Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Graph Powered Semi-Supervised Learning on Biomedical Tabular Data

In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data. In this work, we address this limitation by providing an approach for inferring latent graphs that capture the intrinsic data relationships. By leveraging graph-based representations, our approach facilitates the seamless propagation of information throughout the graph, enabling the effective incorporation of global and local knowledge. Through evaluations on biomedical tabular datasets, we compare the capabilities of our approach to other contemporary meth-ods. Our work demonstrates the significance of inter-instance relationship discovery as practical means for constructing robust latent graphs to enhance semi-supervised learning techniques. Our method achieves state-of-the-art results on three biomedical datasets.
STABLE: Identifying and Mitigating Instability in Embeddings of the Degenerate Core

Are the embeddings of a graph’s degenerate core stable? What happens to the embeddings of nodes in the degenerate core as we systematically remove periphery nodes (by repeatedly peeling off k -cores)? We discover three patterns w.r. t. instability in degenerate-core embeddings across a variety of popular graph embedding algorithms and datasets. We correlate instability with an increase in edge density, and then theoretically show that in the case of Erd¨os-R´enyi graphs embedded with Laplacian Eigenmaps, the best and worst possible embeddings become less distinguishable as density increases. Furthermore, we present the STABLE algo-rithm, which takes an existing graph embedding algorithm and makes it stable. We show the effectiveness of STABLE in terms of making the degenerate-core embedding stable and still producing state-of-the-art link prediction performance.
Secure Guaranteed Computation

We introduce secure guaranteed two-party computation, where parties commit in advance to compute a function over their private inputs, by providing some (validated) compensation, such that if a party fails to provide an appropriate input during protocol execution, then the peer receives the compensation. Enforcement of the guarantees requires a trusted enforcement authority (TEA); however, the protocol protects con dentiality even from the TEA. Our secure guaranteed computation protocol is optimistic, i.e., the TEA is involved only if a party fails to participate (correctly). Secure guaranteed computation has direct practical applications, such as sensitive trading of nancial products, and could also be used as a building block to motivate parties to complete protocols, e.g., ensuring unbiased coin tossing. The guarantee process can be either symmetric (both parties provide guarantees) or asymmetric (e.g., only server provides guarantee to client). Symmetric guarantees should also be fair, i.e., one party cannot obtain guarantee of the other party without supplying a guarantee as well. The protocol guaranteeing output, which we present uses in a modular manner a new protocol, that we construct, for optimistic fair secure computation, which may be of independent interest, as it is simpler and more e cient than previously known protocols.
Dysregulated Urinary Arginine Metabolism in Older Adults With Amnestic Mild Cognitive Impairment

Background: Urine samples, which capture an individual’s metabolic profile, are ideal for the exploration of non-invasive biomarkers to confirm the amnestic mild cognitive impairment (aMCI) status of patients vs. unimpaired ones. Objective: We aimed to detect differentially metabolized amino acids, which are important objectives in metabolomics, garnering particular attention in biomedical pathogenesis from the urine of aMCI patients, which may give clinicians the possibility to intervene with early treatments that curb Alzheimer’s disease (AD). Methods: The study included 208 subjects, 98 of whom were aMCI patients, and 110 who were control subjects without dementia. Urine samples were taken from each participant and supernatant was obtained for analysis. The concentrations of amino acids were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results: Urinary arginine levels in aMCI patients are obviously lower than in normal controls (q < 0.2 and p < 0.05). Meanwhile, aMCI patients had significant reduced urinary global arginine bioavailability ratio (GABR), and GABR in urine displayed a positive correlation with the score of CMMSE. Conclusion: Urinary dysregulated arginine metabolism that may serve as a helpful clinical diagnostic biomarker for aMCI in older adults.
Aerosol effects on cloud cover as evidenced by ground‐based and space‐based observations at five rural sites in the United States

Previous studies of the second aerosol indirect (lifetime) effect on cloud cover have estimated the strength of the effect without correcting for near‐cloud contamination and other confounding factors. Here we combine satellite‐based observations with a multiyear ground‐based data set across five rural locations in the United States to more accurately constrain the second indirect aerosol effect and quantify aerosol effects on radiative forcing. Results show that near‐cloud contamination accounts for approximately 40% of the satellite‐derived aerosol‐cloud relationship. When contamination is removed and the effect of meteorological covariation is minimized, a strong physical aerosol effect on cloud cover remains. Averaged over all stations and after correcting for contamination, the daytime solar and total (solar + IR) radiative forcing is −52 W/m2 and −19 W/m2, respectively, due to both direct and indirect aerosol effects for aerosol optical depths (τ) between 0 and 0.3. Averaged diurnally, the average total radiative forcing is +16 W/m2.
GIS-Based Runoff Estimation in Atbara Area, Sudan

