Miracle-dz/UniFAR-FTUs huggingface dataset:
Space-Time Tradeoffs for Answering Boolean Conjunctive Queries

In this paper, we investigate space-time tradeoﬀs for answering boolean conjunctive queries. The goal is to create a data structure in an initial preprocessing phase and use it for answering (multiple) queries. Previous work has developed data structures that trade oﬀ space usage for answering time and has proved conditional space lower bounds for queries of practical interest such as the path and triangle query. However, most of these results cater to only those queries, lack a comprehensive framework, and are not generalizable. The isolated treatment of these queries also fails to utilize the connections with extensive research on related problems within the database community. The key insight in this work is to exploit the formalism of relational algebra by casting the problems as answering join queries over a relational database. Using the notion of boolean adorned queries and access patterns , we propose a uniﬁed framework that captures several widely studied algorithmic problems. Our main contribution is three-fold. First, we present an algorithm that recovers existing space-time tradeoﬀs for several problems. The algorithm is based on an application of the join size bound to capture the space usage of our data structure. We combine our data structure with query decomposition techniques to further improve the tradeoﬀs and show that it is readily extensible to queries with negation. Second, we falsify two conjectures proposed in the existing literature that relates to the space-time lower bound for path queries and triangle detection by proposing an unexpectedly better algorithm. This result opens a new avenue for improving several algorithmic results that have so far been assumed to be (conditionally) optimal. Finally, we prove new conditional space-time lower bounds for star and path queries.
Epileptic seizure detection using deep learning techniques: A Review

A variety of screening approaches have been proposed to diagnose epileptic seizures, using Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning. Before the rise of deep learning, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in deep learning, the extraction of features and classification is entirely automated. The advent of these techniques in many areas of medicine such as diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of the types of deep learning methods exploited to diagnose epileptic seizures from various modalities has been studied. Additionally, hardware implementation and cloud-based works are discussed as they are most suited for applied medicine.
Robust Digital-Twin Localization via An RGBD-based Transformer Network and A Comprehensive Evaluation on a Mobile Dataset

The potential of digital-twin technology, involving the creation of precise digital replicas of physical objects, to reshape AR experiences in 3D object tracking and localization scenarios is significant. However, enabling robust 3D object tracking in dynamic mobile AR environments remains a formidable challenge. These scenarios often require a more robust pose estimator capable of handling the inherent sensor-level measurement noise. In this paper, recognizing the challenges of comprehensive solutions in existing literature, we propose a transformer-based 6DoF pose estimator designed to achieve state-of-the-art accuracy under real-world noisy data. To systematically validate the new solution's performance against the prior art, we also introduce a novel RGBD dataset called Digital Twin Tracking Dataset v2 (DTTD2), which is focused on digital-twin object tracking scenarios. Expanded from an existing DTTD v1 (DTTD1), the new dataset adds digital-twin data captured using a cutting-edge mobile RGBD sensor suite on Apple iPhone 14 Pro, expanding the applicability of our approach to iPhone sensor data. Through extensive experimentation and in-depth analysis, we illustrate the effectiveness of our methods under significant depth data errors, surpassing the performance of existing baselines. Code and dataset are made publicly available at: https://github.com/augcog/DTTD2
Streamflow in Southeastern South America and the Southern Oscillation

Abstract The relationship between the Southern Oscillation (SO) and streamflow in two major rivers of southeastern South America (Negro and Uruguay rivers) is explored for the period 1909–1989. It is found that streamflow in both rivers has a clear tendency to be below average in the period from June through December in high SO index years (cold events in the equatorial Pacific Ocean) and a slight tendency to be above average in the period from November through the next February in ENSO years. These findings are in broad agreement with previously proposed associations between extremes in the Southern Oscillation and rainfall variability in southeastern South America.
Adaptation of Irrigation Infrastructure on Irrigation Demands under Future Drought in the United States

