Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors

ABSTRACT The Cropland Data Layers (CDL) are high-resolution geo-referenced data products made available by the U.S. Department of Agriculture. However, the CDL lacks in its ability to be employed as a tool to identify the impact of the gradually evolving drivers of land use change, e.g., climate change, due to its limited historical depth. We implement a robust, phenology-based satellite image classification algorithm to identify historical cropland allocation in eastern South Dakota and North Dakota predating the initial CDL by 13 and 22 years, respectively. Five major land covers, i.e., corn, soybeans, wheat, alfalfa and grass (including native grass, hay and pasture) are identified using archived Landsat-5 surface reflectance data, while achieving CDL-like accuracy. The long-term rate of grassland loss during 1985–2011 is found to be significantly lower (26,781 hectares or 1.5% annually) relative to the near-term rate of grassland loss during 2006-’11 (84,545 hectares or 5.2% annually). We find similar discrepancy in regional corn expansion rates. Our value-added raster data provide opportunities for improved identification of land use drivers, whereas relying solely on the CDL’s restricted historical extent may lead to biased land use change estimates and misguide policy.
Augur: a Decentralized Oracle and Prediction Market Platform (v2.0)

Augur is a trustless, decentralized oracle and platform for prediction markets. The outcomes of Augur’s prediction markets are chosen by users that hold Augur’s native Reputation token, who stake their tokens on the actual observed outcome and, in return, receive settlement fees from the markets. Augur’s incentive structure is designed to ensure that honest, accurate reporting of outcomes is always the most proﬁtable option for Reputation token holders. Token holders can post progressively-larger Reputation bonds to dispute proposed market outcomes. If the size of these bonds reaches a certain threshold, Reputation splits into multiple versions, one for each possible outcome of the disputed market; token holders must then exchange their Reputation tokens for one of these versions. Versions of Reputation which do not correspond to the real-world outcome will become worthless, as no one will participate in prediction markets unless they are conﬁdent that the markets will resolve correctly. Therefore, token holders will select the only version of Reputation which they know will continue to have value: the version that corresponds to reality.
EEG – Eye Tracking: A Wavelet Packets Approach

In this project, we investigate a classical approach of an Electroencephalography (EEG) based gaze estimation. EEG data is often noisy and is affected by baseline drift, and needs to be preprocessed before modelling. We propose a wavelet packet based approach to remove baseline drift from the EEG data. We then use a regression model to map the EEG data to the eye movement data and compare results to basic linear regression.
EDUCATIONAL GAME DESIGN: BRIDGING THE GAB BETWEEN COMPUTER BASED LEARNING AND EXPERIMENTAL LEARNING ENVIRONMENTS

Considering the rapidly growing amount of digital educational materials only few of them bridge the gab between experimental learning environments and computer based learning environments (Gardner, 1991). Observations from two cases in primary school and lower secondary school in the subject of home economics shows that problems in computer based learning occurs when the basis of the learning process changes from being hands-on experimental with the subject to being experienced through or with a computer. Some of the central problems in home economics are to force abstraction from the specific course to general principles of cooking, food, variation, energy and nutrition. We believe, that the computer can help doing this. This paper describes a innovative model for integrating technology as a part of practical experimental working with own ideas. The model is exemplified with a prototype of a MOO storyline. The aim of the MOO storyline is to challenge the potential of dialogue, user involvement, and learning responsibility and to use the children’s natural curiosity and motivation for game playing, especially when digital games involves other children. The paper proposes a model, based on the narrative approach for experimental learning subjects, relying on ideas from Csikszentmihalyis notion of flow (Csikszentmihalyi, 1991), storyline-pedagogy (Meldgaard, 1994) and ideas from Howard Gardner (Gardner, 1991). The model forms the basis for educational games to be used in home economics, and thus is a step on the road to produce a learning resource in an area, until now mostly populated with electronic recipe-managers and digital health-tracking ditto.
SE(3) Based LTV-MPC Algorithm for Multi-Obstacle Trajectory Tracking of Fully Driven Spacecraft

