Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Realistic and Representative Trajectories with Mobility Behavior Clustering

Accessing realistic human movements (aka trajectories) is essential for many application domains, such as urban planning, transportation, and public health. However, due to privacy and commercial concerns, real-world trajectories are not readily available, giving rise to an important research area of generating synthetic but realistic trajectories. Inspired by the success of deep neural networks (DNN), data-driven methods learn the underlying human decision-making mechanisms and generate synthetic trajectories by directly fitting real-world data. However, these DNN-based approaches do not exploit people's moving behaviors (e.g., work commute, shopping purpose), significantly influencing human decisions during the generation process. This paper proposes MBP-GAIL, a novel framework based on generative adversarial imitation learning that synthesizes realistic trajectories that preserve moving behavior patterns in real data. MBP-GAIL models temporal dependencies by Recurrent Neural Networks (RNN) and combines the stochastic constraints from moving behavior patterns and spatial constraints in the learning process. Through comprehensive experiments, we demonstrate that MBP-GAIL outperforms state-of-the-art methods and can better support decision making in trajectory simulations.
Geo-knowledge-informed Deep Learning for Auto-identification of Supraglacial Lakes on the Greenland Ice Sheet from Satellite Imagery

Melting glaciers are indicators of global climate change. The formation and dynamics of supraglacial lakes on the Greenland's ice sheet, which appear during the summer melt season, are of particular interest due to their impact on ice dynamics. Detecting these lakes is essential, yet challenging. This paper presents a comprehensive geo-knowledge-informed deep leaning workflow for auto-identification of supraglacial lakes from Sentinel-2 satellite images. The U-Net deep neural network is employed for pixel-level segmentation, distinguishing lakes from other features. Post-processing techniques filter false positives and incorporate geographical knowledge to re-fine results. The experiment results demonstrate the superior performance of our proposed method with F1-score of 0.78. This research contributes to the understanding of supraglacial lake dynamics and the development of robust automated detection methods.
SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction

Mining spatio-temporal correlation patterns for traffic prediction is a well-studied field. However, most approaches are based on the assumption of the availability of and accessibility to a sufficiently dense data source, which is rather the rare case in reality. Traffic sensors in road networks are generally highly sparse in their distribution: fleet-based traffic sensing is sparse in space but also sparse in time. There are also other traffic application, besides road traffic, like moving objects in the marine space, where observations are sparsely and arbitrarily distributed in space. In this paper, we tackle the problem of traffic prediction on sparse and spatially irregular and non-deterministic traffic observations. We draw a border between imputations and this work as we consider high sparsity rates and no fixed sensor locations. We advance correlation mining methods with a Sparse Unstructured Spatio Temporal Reconstruction (SUSTeR) framework that reconstructs traffic states from sparse non-stationary observations. For the prediction the framework creates a hidden context traffic state which is enriched in a residual fashion with each observation. Such an assimilated hidden traffic state can be used by existing traffic prediction methods to predict future traffic states. We query these states with query locations from the spatial domain.
Modeling Multi-Grained User Preference in Location Visitation

Location prediction acts as a fundamental service in today's location-based information platform, which helps users access locations satisfying their demands, improving both user experience and platform profit. Since users with unambiguous demands prefer specific locations while users with compound demands consider first regions and then specific locations, it is necessary to model multi-grained user preferences at different geographical scales. However, most of the existing works concentrate on user preferences at the location-scale only, which can not understand users traveling behaviors thoroughly. In this paper, we propose to model both the fine-grained user preferences at the location scale and the coarsegrained user preferences at the region scale. Specifically, the proposed model harnesses the efficient information extraction power of graph neural networks. Moreover, the proposed geographical calibration method also helps to capture multi-grained user preferences accurately. Experiments on datasets of two very large cities demonstrate the significant performance improvement using our approach over state-of-the-art models. We also conduct experiments to further demonstrate the effectiveness of each component in the proposed model. Source codes of this paper are available at https://github.com/tsinghua-fib-lab/SIGSPATIAL-MMGUP/.
CICMoD - A Climate Index Collection Benchmark (Data and Resources Paper)

In the domain of climate science, machine learning (ML) and in particular deep learning (DL) methods are known to be effective for identifying causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills of forecast systems. To attribute climate events in a data-driven way, we need sufficient training data, which is often limited for real world measurements. The data science community provides standard data sets for many applications. As a new data set, we introduce a consistent and comprehensive collection of climate indices typically used to describe Earth System dynamics. Therefore, we use 1000-year control simulations from Earth System Models. The data set is provided as an open-source framework that can be extended and customized to individual needs. It allows users to develop new ML methodologies and to compare results to existing methods and models as benchmark.
A Scalable Inclusive Security Intervention to Center Marginalized & Vulnerable Populations in Security & Privacy Design