AbstractMore severe droughts in the United States will bring great challenges to irrigation water supply. Here, the authors assessed the potential adaptive effects of irrigation infrastructure under present and more extensive droughts. Based on data over 1985–2005, this study established a statistical model that suggests around 4.4% more irrigation was applied in response to a one-unit reduction in the Palmer drought severity index (PDSI), and approximately 5.0% of irrigation water application could be saved for each 10% decrease in the areas supplied by surface irrigation infrastructure. Based on the results, the model-projected irrigation infrastructure has played a greater role in changes in irrigation than drought in most areas under the current climate except some southwestern counties. However, under the predicted future more severe drought in 2080–99 under the representative concentration pathways 4.5 scenario, the model projected that the drought will require 0%–20% greater irrigation amounts assu.
Why Phubbing is Toxic for your Relationship: Understanding the Role of Smartphone Jealousy among "Generation y" Users

Coined as “phubbing”, excessive use of smartphones in the romantic context has been shown to represent a barrier to meaningful communication, causing conflict, lowering relationship satisfaction, and undermining individual well-being. While these findings project a dire picture of the future of romance, the mechanisms behind the detrimental influence of partner phubbing on relationship-relevant markers are still little understood. Considering prior evidence that partner phubbing leads to the loss of exclusive attention towards the other party, we argue that these are rather the feelings of jealousy partner phubbing is triggering that are responsible for the negative relational outcomes. Based on the analysis of qualitative and quantitative responses from “generation Y” users, we find that partner phubbing is associated with heightened feelings of jealousy, which is inversely related to couple’s relational cohesion. Moreover, jealousy plays a mediating role in the relationship between partner’s smartphone use and relational cohesion, acting as a mechanism behind this undesirable link. Challenging the frequently promoted euphoria with regard to permanent “connectedness”, our study contributes to a growing body of IS research that addresses dark sides of information technology use and provides corresponding implications for IS practitioners.
Holocene Palaeoenvironments at the Timberline in the Taillefer Massif, French Alps: A Study of Pollen, plant Macrofossils and Fossil Insects

In order to reconstruct the history of the tree-line fluctuations, an integrated palaeoecological study of pollen, plant macrofossils and insect fossils was made in two peat-bogs containing Pinus uncinata trunks in the Taillefer Massif (Isére, France) at an altitude of 2100 m (i.e., above the present timberline). There is apparently an absence of a local woodland during the Lateglacial. After a hiatus corresponding to the beginning of the Holocene, the local presence of P. uncinata is recorded from at least 7500 BP onwards. There are also indications of Abies woodland in the vicinity of the plateau during the Atlantic chronozone. Significant changes in regional ecosystems are recorded from 5000 BP in an expansion of Fagus and Pinus cembra, and the first pollen evidence for human activities. Plant macrofossils and fossil insects (mainly Coleoptera) significantly improved the interpretations based on pollen analysis. Pinus uncinata disappeared from the plateau at about 2000 BP, slightly before the expansion of Picea at lower altitude. Pollen data imply that the timberline recession may be due to the exploitation of the forest by man. However, a lowering of the snow-line coinciding with the decline of Pinus uncinata is attested by the appearance of the insect Helophorus glacialis. It is therefore probable that there was a combination of human action and climatic impact, the importance of the latter having often been underestimated up to now.
Arctic climate variability during the past millennium : combining model simulations and proxy data

The Arctic is one of the regions that experienced the fastest and highest warming of the planet during the recent decades, and changes in its environment had worldwide implications. Studying climate changes during the past is essential for a comprehensive understanding of the climate system, particularly to provide insights into the processes involved in the recent Arctic warming, in order to assess future global climate changes. Information about past climate evolution can be extracted from “proxy” data, while climate models can be used to interpret the observed changes. In this doctoral thesis, we have taken advantage of the complementarity of model results and proxy data, through data assimilation, to provide reliable simulations of the Arctic climate over the past millennium that are in agreement with all the possible sources of information: proxies, physics of the model and external forcings. Those simulations have then been used to analyse the role played by different forcing elements in the past Arctic climate changes. We have highlighted, for instance, the dominant contribution of volcanic eruptions in the simulated temperature trends and of the astronomical forcing in the differences in seasonal trends. The role of the atmospheric circulation in explaining some warm periods has been underlined in our simulations assimilating sparse temperature reconstructions based on proxy data. We have also demonstrated the need for a precise estimation of proxy data uncertainties to avoid inconsistencies between those data and the model physics. This study, being among the first to apply a data assimilation method in palaeoclimatology, provides a powerful tool for investigating past climate and some challenging perspectives.
iFoldRNA: three-dimensional RNA structure prediction and folding

