Miracle-dz/UniFAR-FTUs huggingface dataset:
Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models

Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the"cold-start"problem -- it not only requires significant input from previous users to generate and share these overviews, but such overviews may also turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages Large Language Models (LLMs) as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users' sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users' information processing, and effectively improved their overall comprehension and sensemaking experience.
A new perspective on thermal transition in QCD

Motivated by the picture of partial deconfinement developed in recent years for large-$N$ gauge theories, we propose a new way of analyzing and understanding thermal phase transition in QCD. We find nontrivial support for our proposal by analyzing the WHOT-QCD collaboration's lattice configurations for SU(3) QCD in $3+1$ spacetime dimensions with up, down, and strange quarks. We find that the Polyakov line (the holonomy matrix around a thermal time circle) is governed by the Haar-random distribution at low temperatures. The deviation from the Haar-random distribution at higher temperatures can be measured via the character expansion, or equivalently, via the expectation values of the Polyakov loop defined by the various nontrivial representations of SU(3). We find that the Polyakov loop corresponding to the fundamental representation and loops in the higher representation condense at different temperatures. This suggests that there are (at least) three phases, one intermediate phase existing in between the completely-confined and the completely-deconfined phases. Our identification of the intermediate phase is supported also by the condensation of instantons: by studying the instanton numbers of the WHOT-QCD configurations, we find that the instanton condensation occurs for temperature regimes corresponding to what we identify as the completely-confined and intermediate phases, whereas the instantons do not condense in the completely-deconfined phase. Our characterization of confinement based on the Haar-randomness explains why the Polyakov loop is a good observable to distinguish the confinement and the deconfinement phases in QCD despite the absence of the $\mathbb{Z}_3$ center symmetry.
Streamflow variability in the Southern Appalachians and atmospheric teleconnections

We have examined streamflow variability in the Southern Appalachian region of the United States for the period 1950–2009. In particular, we have analysed the monthly discharge time series at two stations along the following rivers in North Carolina: (1) Little Tennessee River (LTR) near Prentiss and (2) French Broad River in Asheville. These two gauging stations are part of the Hydro‐Climatic Data Network (HCDN) system. The HCDN system was developed by the United States Geological Survey (USGS) to provide a long‐term database for tracking changes in flow and water quality of streams and rivers, with minimal human interventions. Using continuous wavelet transform (CWT), we have identified the dominant oscillatory modes in the monthly discharge data at these two rivers, and delineated the time intervals over which these modes may persist. It is found that in addition to the annual hydrologic cycle, the monthly discharge fluctuates at interannual timescales. These interannual variations may be linked to the Pacific North American (PNA) teleconnection pattern. Knowledge of the interannual periodicities may be useful for understanding long‐term streamflow variability in the Southern Appalachian region. Understanding patterns of streamflow variability may be important for water resources management operations in the surrounding area. Copyright © 2010 John Wiley & Sons, Ltd.
A maximum entropy view of probability-distributed catchment models

Abstract The probability-distributed catchment model, as originally proposed by Moore & Clarke (1981), is re-examined from a maximum statistical entropy viewpoint. The distribution of water within a catchment is treated as a problem of statistical inference and resolved using an entropy maximization technique. A simple runoff generating mechanism is employed, which, together with the catchment mass balance equation, yields a catchment model involving just one dynamic parameter, y, and two constants, k and λ. The parameter y determines the temporal variation of catchment storage V and runoff q. The latter is nonlinearly related to V through q = k(1—λyV), where y provides the nonlinear departure from the simple linear reservoir q = kV.
Discrete, compositional, and symbolic representations through attractor dynamics

Compositionality is an important feature of discrete symbolic systems, such as language and programs, as it enables them to have infinite capacity despite a finite symbol set. It serves as a useful abstraction for reasoning in both cognitive science and in AI, yet the interface between continuous and symbolic processing is often imposed by fiat at the algorithmic level, such as by means of quantization or a softmax sampling step. In this work, we explore how discretization could be implemented in a more neurally plausible manner through the modeling of attractor dynamics that partition the continuous representation space into basins that correspond to sequences of symbols. Building on established work in attractor networks and introducing novel training methods, we show that imposing structure in the symbolic space can produce compositionality in the attractor-supported representation space of rich sensory inputs. Lastly, we argue that our model exhibits the process of an information bottleneck that is thought to play a role in conscious experience, decomposing the rich information of a sensory input into stable components encoding symbolic information.
Turbulent Structure in Supernova Remnants G46.8−0.3 and G39.2−0.3 from THOR Polarimetry