Research in computer security has increasingly considered the needs of marginalized and vulnerable groups in technology. Through this work, we hope to translate this research movement into practice and, ultimately, cause designers-in-training (and, eventually, designers) to consider a more inclusive range of stakeholders. Thus, we created an educational intervention to center marginalized and vulnerable populations in the context of threat modeling. We find that computer security students are more likely to consider unique threats and vulnerabilities facing marginalized and vulnerable populations after being exposed to an intervention prompting them to think about populations that might often be overlooked. We suggest practical methods to teach designers-in-training inclusive methods in computer security and discuss other possible adoptions of this practice across the field. This work is part of an important shift toward inclusive security that centers marginalized and vulnerable populations both in research and in practice.
Fewer Burdens but Greater Inequality? Reevaluating the Safety Net through the Lens of Administrative Burden

We examine changes in administrative burden in U.S. social safety net programs, or the negative encounters with the state that people experience when trying to access and use the benefits for which they are eligible. Existing theories equate targeted safety net policies, which sharply limit eligibility, as compared to universal policies, which have more expansive eligibility, with increased administrative burden. The past 30 years, however, tell a more complicated story. While overall burdens have declined in most targeted programs, there is evidence of increasing inequality regarding who faces these burdens. We trace the cause to three factors: (1) expansions in targeted programs, like Medicaid, gave states more administrative control, which increased both geographic and racial disparities in administrative burden; (2) delivering benefits through the tax system created more burdens for low-income populations compared to high-income populations; and (3) a growing reliance on private providers to deliver benefits and services created higher burdens for low-income populations to navigate.
Ad Hoc Transactions through the Looking Glass: An Empirical Study of Application-Level Transactions in Web Applications

Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. We refer to database operations coordinated by application code as ad hoc transactions. Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among 8 popular open-source web applications, we found that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone—53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential for improving performance in contentious workloads by utilizing application semantics such as access patterns. Based on these findings, we discuss the implications of ad hoc transactions to the database research community.
Energy Transfer Between Various Electron Populations Via Resonant Interaction With Whistler Mode Wave

Electron energization in the Earth's radiation belts caused by resonant interaction with whistler mode waves is currently under intense investigation. When the waves are excited due to cyclotron instability in collisionless plasma, that is, when the energy source for the waves is the free energy of unstable electron distribution, particle energization by excited waves is nothing but energy transfer from one group of electrons to another mediated by the waves. An example of such a process is considered in which a quasi‐monochromatic whistler mode wave packet with the frequency and the wave normal angle corresponding to the maximum growth rate is excited at the equator. Since the maximum growth rate corresponds to parallel propagating wave, only the first cyclotron resonance particles play a part in this excitation. While propagating from the equator toward the Earth, the wave normal vector becomes more and more oblique, and all cyclotron resonances, in particular, Landau resonance come into play. Wave‐particle interaction at Landau resonance leads to the wave damping and the corresponding particle energization on the average. Moreover, we show that the mean square variation of resonant particle energy greatly exceeds the average value, thus, the energy increase of some particles is much larger than the Landau resonance particles get from the wave on the average. This means that the exchange of energy between different groups of particles through a wave is a more efficient process than the amplification or damping of a wave due to its resonant interaction with particles.
A descriptive analysis of Xitsonga contemporary music

Indigenous music and dance can help one to vividly fathom the behaviour, values and morals of particular cultural groups, who perform its music and dance for various reasons. The indigenous music and dance of the Vatsonga also play a vital role in the lives of not only the Vatsonga, but also others who enjoy indigenous Xitsonga music and dance. However, there exist gaps regarding documentation, preservation and dance cultural practice. In this study the change in Xitsonga language usage in Xitsonga music, indigenous Xitsonga music and the change in Xitsonga dance genres are analysed descriptively and documented for posterity. The study was guided by a pragmatist paradigm, a case study design and mixed-method research design to analyse Xitsonga music. Random sampling was used to select 15 Xitsonga speakers. Purposive sampling was used to further select five Xitsonga speakers. Individual interviews were used to collect data from the five Xitsonga speakers and questionnaires were used to collect data from the other 15 Xitsonga speakers. The data collected were analysed qualitatively and quantitatively. The thematic method was used to analyse the findings of qualitative data, while quantitative data were analysed and interpreted. A study such as this one can also contribute to African indigenous knowledge systems.
Call to prayer: the sound of the adhan, heritage and shifting urban identity in Cape Town