A linear time-varying model predictive control (LTV-MPC) method based on <inline-formula> <tex-math notation="LaTeX">$SE(3)$ </tex-math></inline-formula> is proposed, to enhance the control precision and reduce energy consumption of the spacecraft. First, left-invariant principle of Lie group, Lie algebra <inline-formula> <tex-math notation="LaTeX">$SO(3)$ </tex-math></inline-formula>, and other differential geometry theories are applied to extend LTV-MPC to <inline-formula> <tex-math notation="LaTeX">$SE(3)$ </tex-math></inline-formula>. Then, considering the obstacle avoidance problem of the spacecraft in space, a suitable optimization function is selected to ensure smooth tracking of the desired working trajectory. By controlling the incremental output with the fastest convergence rate, the controller enables effective trajectory tracking. Finally, the effectiveness and applicability of the controller are verified by simulation experiments. These simulations validate the controller’s ability to achieve constraint satisfaction, optimize transient processes, and enhance control accuracy. The results provide compelling evidence of the controller’s potential for real-world spacecraft control applications.
Modulation of offset analgesia in patients with chronic pain and healthy subjects – a systematic review and meta-analysis

Abstract Objectives Offset analgesia (OA) induces a brief pain inhibition and studies suggest OA impairment in patients with chronic pain when compared to healthy subjects. Conditioned pain modulation remains the most studied descending pain inhibitory control mechanism and is modulated by centrally-acting analgesics. Since OA may be mediated by similar neural substrates as conditioned pain modulation, understanding if OA is a peripheral or central proxy of pain modulation is important. The modulatory effect of centrally-acting drugs on OA in healthy and chronic pain populations has not yet been systematically reviewed and meta-analyzed, and this systematic review and meta-analysis aimed to identify studies employing interventions for modulating OA magnitude. Methods A systematic search of PubMed, Embase, Web of Science, and the Cochrane Library yielded 146 records of which 11 (172 healthy pain-free subjects, 106 chronic pain patients) were eligible for qualitative synthesis, and 10 for meta-analysis on overall modulatory effect of interventions on OA, and subgroup analysis of patients and healthy pain-free subjects. Results Risk of bias was evident for study participation and study confounding in the included studies. Several different methods for assessing and calculating OA magnitude were identified, which may affect interpretability of findings and warrants standardization. The meta-analysis showed no modulatory effects on OA overall (standardized mean difference (SMD) [95%CI]: 0.04 [−0.22, 0.30], Z=0.29, p=0.77), or in the subgroup analysis for patients (SMD [95%CI]: −0.04 [−0.63, 0.71], Z=0.13, p=0.90) or healthy pain-free subjects (SMD [95%CI]: 0.01 [−0.21, 0.24], Z=0.11, p=0.91). Moderate to substantial heterogeneity was found for the overall analysis (I2=47%, p=0.03) and patient subgroup analysis (I2=75%, p=0.003). Conclusions The current systematic review and meta-analysis conclude that centrally-acting drugs and exercise do not influence OA. Evidence on the peripheral contribution to OA response requires further investigations. Preclinical models of OA should be established to identify the neurophysiology and -biology behind OA.
Normas ocupacionais do benzeno: uma abordagem sobre o risco e exposição nos postos de revenda de combustíveis

Introduction: workers are subject to risks from environmental and occupational exposure to benzene. This situation is even more critical when combined with social inequality, multiple exposure, individual susceptibility, environmental degradation, and possible interactions between environmental contaminants. In addition, regulations are not always established and/or updated only from scientific evidences, because they are processes permeated by conflicts of interest. Objective: to produce a critical analysis on risk identification and benzene exposure evaluation. Methods: literature and documentary review of the Brazilian and international regulatory standards on benzene exposure, toxicological aspects, occupational and environmental exposure, and risk evaluation. Discussion: benzene exposure control permeates the technical-political dispute of health and safety, resulting in a regulatory standard from consensus of understandings. These understandings are shaped by the dispute between health protection and business management of working conditions, to the detriment of scientific evidence. In this context, the Occupational Exposure Limits can vary significantly between countries and agencies, despite the universal recognition that there are no safe limits for benzene exposure, because it is a carcinogenic compound.
Toxic Leadership in Emergency Nurses: Assessing Abusive Supervision and Its Team-Level Impacts on Conflict Management and Organizational Commitment

