Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Bayesian MARS: Tools for Emulating Stochastic Computer Models

The multivariate adaptive regression spline (MARS) approach of Friedman (1991) and its Bayesian counterpart (Francom et al. 2018) are effective approaches for the emulation of computer models. The traditional assumption of Gaussian errors limits the usefulness of MARS, and many popular alternatives, when dealing with stochastic computer models. We propose a generalized Bayesian MARS (GBMARS) framework which admits the broad class of generalized hyperbolic distributions as the induced likelihood function. This allows us to develop tools for the emulation of stochastic simulators which are parsimonious, scalable, interpretable and require minimal tuning, while providing powerful predictive and uncertainty quantification capabilities. GBMARS is capable of robust regression with t distributions, quantile regression with asymmetric Laplace distributions and a general form of"Normal-Wald"regression in which the shape of the error distribution and the structure of the mean function are learned simultaneously. We demonstrate the effectiveness of GBMARS on various stochastic computer models and we show that it compares favorably to several popular alternatives.
Clarify Confused Nodes Through Separated Learning

Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, challenging the homophily assumption of classical GNNs and hindering their performance. Most existing studies continue to design generic models with shared weights between heterophilous and homophilous nodes. Despite the incorporation of high-order message or multi-channel architectures, these efforts often fall short. A minority of studies attempt to train different node groups separately, but suffering from inappropriate separation metric and low efficiency. In this paper, we first propose a new metric, termed Neighborhood Confusion ( NC ), to facilitate a more reliable separation of nodes. We observe that node groups with different levels of NC values exhibit certain differences in intra-group accuracy and visualized embeddings. These pave a way for N eighborhood C onfusion-guided G raph C onvolutional N etwork ( NCGCN ), in which nodes are grouped by their NC values and accept intra-group weight sharing and message passing. Extensive experiments on both homophilous and heterophilous benchmarks demonstrate that NCGCN can effectively separate nodes and offers significant performance improvement compared to latest methods.
Softly, Deftly, Scrolls Unfurl Their Splendor: Rolling Flexible Surfaces for Wideband Wireless

With new frequency bands opening up, emerging wireless IoT devices are capitalizing on an increasingly divergent range of frequencies. However, existing coverage provisioning practice is often tied to specific standards and frequencies. There is little shareable wireless infrastructure for concurrent links on different frequencies, across networks and standards. This paper presents Scrolls, a frequency-tunable soft smart surface system to enhance wideband, multi-network coverage. Scrolls' hardware comprises many rows of rollable thin plastic film, each attached with flexible copper strips. When rolled to different lengths, the copper strips act as wire antennas reflecting signals on the corresponding frequencies. The surface control algorithm determines the unrolled strip lengths for link enhancement by probing the search space efficiently. We build a set of distributed, composable Scrolls prototypes and deploy them in an office. Extensive evaluation shows that Scrolls can adapt the antenna lengths effectively to provide link enhancement across diverse standards on sub-6 GHz bands. For concurrent links on 900 MHz (LoRa), 2.4 GHz (Wi-Fi), 3.7 GHz, and 5 GHz, Scrolls can provide received signal strength gains to all links simultaneously, by a median of 4 dB and up to 10 dB.
Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction.

OBJECTIVE Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels. Approach: The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization (QUBO) problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4×4 to 24×24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP). Main result: By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT outperformed that of MLEM and FBP. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM. Significance: This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.
Stochastic facilitation in the brain?

We describe the context for three unsolved problems of noise in the brain as well as provide some new results relevant to one of them. The problems are: are neural oscillations better described as noisy limit cycles or as noise-driven quasicycles, does noise facilitate synchronization and information transmission in the brain, and do noise-driven spatial patterns (quasipatterns) coexist with noise-driven quasicycles in the brain? We provide a few new results indicating that, in models at least, spatial quasipatterns of quasicycles can occur, and resemble patterns observed in other areas, such as predator-prey systems and chemical reactions.
Heat equations associated to harmonic oscillator with exponential nonlinearity