Runoff is one of the most significant hydrological properties that are used in many national works, for optimum design of reservoirs and for flood forecasting. The main objective of this study was to estimate surface runoff in Atbara area, North Sudan, using GIS. The curve number (CN) method of the soil conservation service (SCS) was employed. GIS was used to generate the hydrologic soil map and the land used/land cover map which were then intersected in order to generate CN value map. Runoff was then evaluated for the whole area using rainfall data according to the SCS - CN method. The CN values ranged between 0 and 100.The results has shown that maximum runoff values are encountered in the river Nile and river Atbara channels and in the southeastern part of the study area associated with the soil group (D) and it’s hydrological properties. This paper clearly illustrates that the combination of GIS with Soil Conservation Service-Curve number technique can be applied to generate the curve number map and runoff map for hydrological investigations and water resources management.
Move Schedules: Fast persistence computations in sparse dynamic settings

The standard procedure for computing the persistent homology of a ﬁltered simplicial complex is the matrix reduction algorithm. Its output is a particular decomposition of the total boundary matrix, from which the persistence diagrams and generating cycles can be derived. Persistence diagrams are known to vary continuously with respect to their input; this motivates the algorithmic study of persistence computations for time-varying ﬁltered complexes. Computationally, simulating persistence dynamically can be reduced to maintaining a valid decomposition under adjacent transpositions in the ﬁltration order. In practice, the quadratic scaling in the number of transpositions often makes this maintenance procedure slower than simply computing the decomposition from scratch, eﬀectively limiting the application of dynamic persistence to relatively small data sets. In this work, we propose a coarser strategy for maintaining the decomposition over a discrete 1-parameter family of ﬁltrations. Our ﬁrst result is an analysis of a simple linear-time strategy for reducing the number of column operations needed to simulate persistence across a ﬁxed homotopy by at most a factor of 2. We then show a modiﬁcation of this technique which maintains only a sublinear number of valid states, as opposed to a quadratic number of states, and we provide tight lower bounds for this technique. Finally, we provide empirical results suggesting that the decrease in operations needed to compute diagrams across a family of ﬁltrations is proportional to the diﬀerence between the expected quadratic number of states, and the proposed sublinear coarsening.
Discovering High-Quality Process Models Despite Data Scarcity

Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for processes with concurrent subprocesses such as hierarchical, composite, or distributed processes. While process discovery algorithms traditionally use sequences of activities as input, recently introduced object-centric process discovery algorithms can use graphs of activities as input, encoding partial orders between activities. As such, they contain the concurrency information of many sequences in a single graph. In this paper, we address the research question of reducing process discovery data requirements when using object-centric event logs for process discovery. We classify different real-life processes according to the control-flow complexity within and between subprocesses and introduce an evaluation framework to assess process discovery algorithm quality of traditional and object-centric process discovery based on the sample size. We complement this with a large-scale production process case study. Our results show reduced data requirements, enabling the discovery of large, concurrent processes such as manufacturing with little data, previously infeasible with traditional process discovery. Our findings suggest that object-centric process mining could revolutionize process discovery in various sectors, including manufacturing and supply chains.
Suppression and amplification of phonon sidebands in transition metal dichalcogenides by optical feedback

Transition metal dichalcogenides (TMDCs) combine both strong light-matter-interaction and strong Coulomb-interaction for the formation of optically excitable excitons. Through radiative feedback control, a mechanism to control the linewidth can be applied, which modifies optical transition spectra. Here, we extend these investigations to the absorption spectra of TMDCs in a variety of geometries with respect to non-Markovian exciton-phonon-scattering contributions. Our approach is based on the self consistent solution of the microscopic Bloch equations and the macroscopic solution of the wave equation. We discuss the formation of a phonon sideband for MoSe$_2$ embedded in SiO$_2$, and two setups for enhancing or suppressing the phonon sideband in the spectrum.
Global mantle shear velocity model developed using nonlinear asymptotic coupling theory