Architectural analyses of heritage and the city largely focus on the visual and physical properties of buildings and sites. Traces of intangible, sensuous heritage such as sounds, smells and community-lived experience are often left out. This article critiques this tradition by tuning into the sound of the adhan as broadcast from mosques in Bo-Kaap, a neighbourhood of Cape Town, South Africa, as a heritage indicator. The sound echoes a history of Islam as a suppressed minority faith in the colonial past and the Muslim struggle for belonging. Yet this notion has become contested with noise complaints made about the adhan in contemporary Cape Town. This has led to heated arguments about gentrification, heritage and religious freedom. We unpack what is at stake in this debate and, using a range of data, evaluate public perception of the heritage significance of the adhan and its social resonance as heritage in Cape Town. We develop an expanded concept of the haptic, as being affected, to interpret community acoustic disputes. We argue that a multisensory approach, emphasising sound, touch and feeling, enables a nuanced reading of the negotiation of religion, heritage, identity and belonging in Cape Town.
A ridesharing simulation platform that considers dynamic supply-demand interactions

This paper presents a new ridesharing simulation platform that accounts for dynamic driver supply and passenger demand, and complex interactions between drivers and passengers. The proposed simulation platform explicitly considers driver and passenger acceptance/rejection on the matching options, and cancellation before/after being matched. New simulation events, procedures and modules have been developed to handle these realistic interactions. The capabilities of the simulation platform are illustrated using numerical experiments. The experiments confirm the importance of considering supply and demand interactions and provide new insights to ridesharing operations. Results show that increase of driver supply does not always increase matching option accept rate, and larger matching window could have negative impacts on overall ridesharing success rate. These results emphasize the importance of a careful planning of a ridesharing system.
Metaproteogenomics resolution of a high-CO2 aquifer community suggests an active symbiotic lifestyle of groundwater Gracilibacteria

Background Bacteria of the Candidate Phyla Radiation (CPR), constituting about 25% of the bacterial biodiversity, are characterized by small cell size and patchy genomes without complete key metabolic pathways, suggesting a symbiotic lifestyle. Gracilibacteria (BD1-5), which are part of the CPR branch, possess alternate coded genomes and have not yet been cultivated. However, besides genomic evidence, little is known about the lifestyle of Gracilibacteria, their temporal dynamics, and activity in natural ecosystems, particularly in groundwater, where they were initially been genomically resolved. Therefore, we here aimed to investigate Gracilibacteria activity in situ and to discern expressed genes involved in their lifestyle, using the metaproteogenome of Gracilibacteria as a function of time in the cold-water geyser Wallender Born in the Volcanic Eifel region in Germany. Results We coupled genome-resolved metagenomics and metaproteomics to investigate a cold-water geyser microbial community enriched in Gracilibacteria across a 12-day time-series. Groundwater was collected and sequentially filtered to fraction CPR and other bacteria. Based on 670 Gbps of metagenomic data, 1129 different ribosomal protein S3 marker genes and 751 high-quality genomes (123 population genomes after dereplication), we identified dominant bacteria belonging to Galionellales and Gracilibacteria along with keystone microbes, which were low in genomic abundance but substantially contributing to proteomic abundance. Seven high-quality Gracilibacteria genomes showed typical limitations, such as limited amino acid or nucleotide synthesis, in their central metabolism but no co-occurrence with potential hosts. The genomes of these Gracilibacteria encoded for a high number of proteins related to a symbiotic or even predatory lifestyle, e.g., type IV and type II secretion system subunits and features related to cell-cell interactions and cell motility, which were also detected on protein level. Conclusions Coupling metagenomics to metaproteomics enabled us to identify microbial keystone taxa in a high-CO2 aquifer, and to reveal microbial dynamics of Gracilibacteria. We posit that Gracilibacteria might be successful microbial predators in this ecosystem, potentially aiding in population control of this highly perturbed microbial geyser community from the deep biosphere.
Understanding interactions in a complex world: Design and analysis of multi-species functional response experiments

