them, thus subjecting it to fines up to 20 million euro.
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime

Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field. However, the goal is not simply to identify which run is "best", achieving the top score. The goal is to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This paper uses the MS MARCO and TREC Deep Learning Track as our case study, comparing it to the case of TREC ad hoc ranking in the 1990s. We show how the design of the evaluation effort can encourage or discourage certain outcomes, and raising questions about internal and external validity of results. We provide some analysis of certain pitfalls, and a statement of best practices for avoiding such pitfalls. We summarize the progress of the effort so far, and describe our desired end state of "robust usefulness", along with steps that might be required to get us there.
Automata-based Algorithm for Multiple Word Matching

In this paper, an automata-based algorithm that finds the valid shifts of a given set of words W in text T is presented. Unlike known string matching algorithms, a preprocessing phase is applied to T and not to the words being searched for. In this phase, a deterministic finite state automaton (DFA) that recognizes the words in T is built and is augmented with their shifts in T. The preprocessing phase is relatively expensive in terms of time and space. However, it needs to be done once for any number of words to match in a given text document. The algorithm is analyzed for complexity, implemented and compared with an adjusted version of KMP algorithm. It showed better performance than KMP algorithm for large number of words to match in T. Keywords—Algorithms; finite state automata; word matching; KMP
Fast Sequence-Matching Enhanced Viewpoint-Invariant 3-D Place Recognition

Recognizing the same place undervariant viewpoint differences is the fundamental capability for human beings and animals. However, such a strong place recognition ability in robotics is still an unsolved problem. Extracting local invariant descriptors from the same place under various viewpoint differences is difficult. This article seeks to provide robots with a human-like place recognition ability using a new 3-D feature learning method. This article proposes a novel lightweight 3-D place recognition and fast sequence matching to achieve robust 3-D place recognition, capable of recognizing places from a previous trajectory regardless of viewpoints and temporary observation differences. Specifically, we extracted the viewpoint-invariant place feature from 2-D spherical perspectives by leveraging spherical harmonics’ orientation-equivalent property. To improve sequence-matching efficiency, we designed a coarse-to-fine fast sequence-matching mechanism to balance the matching efficiency and accuracy. Despite the apparent simplicity, our proposed approach outperforms the relative state of the art. In both public and self-gathered datasets with orientation/translation differences or noise observations, our method can achieve above 95% average recall for the best match with only 18% inference time of PointNet-based place recognition methods.
A Public Opinion Analysis System Based on Emotion Analysis in Linyi

This article, the official news site of Linyi five services in Shanghai, Yiwu GanZhou Shenzhen national logistics hub of news as the data source, through Word2Vec and construction LSTM emotion classification model, with positive or negative emotion in general categories, calculating to analyze its emotional value, from the emotional category and time series analysis and word frequency vector to Linyi public opinion analysis of logistics hub
Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches: Extended Version

Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializable in this way or when some of the subproblems have a high width. In this work, we address these limitations by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches. A policy sketch R over a set of Boolean and numerical features is a set of sketch rules that express how the values of these features are supposed to change. Like general policies, policy sketches are domain general, but unlike policies, the changes captured by sketch rules do not need to be achieved in a single step. We show that many planning domains that cannot be solved by SIW are provably solvable in low polynomial time with the SIW_R algorithm, the version of SIW that employs user-provided policy sketches. Policy sketches are thus shown to be a powerful language for expressing domain-specific knowledge in a simple and compact way and a convenient alternative to languages such as HTNs or temporal logics. Furthermore, they make it easy to express general problem decompositions and prove key properties of them like their width and complexity.
The effect of green marketing, green supply chain and green human resources on business performance: Balanced scorecard approach

Green management and its implementation and practices attracted the attention of both practitioners and academicians alike, which in part is due to its positive impact on the environment and sustainability, however, little is known about what really measures the effect of green management practices on business performance. Conventional measures of financial performance cannot reflect the proposed effect since green management is long-term oriented and not limited to financial performance only. This research explores the potentials of using balanced scorecard to measure the effect of green marketing, green supply chain and green human resources on the performance of the firms. Simple random sampling technique was followed in this research, and data were collected from 113 managers from different companies in service and manufacturing sectors in UAE. The findings indicate that green marketing was the major determinant of customers’ performance, green human resources was the major factor affecting learning and growth, and that green supply chain was the main influencer on financial performance and internal processes.
Robust Graph Learning Under Wasserstein Uncertainty