We present a three-dimensional shear velocity model of the whole mantle developed using S H waveform data. The model is expressed horizontally in terms of spherical harmonics up to degree 12, and vertically in terms of Legendre polynomials up to degrees 5 and 7 in the upper and lower mantle, respectively. What distinguishes this model from other tomographic models published to date is (1) the theoretical normal mode-based wave propagation approach, where we include across branch mode coupling terms in order to model the body wave sensitivity to structure along the path more accurately; (2) the wave-packet weighting scheme which allows to balance contributions from high-amplitude and low-amplitude phases, increasing the resolution in some parts of the mantle. We also relax the constraints on the Moho depth, which is allowed to vary in the inversion, thus absorbing some uncertainties in crustal structure. The resulting model is generally in good agreement with other recent global mantle S velocity models and with some regional models. The rms profile with depth has more power than other models in the upper mantle/lower mantle transition region and the zone of increased power and low degree structure near the base of the mantle is confined to the last 500 km in depth. This model provides a particularly good fit to the non-hydrostatic geoid through harmonic degree 12 (79% variance reduction), as well as good fits to observed splitting functions of S velocity sensitive mantle modes, indicating that both large-scale and small-scale features are really well constrained.
SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization

Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. We propose SICNav, a Model Predictive Control (MPC) method that jointly solves for robot motion and predicted crowd motion in closed-loop. We model each human in the crowd to be following an Optimal Reciprocal Collision Avoidance (ORCA) scheme and embed that model as a constraint in the robot's local planner, resulting in a bilevel nonlinear MPC optimization problem. We use a KKT-reformulation to cast the bilevel problem as a single level and use a nonlinear solver to optimize. Our MPC method can influence pedestrian motion while explicitly satisfying safety constraints in a single-robot multi-human environment. We analyze the performance of SICNav in a simulation environment to demonstrate safe robot motion that can influence the surrounding humans. We also validate the trajectory forecasting performance of ORCA on a human trajectory dataset.
Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models

Relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods based on language models commonly have two limitations: (1) they require named entities to be either given as input or infer them, which introduces additional noise, and (2) they require human annotations of documents. As a remedy, we present a novel framework for document-level in-context few-shot relation extraction via pre-trained language models. We achieve crucial benefits in that we eliminate the need for both named entity recognition and human annotation of documents. Unlike existing methods based on fine-tuning, our framework is flexible in that it can be easily updated for a new set of relations without re-training. We evaluate our framework using DocRED, the largest publicly available dataset for document-level relation extraction, and demonstrate that our framework achieves state-of-the-art performance. Finally, we show that our framework actually performs much better than the original labels from the development set of DocRED. To the best of our knowledge, we are the first to reformulate the document-level relation extraction task as a tailored in-context few-shot learning paradigm.
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder

The Koopman operator presents an attractive approach to achieve global linearization of nonlinear systems, making it a valuable method for simplifying the understanding of complex dynamics. While data-driven methodologies have exhibited promise in approximating finite Koopman operators, they grapple with various challenges, such as the judicious selection of observables, dimensionality reduction, and the ability to predict complex system behaviours accurately. This study presents a novel approach termed Mori-Zwanzig autoencoder (MZ-AE) to robustly approximate the Koopman operator in low-dimensional spaces. The proposed method leverages a nonlinear autoencoder to extract key observables for approximating a finite invariant Koopman subspace and integrates a non-Markovian correction mechanism using the Mori-Zwanzig formalism. Consequently, this approach yields a closed representation of dynamics within the latent manifold of the nonlinear autoencoder, thereby enhancing the precision and stability of the Koopman operator approximation. Demonstrations showcase the technique's ability to capture regime transitions in the flow around a circular cylinder. It also provided a low dimensional approximation for chaotic Kuramoto-Sivashinsky with promising short-term predictability and robust long-term statistical performance. By bridging the gap between data-driven techniques and the mathematical foundations of Koopman theory, MZ-AE offers a promising avenue for improved understanding and prediction of complex nonlinear dynamics.
"Mistakes Help Us Grow": Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms

Teachers' growth mindset supportive language (GMSL)--rhetoric emphasizing that one's skills can be improved over time--has been shown to significantly reduce disparities in academic achievement and enhance students' learning outcomes. Although teachers espouse growth mindset principles, most find it difficult to adopt GMSL in their practice due the lack of effective coaching in this area. We explore whether large language models (LLMs) can provide automated, personalized coaching to support teachers' use of GMSL. We establish an effective coaching tool to reframe unsupportive utterances to GMSL by developing (i) a parallel dataset containing GMSL-trained teacher reframings of unsupportive statements with an accompanying annotation guide, (ii) a GMSL prompt framework to revise teachers' unsupportive language, and (iii) an evaluation framework grounded in psychological theory for evaluating GMSL with the help of students and teachers. We conduct a large-scale evaluation involving 174 teachers and 1,006 students, finding that both teachers and students perceive GMSL-trained teacher and model reframings as more effective in fostering a growth mindset and promoting challenge-seeking behavior, among other benefits. We also find that model-generated reframings outperform those from the GMSL-trained teachers. These results show promise for harnessing LLMs to provide automated GMSL feedback for teachers and, more broadly, LLMs' potentiality for supporting students' learning in the classroom. Our findings also demonstrate the benefit of large-scale human evaluations when applying LLMs in educational domains.
A Microcosm Approach for Assessing the Response of Indigenous Chironomids to Salinity