We present the continued analysis of polarization and Faraday rotation for the supernova remnants (SNRs) G46.8−0.3 and G39.2−0.3 in L-band (1–2 GHz) radio continuum in The H i/OH/Recombination line (THOR) survey. In this work, we present our investigation of Faraday depth fluctuations from angular scales comparable to the size of the SNRs down to scales less than our 16″ beam (≲0.7 pc) from Faraday dispersion (σ ϕ ). From THOR, we find median σ ϕ of 15.9 ± 3.2 rad m−2 for G46.8−0.3 and 17.6 ± 1.6 rad m−2 for G39.2−0.3. When comparing to polarization at 6 cm, we find evidence for σ ϕ ≳ 30 rad m−2 in localized regions where we detect no polarization in THOR. We combine Faraday depth dispersion with the rotation measure (RM) structure function (SF) and find evidence for a break in the SF on scales less than the THOR beam. We estimate the RM SF of the foreground interstellar medium using the SF of extragalactic radio sources and pulsars to find that the RM fluctuations we measure originate within the SNRs for all but the largest angular scales.
Effects of evapotranspiration partitioning, plant water stress response and topsoil removal on the soil moisture regime of a floodplain wetland: implications for restoration

Understanding how the soil moisture regime changes following hydrological restoration activities in wetlands is essential for managers interested in establishing targeted wetland vegetation species. Based on observations from two floodplain sites in southwestern Wisconsin, a variably saturated hydrological model was developed and used to illustrate the importance of changing evapotranspiration (ET) partitioning [through changes in leaf‐area index (LAI)], the plant water stress function, and the thickness of a surficial silt‐loam alluvial deposit on the soil moisture regime. Floodplain restoration activities at these sites consisted of removing a layer of post‐settlement alluvium, which is a part of this deposit. Field observations following restoration of the pre‐settlement floodplain surface at one of the sites show a drier soil moisture regime at 10 and 25 cm depths at the restored (RES) site than at the unrestored site even though the potentiometric surface in a shallow monitoring piezometer was closer to the surface at the RES site. Simulations show that four factors may have contributed to this observation of relatively drier soils at the RES site: (1) lower LAI increases soil evaporation, (2) anaerobiosis limits transpiration near saturation, (3) a confining unit inhibits upward flow of water, and (4) removal of a 0·5‐m silt‐loam layer decreases the reservoir of soil moisture available to plant roots. Our work suggests the importance of effectively characterizing parameters that affect ET partitioning (i.e. LAI), determining the appropriate plant water stress function, and considering the effect of floodplain deposits that act as confining units when simulating the hydrology of floodplain wetland ecosystems. Copyright © 2010 John Wiley & Sons, Ltd.
Time‐frequency variability of Kuroshio meanders in Tokara Strait

The meander of the Kuroshio path, with periods of 30–90 days, dominates Tokara Strait, Japan. To understand the time‐frequency variability of this meander, two Kuroshio indicators were subjected to wavelet analysis for the period 1984–2004. The 30–90 day variance of the position of the Kuroshio in Tokara Strait shows a season‐fixed nature, with larger amplitudes in the period December–July. The amplitude of the variance in this phenomenon is also modulated by interannual variations, with small variance recorded during 1989–1992, large variance during 1993–1998, and a return to small variance from 1999–2003. This interannual variation is positively correlated with that of Kuroshio volume transport in Tokara Strait. The largest variance during February–April precedes the largest volume transport in April–May by about 1 month, suggesting that mesoscale menders are the forcing of the volume transport increase, not the other way around.
On the natural nullcones of the symplectic and general linear groups