The functional response describes resource-dependent feeding rates of consumers. While models for feeding on a single resource species are well studied and supported by a large body of empirical research, consumers feeding on multiple resource species are ubiquitous in nature. However, feeding experiments designed for parameterizing multi-species functional responses (MSFR) are extremely rare, mainly due to logistical challenges and the non-trivial nature of their statistical analysis. Here, we describe how these models can be fitted to empirical data in a Bayesian framework. Specifically, we address the problem of prey depletion during experiments, which can be accounted for through dynamical modeling. In a comprehensive simulation study, we test the effects of experimental design, sample size and noise level on the identifiability of four distinct MSFR models. Additionally, we demonstrate the method’s versatility by applying it to a list of empirical datasets. We identify experimental designs for feeding trials that produce the most accurate parameter estimates in two- and three-prey scenarios. Although noise can introduce systematic bias in parameter estimates, model selection performs surprisingly well, almost always identifying the correct model even for small datasets. Our flexible method allows the simultaneous analysis of feeding experiments from both single- and multi-prey scenarios, either with or without prey depletion. This will help to improve our understanding of prey selectivity, prey switching and hence food web stability and biodiversity. Our approach equips researchers with the right statistical tools to improve the mechanistic understanding of feeding interactions in complex ecosystems.
Zelda is dispensable for Drosophila melanogaster histone gene regulation

To ensure that the embryo can package exponentially increasing amounts of DNA, replication-dependent histones are some of the earliest transcribed genes from the zygotic genome. However, how the histone genes are identified is not known. The pioneer factors Zelda and CLAMP collaborate at a subset of genes to regulate zygotic genome activation in Drosophila melanogaster and target early activated genes to induce transcription. CLAMP also regulates the embryonic histone genes and helps establish the histone locus body, a suite of factors that controls histone mRNA biosynthesis. The relationship between Zelda and CLAMP led us to hypothesize that Zelda helps identify histone genes for early embryonic expression. We found that Zelda targets the histone locus early during embryogenesis, prior to histone gene expression. However, depletion of zelda in the early embryo does not affect histone mRNA levels or histone locus body formation. While surprising, these results concur with other investigations into Zelda’s role in the early embryo, suggesting the earliest factors responsible for specifying the zygotic histone genes remain undiscovered.
Propagation of a rapid cell-to-cell H2O2 signal over long distances in a monolayer of cardiomyocyte cells

Cell-to-cell communication plays a cardinal role in the biology of multicellular organisms. H2O2 is an important cell-to-cell signaling molecule involved in the response of mammalian cells to wounding and other stimuli. We previously identified a signaling pathway that transmits wound-induced cell-to-cell H2O2 signals within minutes over long distances, measured in centimeters, in a monolayer of cardiomyocytes. Here we report that this long-distance H2O2 signaling pathway is accompanied by enhanced accumulation of cytosolic H2O2 and altered redox state in cells along its path. We further show that it requires the production of superoxide, as well as the function of gap junctions, and that it is accompanied by changes in the abundance of hundreds of proteins in cells along its path. Our findings highlight the existence of a unique and rapid long-distance H2O2 signaling pathway that could play an important role in different inflammatory responses, wound responses/healing, cardiovascular disease, and/or other conditions. Highlights Wounding induces an H2O2 cell-to-cell signal in a monolayer of cardiomyocytes. The cell-to-cell signal requires H2O2 and O2·- accumulation along its path. The signal propagates over several centimeters changing the redox state of cells. Changes in the abundance of hundreds of proteins accompanies the signal. The cell-to-cell signal requires paracrine and juxtacrine signaling. Graphical Abstract
Sparse Representation for High-dimensional Multiclass Microarray Data Classification

Sparse representation of signals has achieved satisfactory results in classification applications compared to the conventional methods. Microarray data, which are obtained from monitoring the expression levels of thousands of genes simultaneously, have very high dimensions in relation to the small number of samples. This has led to the weaknesses of state-of-the-art classifiers to cope with the microarray data classification problem. The ability of the sparse representation to represent the signals as a linear combination of a small number of training data and to provide a brief description of signals led to reducing computational complexity as well as increasing classification accuracy in many applications. Using all training samples in the dictionary imposes a high computational burden on the sparse coding stage of high dimensional data. Proposed solutions to solve this problem can be roughly divided into two categories: selection of a subset of training data using different criteria, or learning a concise dictionary. Another important factor in increasing the speed and accuracy of a sparse representation-based classifier is the algorithm which is used to solve the related ℓ1 –norm minimization problem. In this paper, different sparse representation-based classification methods are investigated in order to tackle the problem of 14-Tumors microarray data classification. Our experimental results show that good performances are obtained by selecting a subset of the original atoms and learning the associated dictionary. Also, using SL0 sparse coding algorithm increases speed, and in most cases, accuracy of the classifiers.