Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area

Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through the evaluation of important drivers is, however, a challenging work. The main purpose of this study was to perform a quantitative evaluation of the current desertification status in the Segzi Plain, Isfahan Province, Iran, through the modified Mediterranean Desertification and Land Use (MEDALUS) model and GIS. In this regard, five main indicators including soil, groundwater, vegetation cover, climate, and erosion were selected for estimating the environmental sensitivity to desertification. Each of these qualitative indicators is driven by human interference and climate. After statistical analysis and a normality test for each indicator data, spatial distribution maps were established. Then, the maps were scored in the MEDALUS approach, and the current desertification status in the study area from the geometric mean of all five quality indicators was created. Based on the results of the modified MEDALUS model, about 23.5% of the total area can be classified as high risk to desertification and 76.5% classified as very high risk to desertification. The results indicate that climate, vegetation, and groundwater quality are the most important drivers for desertification in the study area. Erosion (wind and water) and soil indices have minimal importance.
The Input/Output Complexity

We provide tight upper and lower bounds, up to a constant factor, for the number of inputs and outputs (I/OS) between internal memo y and seconda y storage required for five sorting-related problems: sorting, the fast Fourier transform (FFT), permutation networks, permuting, and matrix transposition. The bounds hold both in the worst case and in the average case, and in several situations the constant factors match. Secondary storage is modeled as a magnetic disk capable of transferring P blocks each containing B records in a single time unit; the records in each block must be input from or output to B contiguous locations on the disk. We give two optimal algorithms for the problems, which are variants of merge sorting and distribution sorting. In particular we show for P = 1 that the standard merge sorting algorithm is an optimal external sorting method, up to a constant factor in the number of I/OS. Our sorting algorithms use the same number of I/OS as does the permutation phase of key sorting, except when the internal memo y size is extremely small, thus affirming the popular adage that key sorting is not faster. We also give a simpler and more direct derivation of Hong and Kung’s lower bound for the FFT for the special case B = P = O(1).
The Enrichment of Science Teaching Materials Sourced from Marine Aquaculture Ecology to Increase Scientific Literacy of Students’ Junior High School

The environment is an interactive learning resource to improve students' scientific literacy, such as the study location's marine cultivation area environment. This study aimed to integrate the marine culture environment's ecological parameters for the enrichment of teaching materials in improving students' scientific literacy. The research method used observation and experiment—the non-equivalent pretest-posttest control group design—sampling technique using a total sampling technique. The research data collection used multiple-choice tests, and the data were analyzed using the independent sample ttest with the help of SPSS 17.00. The results showed that students at the initial stage had limited knowledge about the marine culture environment relevant to the teaching material. However, relatively all students understand correctly the location of cultivation and cultivation techniques developed by the community. Furthermore, the results of the analysis of the independent sample t-test showed that the significant value of students' scientific literacy skills was less than 0.05 (p <0.05). The conclusion is that the enrichment of science teaching materials sourced from marine cultivation ecology can significantly increase students' science literacy. Therefore, scientific learning that only standardization material from school books with fabric sourced from the environment. Keywords—teaching materials, ecological of marine culture, scientific literacy, and junior high school students
How does neighborhood walkability affect obesity? The mediating role of commute mode

The walkability of a neighborhood is closely related to residents’ travel behavior and daily life and, ultimately, their health and wellbeing. Although existing studies in this area have reached some enlightening conclusions, few of them have considered residents’ travel attitudes and preferences, or the mediating role of commute mode. Do travel attitudes and preferences matter in the relationship between neighborhood walkability and residents being obese? How does commute mode work as a mediator? To answer these questions, based on the 2019 travel survey data in Guangzhou, this paper uses the Multilevel Linear Model (MLM) to examine the association between neighborhood walkability and residents’ body mass index (BMI). Furthermore, the Mediation Model is used to identify the mediating role of commute mode in the relationship between walkability and BMI. The results show that (1) travel attitudes and preferences do affect the individual’s BMI through the mediator of commute mode. (2) After controlling the individual socio-demographics and travel attitudes and preferences, neighborhood walkability has a significant negative effect on BMI. Meanwhile, walkability has a significant positive effect on the use of non-private motorized commute modes. Non-private motorized commute modes have a significant negative effect on BMI. (3) The mediating effect of commute mode in the relationship of neighborhood walkability with the individual’s BMI is significant. The proportion of mediation is 32.90%. Insights into the relationship between neighborhood walkability, commute mode, and individual BMI highlight the importance of walkable neighborhoods that encourage people to use healthy commute modes.
Implications of comprehensive geriatric assessment on quality of life in older adults with cancer: an integrative review