UNLABELLED Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nt) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2-5 A root mean squre deviations (RMSDs) from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, RMSDs from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses. AVAILABILITY http://iFoldRNA.dokhlab. org.
The role and use of experts and expert knowledge in spatial conservation prioritization

Experts and expert knowledge are used in spatial prioritization processes, but the extent and the manner in which it is incorporated has not been explicitly researched. This study aimed to explore when, how and what experts had been used in papers, published in the peerreviewed literature, which carry out spatial prioritization between 1984 and 2010. A total of 394 papers were identified, of which 109 (27.7%) had included experts or expert knowledge in the spatial prioritization process. Ecological experts and species specific experts were predominantly used to map species localities and habitats and/or in selecting priority areas. In papers where the lead author was affiliated to a NGO or private research institution experts and expert knowledge were used significantly more than in papers where the lead author was affiliated to a university. Although experts are used in spatial prioritization, how they are selected and used, who they are and how many experts are used is not often explicitly stated. This makes the regulation and use of experts and expert knowledge challenging. In order to more effectively utilize and incorporate experts and expert knowledge in spatial prioritization processes further research needs to go into understanding experts, how they are selected to take part in spatial prioritization, the defensibility of the knowledge they provide and how best to elicit expert knowledge.
Peer Reviewed Title: Model Predictive Control Approach to Online Computation of Demand-Side Flexibility of Commercial Buildings HVAC Systems for Supply Following

Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We investigate how to take advantage of this flexibility. We first propose a means to define and quantify the flexibility of a commercial building. We then propose a contractual framework that could be used by the building operator and the utility to declare flexibility on the one side and reward structure on the other side. We then design a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward, given the contractual framework. Finally, we perform at-scale experiments to demonstrate the feasibility of the proposed algorithm.
Measurement and simulation of subsurface tracer migration to tile drains in low permeability, macroporous soil

Multiyear monitoring and simulation of a conservative tracer was used in this study to investigate preferential flow and macropore‐matrix interactions in low permeability, macroporous soil. 2,6‐Difluorobenzoic acid (DFBA) tracer was applied to a 20 × 20 m drip irrigated test plot situated over two tile drains. Tracer movement over the 2009 and 2010 field seasons was monitored using tile drain effluent, suction lysimeters, monitoring wells, and soil cores. Despite similar volumes of water application to the plot in each season, 10 times more water and 14 times more DFBA were captured by the drains in 2010 due to wetter regional hydrologic conditions. The importance of preferential flow along macropores was shown by rapid DFBA breakthrough to the tile (<47 h), and DFBA detections in sand units below the tile drains. Preferential flow resulted in less than 8% of the DFBA mass being captured by the tiles over both years. With much of the DFBA mass (75%) retained in the upper 0.25 m of the soil at the end of 2009, numerical simulations were used to quantify the migration of this in situ tracer during the subsequent 2010 field season. Dual permeability and dual porosity models produced similar matches to measured tile drain flows and concentrations, but solute leaching was captured more effectively by the dual permeability formulation. The simulations highlighted limitations in current descriptions for small‐scale mass transfer between matrix and macropore domains, which do not consider time‐dependent transfer coefficients or nonuniform distributions of solute mass within soil matrix blocks.
Correlation for the Lower Palaeozoic

Abstract The global correlation standards now agreed for the Ordovician and Silurian systems, and partly so for the Cambrian System, are summarized. Correlation of the international series and stages with the traditional and revised British series and stages is reviewed, as well as with those in North America (Laurentia).
Evaluating Group-Robot Interaction in Crowded Public Spaces: A Week-Long Exploratory Study in the Wild with a Humanoid Robot Guiding Visitors Through a Science Museum

This paper describes an exploratory study on group interaction with a robot guide in an open largescale busy environment. For an entire week, a humanoid robot was deployed in the popular Cosmocaixa Science Museum in Barcelona and guided hundreds of people through the museum facilities. The main goal of this experience is to study in the wild the episodes of the robot guiding visitors to a requested destination focusing on the group behavior during displacement. The walking behaviorfollow-me and the face-to-face communication in a populated environment are analyzed in terms of guide–visitors interaction, grouping patterns and spatial formations. Results from observational data show that the space con¯gurations spontaneously formed by the robot guide and visitors walking together did not always meet the robot communicative and navigational requirements for successful guidance. Therefore, additional verbal and nonverbal rompts must be considered to regulate e®ectively the walking ogether and follow-me behaviors. Finally, we discuss lessons learned and recommendations for robot's spatial behavior in dense crowded scenarios.
HDD: a hypercube division-based algorithm for discretisation