We investigate the Cauchy problem for a heat equation involving a fractional harmonic oscillator and an exponential nonlinearity. Our main contributions are as follows: -We establish the local well-posedness in Orlicz spaces. -By considering small initial data in suitable Lebesgue spaces, we derive the existence of global weak-mild solutions. -We provide precise decay estimates for large time, revealing that the decay rate depends on the behavior of the nonlinearity near the origin. -Furthermore, we demonstrate that when considering certain non-negative initial data within the appropriate Orlicz space, the existence of local non-negative classical solutions is no longer guaranteed. In summary, our work addresses the local and global behavior of solutions, decay estimates, and the impact of nonlinearity on the existence of classical solutions, offering some insights into the dynamics of the considered heat equation with a fractional harmonic oscillator and exponential nonlinearity.
Towards Robust Feature Learning with t-vFM Similarity for Continual Learning

Continual learning has been developed using standard supervised contrastive loss from the perspective of feature learning. Due to the data imbalance during the training, there are still challenges in learning better representations. In this work, we suggest using a different similarity metric instead of cosine similarity in supervised contrastive loss in order to learn more robust representations. We validate the our method on one of the image classification datasets Seq-CIFAR-10 and the results outperform recent continual learning baselines.
Sur la théorie des formes différentielles harmoniques

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Bimodal Distributions in the Space of a Non-Uniform Weight

Bimodal approximate solutions of the Boltzmann equation for the model of hard spheres, which describe the interaction between the ”accelerationpacking” flows, are constructed. Some sufficient conditions for infinitesimality of the mixed error ”with weight” between the left-hand and the right-hand sides of the equation are obtained.
Teaching Distributed Programming – Revisiting the Broker Pattern

The Broker pattern[5] had its golden era as an architectural pattern for distributed object-oriented programming in the late 1990’ies and early 2000’s and was supported by a large set of frameworks: CORBA, Java RMI,. Net remoting, gRPC, SOAP and others. However, the tooling tended to be heavy-weight and cumbersome, security issues were hard to handle, and the growth of the more lightweight REST architectural style [13, 23] caused Broker based architectures to become much less used. In this tool paper we will argue why the Broker pattern offers a number of advantages over REST in a teaching context, argue that Broker can achieve similar architectural qualities as REST, show case an open source Broker library, and report from successful teaching experiences.
Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube construction time. Then, at query time, it uses whatever information is available from the materialized projections and extrapolates missing information to approximate query results. Thus, Sudokube avoids costly projections at query time while also avoiding the astronomical compute and storage requirements needed for fully materialized high-dimensional data cubes. In this paper, we show the capabilities of the Sudokube system and how it approximates query results using different techniques and materialization strategies.
ARENA: Alternative Relational Query Plan Exploration for Database Education

A key learning goal of learners taking a database systems course is to understand how SQL queries are processed in an RDBMS in practice. To this end, comprehension of different alternative query plans (AQPs) that may be considered during the selection of the query execution plan (QEP) of a query is paramount. In this demonstration, we present a novel and generic system called ARENA that facilitates exploration of informative alternative query plans of a given SQL query to aid the comprehension of QEP selection. Under the hood, ARENA addresses a novel problem called informative plan selection problem (TIPS) which aims to discover alternative plans from the underlying plan space so that the plan informativeness is maximized. We demonstrate various innovative features of ARENA emphasizing the important role it can play in supplementing database education.
Dexer: Detecting and Explaining Biased Representation in Ranking

With the growing use of ranking algorithms in real-life decision-making purposes, fairness in ranking has been recognized as an important issue. Recent works have studied different fairness measures in ranking, and many of them consider the representation of different "protected groups", in the top-k ranked items, for any reasonable k. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. To this end, we present Dexer, a system for the detection of groups with biased representation in the top-k. Dexer utilizes the notion of Shapley values to provide the users with visual explanations for the cause of bias. We will demonstrate the usefulness of Dexer using real-life data.
Table Discovery in Data Lakes: State-of-the-art and Future Directions

Data discovery refers to a set of tasks that enable users and downstream applications to explore and gain insights from massive collections of data sources such as data lakes. In this tutorial, we will provide a comprehensive overview of the most recent table discovery techniques developed by the data management community. We will cover table understanding tasks such as domain discovery, table annotation, and table representation learning which help data lake systems capture semantics of tables. We will also cover techniques enabling various query-driven discovery and table exploration tasks, as well as how table discovery can support key data science applications such as machine learning and knowledge base construction. Finally, we will discuss future research directions on developing new table discovery paradigms by combining structured knowledge and dense table representations, as well as improving the efficiency of discovery using state-of-the-art indexing techniques, and more.
TeeBench: Seamless Benchmarking in Trusted Execution Environments