Comprehensive geriatric assessment (CGA) improves the quality of care for older adults with cancer, as it identifies geriatric problems and weaknesses that have implications for the health of the individual. Despite the benefits of CGA, difficulties related to time of application and cost of this tool limit its implementation in practice. The purpose of this review is to evaluate the relationship between CGA and the quality of life (QoL) of older adults with cancer, through an integrative review. A search was performed for articles in the PubMed, Medline, IBECS and Lilacs databases, published between 2015 and 2020, that addressed the implications of CGA on the QoL of older adults with cancer and, of the 298 studies found, 21 were selected for analysis. These studies revealed that CGA performs an important role in identifying older adults with a higher risk of QoL impairment during the course of cancer and cancer treatment, as well as guiding the indication of specific geriatric interventions that prevent the deterioration of QoL. Thus, the present review highlights the importance of the broad assessment of older adults with cancer, which, through different spheres, whether prognostic or interventionist, can play a fundamental role in preserving the QoL of this population. It is imperative that strategies are developed that incorporate CGA in the care of older adults with cancer. 1 Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Escola de Medicina, Programa de Pós-Graduação em Gerontologia Biomédica. Porto Alegre, RS, Brasil. 2 Hospital Mãe de Deus, Centro Integrado de Oncologia. Porto Alegre, RS, Brasil. The authors declare there are no conflicts of interest in relation to the present study. No funding was received in relation to the present study. Correspondence Valéria Sgnaolin v.sgnaolin@gmail.com Received: October 1, 2020 Approved: April 26, 2021 ID
Momentum Strategies and Stock Returns: A Case of Saudi Stock Market

This paper investigates the presence of momentum profits in the Saudi stock market. The study applied a quantitative method by utilizing monthly closing prices of 194 listed firms on Tadawal (Saudi Stock Market). The data from January 2010 to February 2019 is taken from the Tadawal market database for analysis. The sample is further divided into two equal sub-samples based on the structural changes that occurred in the Saudi stock market. Moreover, the highand low-value traded portfolios are also constructed to examine the presence of momentum profits. Sixteen investment strategies are formed for each sample. The results show a very strong presence of momentum profits in the Saudi stock market for the full sample as well as for the sub-samples. The momentum profits are observed for a longer investment horizon. The results confirm that the short or medium-term formation of portfolios produces negative momentum returns for high-value traded stocks. The low-value traded stocks portfolios give similar results to the full sample results in terms of momentum profits. The results suggest that an investor should keep an eye on the past performance of desired stocks for at least three-nine months in which they are willing to invest.
SITE RESPONSE STUDY OF A DEEP BASIN CONTAGIOUS TO ACTIVE REGION- UTTAR PRADESH REGION, INDIA

Uttar Pradesh region (UPR) of India had experienced catastrophic earthquake damages due to the presence of thick soil depth of 0.05 km to about 4 km. However, very few studies have been carried out to characterize the UPR soil up to the shallow depth and very limited attempts have been made to measure the dynamic properties for deeper depths. Hence, in this study, firstly, the shear velocity profile up to 300 m depth is measured using combined active and passive multichannel analysis of surface wave (MASW) survey in 75 selected locations in UPR. Further, these sites are classified and characterized based on time-averaged in the upper 30 m depth as per NEHRP seismic site classification. The measured Vs profiles are used to estimate the site-specific response parameters at different locations by carrying out non-linear site response analysis. Input ground motions are selected from the worldwide-recorded database based on the seismicity of the region. Shear modulus and damping curve are selected based on the stratigraphical variation of the soil column in UPR. Based on the analysis, it has determined that for the amplification range of site class E, D, C and B is 2.35 to 7.58, 1.82 to 5.58, 1.5 to 4.92 and 1.23 to 3.25 respectively. The first time, representative site response for deep soil column and amplification factors for the different periods for different seismic site class are estimated for UPR region which would be further useful in developing a design response spectrum for deep deposits in India.
Phase delays in 
ω−2ω
 above-threshold ionization