Ecotoxicological testing done using laboratory-reared organisms may not always reflect the responses of local indigenous species when assessing the field effects of pollutants. Here we outline and test a microcosm approach for evaluating responses of one group of aquatic macroinvertebrates, Chironomidae, that are indigenous to an area. By placing microcosms with manipulated levels of salinity within a region where salinity is perceived to be an important environmental stress, we show that three indigenous wetland chironomid species differ in their salinity response. Two of the three species are negatively affected by levels of salinity encountered in the region, suggesting that salinity could negatively impact on chironomid biodiversity in the Wimmera. This approach could be useful for examining the salinity tolerance of indigenous chironomid species within a region and validating a role for salinity in determining field distributions of chironomids.
Causal discovery using dynamically requested knowledge

Causal Bayesian Networks (CBNs) are an important tool for reasoning under uncertainty in complex real-world systems. Determining the graphical structure of a CBN remains a key challenge and is undertaken either by eliciting it from humans, using machine learning to learn it from data, or using a combination of these two approaches. In the latter case, human knowledge is generally provided to the algorithm before it starts, but here we investigate a novel approach where the structure learning algorithm itself dynamically identifies and requests knowledge for relationships that the algorithm identifies as uncertain during structure learning. We integrate this approach into the Tabu structure learning algorithm and show that it offers considerable gains in structural accuracy, which are generally larger than those offered by existing approaches for integrating knowledge. We suggest that a variant which requests only arc orientation information may be particularly useful where the practitioner has little preexisting knowledge of the causal relationships. As well as offering improved accuracy, the approach can use human expertise more effectively and contributes to making the structure learning process more transparent.
NP-RDMA: Using Commodity RDMA without Pinning Memory

Remote Direct Memory Access (RDMA) has been haunted by the need of pinning down memory regions. Pinning limits the memory utilization because it impedes on-demand paging and swapping. It also increases the initialization latency of large memory applications from seconds to minutes. To remove memory pining, existing approaches often require special hardware which supports page fault, and still have inferior performance. We propose NP-RDMA, which removes memory pinning during memory registration and enables dynamic page fault handling with commodity RDMA NICs. NP-RDMA does not require NICs to support page fault. Instead, by monitoring local memory paging and swapping with MMU-notifier, combining with IOMMU/SMMU-based address mapping, NP-RDMA efficiently detects and handles page fault in the software with near-zero additional latency to non-page-fault RDMA verbs. We implement an LD_PRELOAD library (with a modified kernel module), which is fully compatible with existing RDMA applications. Experiments show that NP-RDMA adds only 0.1{\sim}2 {\mu}s latency under non-page-fault scenarios. Moreover, NP-RDMA adds only 3.5{\sim}5.7 {\mu}s and 60 {\mu}s under minor or major page faults, respectively, which is 500x faster than ODP which uses advanced NICs that support page fault. With non-pinned memory, Spark initialization is 20x faster and the physical memory usage reduces by 86% with only 5.4% slowdown. Enterprise storage can expand to 5x capacity with SSDs while the average latency is only 10% higher. To the best of our knowledge, NP-RDMA is the first efficient and application-transparent software approach to remove memory pinning using commodity RDMA NICs.
Elemental composition of the January 6, 1997, CME

Using solar wind particle data from the CELIAS/MTOF sensor on the SOHO mission, we studied the abundance of the elements O, Ne, Mg, Si, S, Ca, and Fe for the time period around the January 6, 1997, coronal mass ejection event (CME). In the interstream and coronal hole regions before and after this event we found elemental abundances consistent with the expected abundance patterns of the respective flow regimes. However, during the passage of the CME and during the passage of the erupted filament, which followed the CME, we found that the elemental composition differed markedly from the interstream and coronal hole regions before and after this event. During the passage of the CME and the passage of the erupted filament we found a mass‐dependent element fractionation, with a monotonic increase toward heavier elements. We observed Si/O and Fe/O abundance ratios of about one half during these time periods, which is significantly higher than for typical solar wind.