Trusted Execution Environments (TEEs) have enabled building secure systems that operate on untrusted machines. However, TEEs' architecture questions previous performance findings. The existing relational algorithms have been designed for traditional CPUs. Prior work has shown that these algorithms underperform in TEEs and, in most cases, can not be easily reused. Moreover, they frequently used benchmarks pertinent to CPUs and ignored TEE-specific metrics essential to understand the performance differences. Therefore, there is a need for a fair benchmarking approach for TEE algorithms. In this demonstration, we showcase TeeBench, a unified benchmarking framework for relational operators across TEEs. TeeBench focuses on TEE-specific hardware metrics. It enables a comprehensive performance analysis that helps researchers to evaluate their advances. It comes with an interactive web browser tool that allows the users to upload their implementation of a relational algorithm and seamlessly benchmark it across different TEEs. In addition, it introduces a novel TEE-Analyzer that hints the users about performance bottlenecks and suggests possible code improvements. Users receive instant feedback if changes to their algorithm improve the performance through an interactive, human-friendly web interface. We expect TeeBench to encourage the usage of TEEs and to advance the study of privacy-preserving systems.
NEXUS: On Explaining Confounding Bias

When analyzing large datasets, analysts are often interested in the explanations for unexpected results produced by their queries. In this work, we focus on aggregate SQL queries that expose correlations in the data. A major challenge that hinders the interpretation of such queries is confounding bias, which can lead to an unexpected association between variables. For example, a SQL query computes the average Covid-19 death rate in each country, may expose a puzzling correlation between the country and the death rate. In this work, we demonstrate NEXUS, a system that generates explanations in terms of a set of potential confounding variables that explain the unexpected correlation observed in a query. NEXUS mines candidate confounding variables from external sources since, in many real-life scenarios, the explanations are not solely contained in the input data. For instance, NEXUS might extract data about factors explaining the association between countries and the Covid-19 death rate, such as information about countries' economies and health outcomes. We will demonstrate the utility of NEXUS for investigating unexpected query results by interacting with the SIGMOD'23 participants, who will act as data analysts.
An economic evaluation of fine-needle cytology as the primary diagnostic tool in the diagnosis of lymphadenopathy

Abstract Fine-needle aspiration cytology (FNAC) is commonly used to obtain a pre-surgical pathological diagnosis in many organs, but its cost-effectiveness in lymphadenopathy has not been studied yet. We calculated the cost and diagnostic accuracy of a diagnostic algorithm that uses FNAC as a first-line procedure and compared it to a purely surgical approach in 545 consecutive lymphadenopathies. In 74% of the cases, FNAC alone can obtain a sufficiently detailed diagnosis, avoiding the surgical biopsy. In doing so, the average cost of diagnosis is cut to less than one-third, the patient avoids an invasive procedure and the diagnosis is reached earlier. In conclusion, the systematic use of lymph node-FNAC in the initial assessment of lymphadenopathy is clinically and economically advantageous as it avoids surgical biopsies in cases where cytology can suffice.
Morphological and Molecular Characterization of Talanema eshtiaghii sp. n. (Dorylaimida, Qudsianematidae) from Iran

Abstract A new species of the genus Talanema, recovered from the northwest of Iran, was described based on morphological, morphometric, and molecular data. Talanema eshtiaghii sp. n. was characterized by its 1.45–1.68 mm long body, lip region offset by constriction and 13–15 μm wide, odontostyle 15–18 μm long, double guiding ring, neck 312–362 μm long, pharyngeal expansion occupying 41–43% of the total neck length, uterus tripartite, and 111–189 μm long or 2.1–3.2 body diameters, vulva transverse (V = 55–58), tail similar in both sexes, conical with a dorsal concavity (30–44 μm, c = 33–56, c’ = 1.0–1.6), spicules 49–56 μm long, and 14–18 shortly spaced ventromedian supplements in front of the level of the anterior end of spicules, with distinct hiatus. It was compared to four closely similar species, with emphasis on the most relevant traits to distinguish them. Molecular phylogenetic studies using partial sequence of the 28S rDNA (D2–D3 segment) revealed that the new species forms a clade with other currently sequenced representatives of Talanema, tentatively supporting the monophyly of this genus.
Creating stories of educational change in and for geography: what can we learn from Bolivia and Peru?