Relative phases of atomic above-threshold ionization wavepackets have been investigated in a recent experiment [L. J. Zipp, A. Natan, and P. H. Bucksbaum, Optica 1, 361-364 (2014)] exploiting interferences between different pathways in a weak probe field at half the frequency of the strong ionization pulse. In this work we theoretically explore the extraction of phase delays and time delays of attosecond wavepackets formed in strong-field ionization. We perform simulations solving the time-dependent Schrödinger equation and compare these results with the strong-field and Coulomb-Volkov approximations. In order to disentangle shortfrom longranged effects of the atomic potential we also perform simulations for atomic model potentials featuring a Yukawa-type short-range potential. We find significant deviations of the ab-initio phase delays between different photoelectron pathways from the predictions by the strong-field approximation even at energies well above the ionization threshold. We identify similarities but also profound differences to the well-known interferometric extraction of phaseand time delays in one-photon ionization. PACS numbers: 32.80. Rm,32.80. Fb,03.65. Sq
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been much work on benchmark datasets needed to track modeling progress. Question answering and reading comprehension have been particularly prolific in this regard, with more than 80 new datasets appearing in the past 2 years. This study is the largest survey of the field to date. We provide an overview of the various formats and domains of the current resources, highlighting the current lacunae for future work. We further discuss the current classifications of “skills” that question answering/reading comprehension systems are supposed to acquire and propose a new taxonomy. The supplementary materials survey the current multilingual resources and monolingual resources for languages other than English, and we discuss the implications of overfocusing on English. The study is aimed at both practitioners looking for pointers to the wealth of existing data and at researchers working on new resources.
On the hardness of knowing busy beaver values BB(15) and BB(5, 4)

The busy beaver value BB($n$) is the maximum number of steps made by any $n$-state, 2-symbol deterministic halting Turing machine starting on blank tape, and BB($n,k$) denotes its $k$-symbol generalisation to $k\geq 2$. The busy beaver function $n \mapsto \text{BB}(n)$ is uncomputable and its values have been linked to hard open problems in mathematics and notions of unprovability. In this paper, we show that there are two explicit Turing machines, one with 15 states and 2 symbols, the other with 5 states and 4 symbols, that halt if and only if the following Collatz-related conjecture by Erd\H{o}s [1979] does not hold does not hold: for all $n>8$ there is at least one digit 2 in the base 3 representation of $2^n$. This result implies that knowing the values of BB(15) or BB(5,4) is at least as hard as solving Erd\H{o}s' conjecture and makes, to date, BB(15) the smallest busy beaver value that is related to a natural open problem in mathematics. For comparison, Yedidia and Aaronson [2016] show that knowing BB(4,888) and BB(5,372) are as hard as solving Goldbach's conjecture and the Riemann hypothesis, respectively (later informally improved to BB(27) and BB(744)). Finally, our result puts a finite, albeit large, bound on Erd\H{o}s' conjecture, by making it equivalent to the following finite statement: for all $8
COPS: Controlled Pruning Before Training Starts

State-of-the-art deep neural network (DNN) pruning techniques, applied one-shot before training starts, evaluate sparse architectures with the help of a single criterion-called pruning score. Pruning weights based on a solitary score works well for some architectures and pruning rates but may also fail for other ones. As a common baseline for pruning scores, we introduce the notion of a generalized synaptic score (GSS). In this work we do not concentrate on a single pruning criterion, but provide a framework for combining arbitrary GSSs to create more powerful pruning strategies. These COmbined Pruning Scores (COPS) are obtained by solving a constrained optimization problem. Optimizing for more than one score prevents the sparse network to overly specialize on an individual task, thus COntrols Pruning before training Starts. The combinatorial optimization problem given by COPS is relaxed on a linear program (LP). This LP is solved analytically and determines a solution for COPS. Furthermore, an algorithm to compute it for two scores numerically is proposed and evaluated. Solving COPS in such a way has lower complexity than the best general LP solver. In our experiments we compared pruning with COPS against state-of-the-art methods for different network architectures and image classification tasks and obtained improved results.
Towards Black-box Attacks on Deep Learning Apps

Deep learning is a powerful weapon to boost application performance in many fields, including face recognition, object detection, image classification, natural language understanding, and recommendation system. With the rapid increase in the computing power of mobile devices, developers can embed deep learning models into their apps for building more competitive products with more accurate and faster responses. Although there are several works about adversarial attacks against deep learning models in mobile apps, they all need information about the models' internals (i.e., structures, weights) or need to modify the models. In this paper, we propose an effective black-box approach by training a substitute model to spoof the deep learning system inside the apps. To evaluate our approach, we select 10 real-world deep-learning apps with high popularity from Google Play to perform black-box adversarial attacks. Through the study, we find three factors that can influence the performance of attacks. Our approach can reach a relatively high attack success rate of 66.60% on average. Compared with other adversarial attacks on mobile deep learning models, in terms of the average attack success rates, our approach outperforms counterparts by 27.63%.
KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation

Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those users without a background in data visualizations. However, existing rule-based approaches require tedious manual specifications of visualization rules by visualization experts. Other machine learning-based approaches often work like black-box and are difficult to understand why a specific visualization is recommended, limiting the wider adoption of these approaches. This paper fills the gap by presenting KG4Vis, a knowledge graph (KG)-based approach for visualization recommendation. It does not require manual specifications of visualization rules and can also guarantee good explainability. Specifically, we propose a framework for building knowledge graphs, consisting of three types of entities (i.e., data features, data columns and visualization design choices) and the relations between them, to model the mapping rules between data and effective visualizations. A TransE-based embedding technique is employed to learn the embeddings of both entities and relations of the knowledge graph from existing dataset-visualization pairs. Such embeddings intrinsically model the desirable visualization rules. Then, given a new dataset, effective visualizations can be inferred from the knowledge graph with semantically meaningful rules. We conducted extensive evaluations to assess the proposed approach, including quantitative comparisons, case studies and expert interviews. The results demonstrate the effectiveness of our approach.
Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers

Self-supervised pre-training of large-scale transformer models on text corpora followed by finetuning has achieved state-of-the-art on a number of natural language processing tasks. Recently, Lu et al. (2021, arXiv:2103.05247) claimed that frozen pretrained transformers (FPTs) match or outperform training from scratch as well as unfrozen (fine-tuned) pretrained transformers in a set of transfer tasks to other modalities. In our work, we find that this result is, in fact, an artifact of not tuning the learning rates. After carefully redesigning the empirical setup, we find that when tuning learning rates properly, pretrained transformers do outperform or match training from scratch in all of our tasks, but only as long as the entire model is finetuned. Thus, while transfer from pretrained language models to other modalities does indeed provide gains and hints at exciting possibilities for future work, properly tuning hyperparameters is important for arriving at robust findings.
SpectGRASP: Robotic Grasping by Spectral Correlation

This paper presents a spectral correlation-based method (SpectGRASP) for robotic grasping of arbitrarily shaped, unknown objects. Given a point cloud of an object, SpectGRASP extracts contact points on the object’s surface matching the hand configuration. It neither requires offline training nor a-priori object models. We propose a novel Binary Extended Gaussian Image (BEGI), which represents the point cloud surface normals of both object and robot fingers as signals on a 2-sphere. Spherical harmonics are then used to estimate the correlation between fingers and object BEGIs. The resulting spectral correlation density function provides a similarity measure of gripper and object surface normals. This is highly efficient in that it is simultaneously evaluated at all possible finger rotations in SO(3). A set of contact points are then extracted for each finger using rotations with high correlation values. We then use our previous work, Local Contact Moment (LoCoMo) similarity metric, to sequentially rank the generated grasps such that the one with maximum likelihood is executed. We evaluate the performance of SpectGRASP by conducting experiments with a 7-axis robot fitted with a parallel-jaw gripper, in a physics simulation environment. Obtained results indicate that the method not only can grasp individual objects, but also can successfully clear randomly organized groups of objects. The SpectGRASP method also outperforms the closest state-of-the-art method in terms of grasp generation time and grasp-efficiency.
Next-Generation Multiple Access Based on NOMA With Power Level Modulation

To cope with the explosive traffic growth expected in next-generation wireless networks, it is necessary to design next-generation multiple access techniques that can provide higher spectral efficiency as well as larger-scale connectivity. As a promising candidate, power-domain non-orthogonal multiple access (NOMA) has been widely studied. In conventional power-domain NOMA, multiple users are multiplexed in the same time and frequency band with different preset power levels, which, however, may limit the spectral efficiency under practical finite alphabet inputs. Inspired by the concept of spatial modulation, we propose to solve this problem by encoding extra information bits into the power levels, and exploiting different signal constellations to help the receiver distinguish between them. To convey this idea, termed power selection (PS)-NOMA, clearly, we consider a simple downlink two-user NOMA system with finite input constellations. Assuming maximum-likelihood detection, we derive closed-form approximate bit error rate (BER) expressions for both users. Moreover, the two-user achievable rate region is also characterized. Simulation results verify the analysis and show that the proposed PS-NOMA can outperform conventional NOMA in terms of BER and achievable rate.