Consider a group acting on a polynomial ring $S$ over a field $K$ by degree-preserving $K$-algebra automorphisms. The invariant ring $R$ is a graded subring of $S$; let $\mathfrak{m}_R$ denote the homogeneous maximal ideal of $R$. Several key properties of the invariant ring and its embedding in $S$ can be deduced by studying the nullcone $S/\mathfrak{m}_R S$ of the group action. This includes, for example, the finite generation of the invariant ring and the purity of the embedding. In this article, we study the nullcones arising from the natural actions of the symplectic and general linear groups. For the natural representation of the symplectic group, the invariant ring is the ring defined by the principal Pfaffians of a fixed even size of a generic alternating matrix. We show that the nullcone of this embedding is a strongly $F$-regular ring in positive characteristic, and hence in characteristic zero, a ring of strongly $F$-regular type. Independent of characteristic, we give a complete description of the divisor class group of the nullcone and determine precisely when it is Gorenstein. We also show that the nullcone ideal has a squarefree initial ideal. For the natural representation of the general linear group, the invariant ring is a generic determinantal ring. The nullcone of this embedding is typically a non-equidimensional ring. The irreducible components of the nullcone are the varieties of complexes of length two, as introduced by Buchsbaum and Eisenbud. We show that each of these irreducible components define strongly $F$-regular rings in positive characteristic. We also show that the Frobenius splitting of the varieties of complexes can be chosen compatibly; it follows that the nullcone is an $F$-pure ring. We also show that the nullcone ideal and the ideals defining the varieties of complexes have squarefree initial ideals with respect to the same monomial order.
Throughput and Coverage Trade-Off in Integrated Terrestrial and Non-Terrestrial Networks: An Optimization Framework

In past years, non-terrestrial networks (NTNs) have emerged as a viable solution for providing ubiquitous connectivity for future wireless networks due to their ability to reach large geographical areas. However, the efficient integration and operation of an NTN with a classic terrestrial network (TN) is challenging due the large amount of parameters to tune. In this paper, we consider the downlink scenario of an integrated TN-NTN transmitting over the S band, comprised of low-earth orbit (LEO) satellites overlapping a large-scale ground cellular network. We propose a new resource management framework to optimize the user equipment (UE) performance by properly controlling the spectrum allocation, the UE association and the transmit power of ground base stations (BSs) and satellites. Our study reveals that, in rural scenarios, NTNs, combined with the proposed radio resource management framework, reduce the number of UEs that are out of coverage, highlighting the important role of NTNs in providing ubiquitous connectivity, and greatly improve the overall capacity of the network. Specifically, our solution leads to more than 200% gain in terms of mean data rate with respect to a network without satellites and a standard integrated TN-NTN when the resource allocation setting follows 3GPP recommendation.
High‐resolution 40Ar/39Ar dating using a mechanical sample transfer system combined with a high‐temperature cell for step heating experiments and a multicollector ARGUS noble gas mass spectrometer

40Ar/39Ar dating of young (<1 Ma) and/or low potassium samples requires degassing of large amounts of material either by a laser or a furnace. A furnace excels in precise and reproducible temperature control and in providing a homogeneous temperature distribution even for relatively large sample amounts. For most systems, however, the degassed mineral or whole‐rock residues remain in the crucible after measurement. This reduces the thermal conductivity and increases the blank levels and baking times. To mitigate these problems, we combined a mechanical sample transfer system with a low volume (∼90 mL) high‐temperature cell. The system operates under ultrahigh vacuum and uses Mo‐crucibles, in which up to 1000 mg of sample is transferred into the furnace and taken out after degassing. The system has short baking times (20–30 min), highly reproducible heating conditions and an overall low blank level due to the absence of silicate (glassy) sample residue in the main crucible of the furnace. The system interconnects through a gas purification line with a low volume, high sensitivity multicollector ARGUS noble gas mass spectrometer. Intraday reproducibility of 40Ar/36Ar ratios measured on air aliquots of >5 × 10−16 mol 36Ar is better than 0.5‰–1.0‰ (1σ, n = 4–8). We illustrate the system performance by 40Ar/39Ar dating of whole‐rock samples and mineral separates from the Oman ophiolite as well as from the Siebengebirge, Heldburg, and Rhön volcanic provinces in Central Germany.
How do Software Engineering Researchers Use GitHub? An Empirical Study of Artifacts & Impact