Background. Emergency departments suffer from authoritarian and manipulative leadership styles that affect team dynamics, emotional exhaustion, and quality patient care. However, little research specifically explores these toxic leadership effects on conflict management and nurses' organizational commitment. Objectives. This cross-sectional study aimed to assess the correlations between perceived toxic leadership, conflict resolution strategies, and commitment dimensions among emergency nurses while evaluating conflict tendencies as a mediating mechanism. Methods. A cross-sectional design that included multiple regression and mediation analyses was utilized. The sample consisted of 387 emergency nurses from five major Saudi hospitals surveyed using validated scales that measure perceived toxic leadership, conflict styles, and organizational commitment. Results. High prevalence rates for perceived authoritarian (77%), narcissistic (75%), and unpredictable (63%) leadership were reported. Increased toxicity was positively related to dominating and avoiding conflict styles but negatively related to integrating and compromising strategies. Toxic leadership is also associated with lower affective/normative commitment but higher continuance commitment. Conflict management partially mediated the leadership-commitment relationship, which explained 29% of the total effect. Finally, higher experience and education predicted greater perceived toxicity. Conclusions. The significant correlations between destructive leadership, adverse conflict, and reduced commitment in emergency nurses underscore the need for context-specific leadership training. Fostering supportive environments through multifaceted interventions can counteract toxicity impacts, impart constructive communication techniques, improve nurse well-being, and ensure high-quality patient care. As conflict tendencies and nurse characteristics influence susceptibility to detrimental leadership, tailored programs addressing experience levels are vital.
Mobile Robot Obstacle Avoidance Based on Deep Reinforcement Learning

Obstacle avoidance is one of the core problems in the field of mobile robot autonomous navigation. This paper aims to solve the obstacle avoidance problem using Deep Reinforcement Learning. In previous work, various mathematical models have been developed to plan collision-free paths for such robots. In contrast, our method enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. The derived mathematical model is capable of choosing an action directly according to the input sensor data for the mobile robot. In this paper, we develop an obstacle avoidance framework based on deep reinforcement learning. A 3D simulator is designed as well to provide the training and testing environments. In addition, we developed and compared obstacle avoidance methods based on different Deep Reinforcement Learning strategies, such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN) and DDQN with Prioritized Experience Replay (DDQN-PER) using our simulator.
Neuroinformatics 2010 3rd INCF Congress of Neuroinformatics

Spatial correlations in spike-train ensembles can seriously impair the en-and de-coding of information in the population rate [1] or in the fine spatio-temporal structure of these spike trains [2]. Recent theoretical and experimental studies showed that spike correlations in neural networks can be considerably smaller than expected based on the amount of shared presynaptic input in such systems [3,4,5]. Here, we demonstrate by means of a simple linear model and simulations of networks of integrate-and-fire neurons that pairwise correlations and hence population-rate fluctuations in recurrent networks are actively suppressed by inhibitory feedback. To investigate the role of feedback, we calculate the power-and cross-spectra of the network response for the intact recurrent system and for the case where the 2nd-order statistics of the feedback signals is perturbed while the shared-input structure and the 1st-order statistics are preserved. In general, any modification of the feedback statistics causes a shift in the power and coherence of the population response. In particular, the neglect of correlations within the ensemble of feedback channels or between the external stimulus and the feedback can amplify population-rate fluctuations by orders of magnitude. This effect can be observed both in networks with purely inhibitory and in those with mixed excitatory-inhibitory coupling. In purely inhibitory networks, shared-input correlations are canceled by negative correlations between the feedback signals. In excitatory-inhibitory networks, the responses are typically positively correlated. Here, the suppression of input correlations is not a result of the mere existence of correlations between the responses of excitatory (E) and inhibitory (I) neurons [4], but is instead a consequence of the heterogeneity of response correlations across different types of neuron pairs (EE, EI, II). If correlations between EE, II and EI pairs were identical, input correlations could not fall below the level
Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class

The combinatorial pure exploration (CPE) in the stochastic multi-armed bandit setting (MAB) is a well-studied online decision-making problem: A player wants to find the optimal action π ∗ from action class A , which is a collection of subsets of arms with certain combinatorial structures. Though CPE can represent many combinatorial structures such as paths, matching, and spanning trees, most existing works focus only on binary action class A ⊆ { 0 , 1 } d for some positive integer d . This binary formulation excludes important problems such as the optimal transport, knapsack, and production planning problems. To overcome this limitation, we extend the binary formulation to real, A ⊆ R d , and propose a new algorithm. The only assumption we make is that the number of actions in A is polynomial in d . We show an upper bound of the sample complexity for our algorithm and the action class-dependent lower bound for R-CPE-MAB, by introducing a quantity that characterizes the problem’s difficulty, which is a generalization of the notion width introduced in Chen et al. [2014].
Energy efﬁcient resource allocation for quantity of information delivery in parallel channels