Discretisation, as one of the basic data preparation techniques, has played an important role in data mining. This article introduces a new hypercube division-based (HDD) algorithm for supervised discretisation. The algorithm considers the distribution of both class and continuous attributes and the underlying correlation structure in the data set. It tries to find a minimal set of cut points, which divides the continuous attribute space into a finite number of hypercubes, and the objects within each hypercube belong to the same decision class. Finally, tests are performed on seven mix-mode data sets, and the C5.0 algorithm is used to generate classification rules from the discretised data. Compared with the other three well-known discretisation algorithms, the HDD algorithm can generate a better discretisation scheme, which improves the accuracy of classification and reduces the number of classification rules.
Evaluation of the effects of expected climate change on the durability of building materials with suggestions for adaptation

Regardless causes of climate change, changing climate parameters, such as higher temperature, amount and intensity of precipitation, different wind regime, will affect the durability of materials used in the building envelope, either individually or combined. The current paper evaluates possible trends and tendencies arising from these changing climate parameters on the durability of building materials in the Netherlands, based upon four scenario’s of climate change developed by the Royal Netherlands Meteorological Institute, KNMI (2006).
Is Simple Uniform Sampling Efficient for Center-Based Clustering With Outliers: When and Why?

Clustering has many important applications in computer science, but real-world datasets often contain outliers. The presence of out-liers can make the clustering problems to be much more challenging. In this paper, we propose a framework for solving three representative center-based clustering withoutliersproblems: 𝑘 -center/median/means clustering with outliers. The framework actually is very simple, where we just need to take a small uniform sample from the input and run an existing approximation algorithm on the sample. However, ouranalysis is fundamentally diﬀerent from the previous (uniform and non-uniform) sampling based ideas. To explain the eﬀectiveness of uniform sampling in theory, we introduce a “sig-niﬁcance” criterion and prove that the performance of our framework depends on the signiﬁcance degree of the given instance. In particular, the sample size can be independent of the input data size 𝑛 and the dimensionality 𝑑 , if we assume the given instance is suﬃciently “signiﬁcant”, which is in fact a fairly appropriate assumption in practice. Due to its simplicity, the uniform sampling approach also enjoys several signiﬁcant advantages over the non-uniform sampling approaches. The experiments suggest that our framework can achieve comparable clustering results with existing methods, but is much easier to implement and can greatly reduce the running times. To the best of our knowledge, this is the ﬁrst work that systematically studies the eﬀectiveness of uniform sampling from both theoretical and experimental aspects.
VQA-GNN: Reasoning with Multimodal Semantic Graph for Visual Question Answering

Visual understanding requires seamless integration between recognition and reasoning: beyond image-level recognition ( e.g. , detecting objects), systems must perform concept-level reasoning ( e.g. , inferring the context of objects and intents of people). However, existing methods only model the image-level features, and do not ground them and reason with background concepts such as knowledge graphs (KGs). In this work, we propose a novel visual question answering method, VQA-GNN , which uniﬁes the image-level information and conceptual knowledge to perform joint reasoning of the scene. Speciﬁcally, given a question-image pair, we build a scene graph from the image, retrieve a relevant linguistic subgraph from ConceptNet and visual subgraph from Vi-sualGenome, and unify these three graphs and the question into one joint graph, multimodal semantic graph . Our VQA-GNN then learns to aggregate messages and reason across different modalities captured by the multimodal semantic graph. In the evaluation on the VCR task, our method outperforms the previous scene graph-based Trans-VL models by over 4% , and VQA-GNN-Large , our model that fuses a Trans-VL further improves the state of the art by 2% , attaining the top of the VCR leaderboard at the time of submission. This result suggests the efﬁcacy of our model in performing conceptual reasoning beyond image-level recognition for visual understanding. Finally, we demonstrate that our model is the ﬁrst work to provide interpretability across visual