ABSTRACT This article will explore the potential for embedding education outputs within contemporary geographical scholarship to provide a disciplinary resource for school teachers’ curriculum thinking and pathways to impact for academic geographers. In particular, the article will draw upon two projects to show the empirical depth that can be achieved by developing resources that give teachers and students insight into the particularities of places (in this case Bolivia and Peru) in relation to sustainable development agendas via a focus on the co-production of geographical knowledge. Through engaging with research pursued by geographers, this article sheds light on the relationship between environmental justice and sustainable development, which can play an important role in shaping geography teachers’ curricular decision-making. It also recasts expertise where Indigenous leaders and others with ﬁrst-hand experience of their local environment are at the forefront of complex decisions and conﬂicts to determine trajectories of sustainability.
Exoribonuclease RNase R protects Antarctic Pseudomonas syringae Lz4W from DNA damage and oxidative stress

ABSTRACT RNase R is a highly processive, 3′–5′ exoribonuclease involved in RNA degradation, maturation, and processing in bacteria. In Pseudomonas syringae Lz4W, RNase R interacts with RNase E to form the RNA degradosome complex and is essential for growth at low temperature. RNase R is also implicated in general stress response in many bacteria. We show here that the deletion mutant of rnr gene (encoding RNase R) of P. syringae is highly sensitive to various DNA damaging agents and oxidative stress. RNase R is a multidomain protein comprised of cold shock domain, RNB, and S1 domains. We investigated the role of each domain of RNase R and its exoribonuclease activity in nucleic acid damage and oxidative stress response. Our results revealed that the RNB domain alone without its exoribonuclease activity is sufficient to protect against DNA damage and oxidative stress. We also show that the association of RNase R with the degradosome complex is not required for this function. Our study has discovered for the first time a hitherto unknown role of RNase R in protecting P. syringae Lz4W against DNA damage and oxidative stress. IMPORTANCE Bacterial exoribonucleases play a crucial role in RNA maturation, degradation, quality control, and turnover. In this study, we have uncovered a previously unknown role of 3′–5′ exoribonuclease RNase R of Pseudomonas syringae Lz4W in DNA damage and oxidative stress response. Here, we show that neither the exoribonuclease function of RNase R nor its association with the RNA degradosome complex is essential for this function. Interestingly, in P. syringae Lz4W, hydrolytic RNase R exhibits physiological roles similar to phosphorolytic 3′–5′ exoribonuclease PNPase of E. coli. Our data suggest that during the course of evolution, mesophilic E. coli and psychrotrophic P. syringae have apparently swapped these exoribonucleases to adapt to their respective environmental growth conditions. Bacterial exoribonucleases play a crucial role in RNA maturation, degradation, quality control, and turnover. In this study, we have uncovered a previously unknown role of 3′–5′ exoribonuclease RNase R of Pseudomonas syringae Lz4W in DNA damage and oxidative stress response. Here, we show that neither the exoribonuclease function of RNase R nor its association with the RNA degradosome complex is essential for this function. Interestingly, in P. syringae Lz4W, hydrolytic RNase R exhibits physiological roles similar to phosphorolytic 3′–5′ exoribonuclease PNPase of E. coli. Our data suggest that during the course of evolution, mesophilic E. coli and psychrotrophic P. syringae have apparently swapped these exoribonucleases to adapt to their respective environmental growth conditions.
MFGE8 inhibits insulin signaling through PTP1B

The role of integrins in regulating insulin signaling is incompletely understood. We have previously shown that binding of the integrin ligand milk fat globule epidermal growth factor like 8 (MFGE8) to the αvβ5 integrin promotes termination of insulin receptor signaling in mice. Upon ligation of MFGE8, β5 complexes with the insulin receptor beta (IRβ) in skeletal muscle resulting in dephosphorylation of IRβ and reduction of insulin-stimulated glucose uptake. Here we investigate the mechanism by which the interaction between β5 and IRβ impacts IRβ phosphorylation status. We show that β5 blockade inhibits and MFGE8 promotes PTP1B binding to and dephosphorylation of IRβ resulting in reduced or increased insulin-stimulated myotube glucose uptake respectively. The β5-PTP1B complex is recruited by MFGE8 to IRβ leading to termination of canonical insulin signaling. β5 blockade enhances insulin-stimulated glucose uptake in wild type but not Ptp1b KO mice indicating that PTP1B functions downstream of MFGE8 in modulating insulin receptor signaling. Furthermore, in a human cohort, we report serum MFGE8 levels correlate with indices of insulin resistance. These data provide mechanistic insights into the role of MFGE8 and β5 in regulating insulin signaling.