Graphs are playing a crucial role in different fields since they are powerful tools to unveil intrinsic relationships among signals. In many scenarios, an accurate graph structure representing signals is not available at all and that motivates people to learn a reliable graph structure directly from observed signals. However, in real life, it is inevitable that there exists uncertainty in the observed signals due to noise measurements or limited observability, which causes a reduction in reliability of the learned graph. To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data. Specifically, two models are developed, one of which assumes all distributions in uncertainty set are Gaussian distributions and the other one has no prior distributional assumption. Instead of using interior point method directly, we propose two algorithms to solve the corresponding models and show that our algorithms are more time-saving. In addition, we also reformulate both two models into Semi-Definite Programming (SDP), and illustrate that they are intractable in the scenario of large-scale graph. Experiments on both synthetic and real world data are carried out to validate the proposed framework, which show that our scheme can learn a reliable graph in the context of uncertainty.
Contacts, equilibration, and interactions in fractional quantum Hall edge transport

C. Sp̊anslätt, 2, 3 Yuval Gefen, 2 I. V. Gornyi, 3, 5 and D. G. Polyakov Department of Microtechnology and Nanoscience, Chalmers Institute of Technology, S-412 96 Göteborg, Sweden Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, 76021 Karlsruhe, Germany Institut für Theorie der Kondensierten Materie, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany Department of Condensed Matter Physics, Weizmann Institute of Science, Rehovot 76100, Israel Ioffe Institute, 194021 St.Petersburg, Russia (Dated: May 11, 2021)
Poverty and the Incidence of Material Hardship, Revisited

The last twenty years have seen little decline in the incidence of poverty in the United States. In fact, after a decline in poverty during the strong economy of the 1990s, poverty increased in the wake of two recessions. Much less is known about trends in material hardships , such as food insufficiency and housing problems, which are intrinsically important outcomes. Using data from the 1992-2011 Survey of Income and Program Participation, we examine trends in seven types of material hardship (food, housing, neighborhood, bill paying, health, fear of crime, and lack of consumer durables) and how their incidence by poverty status changed over the period. We find declines in four of the seven hardships, with little change or moderate increases for the others. Declines were larger for hardship more dependent on longer-term income flows, while those more sensitive to short-term income fluctuations declined by less (or increased), suggesting that income volatility imposes important challenges for many households. Of key interest, declines in hardship were evident across all measures among the lowest-income groups over the period. This may result from a greater under-reporting of income over time and/or that family resources are not comprehensively counted in the official poverty measure.
Potential applications of deep eutectic solvents in natural gas sweetening for CO2 capture

Abstract Novel solvents named deep eutectic solvents (DESs) have been intensively investigated in recent years. Their non-toxicity, biodegradability, low volatility, easy preparation and low cost make them promising green solvents for several industrial processes. This article provides a status review of the possible applications of DESs in natural gas (NG) sweetening by carbon dioxide (CO2) capturing. Investigations on preparation and chemical structures of DESs were reported. In addition, very recent physiochemical properties and applications of DESs were well discussed. Mainly, experimental and predicted solubilities of CO2 in DESs were reported and compared in this work. It was deduced that DESs offer some potential advantages over the conventional solvents, i.e. alkanolamines, in terms of absorption efficiency, easy preparation and low economic cost. It is expected that in coming decades, DESs will offer potential use in NG technology. Further investigations are highly required to highlight more about these neoteric solvents.
Fast and Error-Adaptive Influence Maximization based on Count-Distinct Sketches

Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are frequently used for IM. In this work, we describe a fast, error-adaptive approach that leverages Count-Distinct sketches and hash-based fused sampling. To estimate the number of influenced vertices throughout a diffusion, we use per-vertex Flajolet-Martin sketches where each sketch corresponds to a sampled subgraph. To efficiently simulate the diffusions, the reach-set cardinalities of a single vertex are stored in memory in a consecutive fashion. This allows the proposed algorithm to estimate the number of influenced vertices in a single step for simulations at once. For a faster IM kernel, we rebuild the sketches in parallel only after observing estimation errors above a given threshold. Our experimental results show that the proposed algorithm yields high-quality seed sets while being up to 119x faster than a state-of-the-art approximation algorithm. In addition, it is up to 62x faster than a sketch-based approach while producing seed sets with 3%-12% better influence scores
An Online Framework for Joint Network Selection and Service Placement in Mobile Edge Computing

With the rapid development and deployment of 5G wireless technology, mobile edge computing (MEC) has emerged as a new computing paradigm to facilitate a large variety of infrastructures at the network edge to reduce user-perceived communication delay. One of the fundamental problems in this new paradigm is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and often capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works have primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. In this work, we study the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving the QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. Specifically, we propose an efficient online framework to decompose a long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm based on matching and game theory, which achieves a near-optimal solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations are conducted to validate the efficacy of our proposed solution.