This paper deals with the problem of minimising the energy required to transmit a certain amount of information bits. If there is no constraint, in a Gaussian channel, the solution leads us to the use an inﬁnite transmission time or an inﬁnite bandwidth. In particular, the minimum required signal-to-noise ratio to transmit one bit is equal to (cid:2) 1.59 dB. To allow the development of new green communication services, a ﬁnite bandwidth and a ﬁnite time have to be used in practice. Thus, we focus on the transmission of a given number of bits over a set of parallel Gaussian channels when there is an energy constraint, and we have the goal of minimising either the highest transmission time (amongst the channels) or the average channel occupancy time. This is a resource allocation problem that is formulated by targeting a certain energy consumption factor, herein deﬁned as the ratio between the energy required in the asymptotic regime and the energy required to transmit a certain quantity of information with limited (time, bandwidth) resources. The Pareto front for the joint optimization of the maximum transmission time and the average channel occupancy time is also derived. Several explicative numerical results of the derived resource allocation algorithms for energy efﬁcient communications are also reported. Copyright © 2014
This paper is included in the Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation

Large-scale cluster storage systems use redundancy (via erasure coding) to ensure data durability. Disk-adaptive redundancy—dynamically tailoring the redundancy scheme to observed disk failure rates—promises signiﬁcant space and cost savings. Existing disk-adaptive redundancy systems, however, pose undesirable constraints on data placement, partitioning disks into subclusters that have homogeneous failure rates and forcing each erasure-coded stripe to be entirely placed on the disks within one subcluster. This design increases risk, by reducing intra-stripe diversity and being more susceptible to unanticipated changes in a make/model’s failure rate, and only works for very large storage clusters fully committed to disk-adaptive redundancy. Tiger is a new disk-adaptive redundancy system that efﬁ-ciently avoids adoption-blocking placement constraints, while also providing higher space-savings and lower risk relative to prior designs. To do so, Tiger introduces the eclectic stripe , in which redundancy is tailored to the potentially-diverse failure rates of whichever disks are selected for storing that particular stripe. With eclectic stripes, pre-existing placement policies can be used while still enjoying the space-savings and robustness beneﬁts of disk-adaptive redundancy. This paper introduces eclectic striping and Tiger’s design, including a new mean-time-to-data-loss (MTTDL) approximation technique and new approaches for ensuring safe per-stripe settings given that failure rates of different devices change over time. In addition to avoiding placement constraints, evaluation with logs from real-world clusters shows that Tiger provides better space-savings, less bursty IO for changing redundancy schemes, and better robustness (due to increased risk-diversity) than prior disk-adaptive redundancy designs.
The Effect of Linguistic Parameters in CLIR Performance

This paper will detail how IARPA’s MATERIAL Cross-Language Information Retrieval (CLIR) program investigated certain linguistic parameters to guide language choice, data collection and partitioning, and understand evaluation results. Discerning which linguistic parameters correlated with overall performance enabled the evaluation of progress when different languages were measured, and also was an important factor in determining the most effective CLIR pipeline design, customized to handle language-specific properties deemed necessary to address.
Reflections on “ The Context-Tree Weighting Method : Basic Properties ”

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Artificial Experts: Social Knowledge and Intelligent Machines

A machine is what we make of it. It can mimic us if we can mimic it, or help it out, or overlook its mistakes. In "Artificial Experts sociologist Harry Collins explains what computers can't do, but also studies the ordinary and extraordinary things that they can do. He argues that although machines are limited because we cannot reproduce in symbols what every community knows, we give them abilities because of the way we embed them in our society. He unfolds a compelling account of the difference between human action and machine intelligence, the core of which is a witty and learned exploration of knowledge itself, of what communities know and the ways in which they know it. In the course of his investigations, Collins derives enlightening metaphors for the relation between artificial intelligence and prosthetic technologies such as artificial hearts. He provides an intriguing explanation of why pocket calculators work and shares his own experience in constructing an expert system designed to teach people to grow specialized semiconductor crystals. He describes a novel development of the Turing protocol for the definition Of intelligence, a new classification of human skill, and an original way of understanding our relationship to machines. From an AI point of view, the acquisition of knowledge and the selection of applications are critical to the success of expert systems. Collins offers an original approach to both problems for AI researchers and practitioners, providing a sociological perspective on the kinds of knowledge expert systems can and cannot capture, and on the domains that are and are not likely to be successful. Harry M. Collins is a widely published sociologist. He isProfessor of Sociology, Head of the School of Social Sciences, and Director of the Science Studies Centre at the University of Bath.