Millions of developers share their code on open-source platforms like GitHub, which offer social coding opportunities such as distributed collaboration and popularity-based ranking. Software engineering researchers have joined in as well, hosting their research artifacts (tools, replication package&datasets) in repositories, an action often marked as part of the publications contribution. Yet a decade after the first such paper-with-GitHub-link, little is known about the fate of such repositories in practice. Do research repositories ever gain the interest of the developer community, or other researchers? If so, how often and why (not)? Does effort invested on GitHub pay off with research impact? In short: we ask whether and how authors engage in social coding related to their research. We conduct a broad empirical investigation of repositories from published work, starting with ten thousand papers in top SE research venues, hand-annotating their 3449 GitHub (and Zenodo) links, and studying 309 paper-related repositories in detail. We find a wide distribution in popularity and impact, some strongly correlated with publication venue. These were often heavily informed by the authors investment in terms of timely responsiveness and upkeep, which was often remarkably subpar by GitHubs standards, if not absent altogether. Yet we also offer hope: popular repositories often go hand-in-hand with well-citepd papers and achieve broad impact. Our findings suggest the need to rethink the research incentives and reward structure around research products requiring such sustained contributions.
MarineDet: Towards Open-Marine Object Detection

Marine object detection has gained prominence in marine research, driven by the pressing need to unravel oceanic mysteries and enhance our understanding of invaluable marine ecosystems. There is a profound requirement to efficiently and accurately identify and localize diverse and unseen marine entities within underwater imagery. The open-marine object detection (OMOD for short) is required to detect diverse and unseen marine objects, performing categorization and localization simultaneously. To achieve OMOD, we present \textbf{MarineDet}. We formulate a joint visual-text semantic space through pre-training and then perform marine-specific training to achieve in-air-to-marine knowledge transfer. Considering there is no specific dataset designed for OMOD, we construct a \textbf{MarineDet dataset} consisting of 821 marine-relative object categories to promote and measure OMOD performance. The experimental results demonstrate the superior performance of MarineDet over existing generalist and specialist object detection algorithms. To the best of our knowledge, we are the first to present OMOD, which holds a more valuable and practical setting for marine ecosystem monitoring and management. Our research not only pushes the boundaries of marine understanding but also offers a standard pipeline for OMOD.
Distributionally Robust Path Integral Control

We consider a continuous-time continuous-space stochastic optimal control problem, where the controller lacks exact knowledge of the underlying diffusion process, relying instead on a finite set of historical disturbance trajectories. In situations where data collection is limited, the controller synthesized from empirical data may exhibit poor performance. To address this issue, we introduce a novel approach named Distributionally Robust Path Integral (DRPI). The proposed method employs distributionally robust optimization (DRO) to robustify the resulting policy against the unknown diffusion process. Notably, the DRPI scheme shows similarities with risk-sensitive control, which enables us to utilize the path integral control (PIC) framework as an efficient solution scheme. We derive theoretical performance guarantees for the DRPI scheme, which closely aligns with selecting a risk parameter in risk-sensitive control. We validate the efficacy of our scheme and showcase its superiority when compared to risk-neutral PIC policies in the absence of the true diffusion process.
Decentralization Cheapens Corruptive Majority Attacks

Corruptive majority attacks, in which mining power is distributed among miners and an attacker attempts to bribe a majority of miners into participation in a majority attack, pose a threat to blockchains. Budish bounded the cost of bribing miners to participate in an attack by their expected loss as a result of attack success. We show that this bound is loose. In particular, an attack may be structured so that under equilibrium play by most miners, a miner's choice to participate only slightly affects the attack success chance. Combined with the fact that most of the cost of attack success is externalized by any given small miner, this implies that if most mining power is controlled by small miners, bribing miners to participate in such an attack is much cheaper than the Budish bound. We provide a scheme for a cheap corruptive majority attack and discuss practical concerns and consequences.
Multi-class Network Intrusion Detection with Class Imbalance via LSTM & SMOTE

Monitoring network traffic to maintain the quality of service (QoS) and to detect network intrusions in a timely and efficient manner is essential. As network traffic is sequential, recurrent neural networks (RNNs) such as long short-term memory (LSTM) are suitable for building network intrusion detection systems. However, in the case of a few dataset examples of the rare attack types, even these networks perform poorly. This paper proposes to use oversampling techniques along with appropriate loss functions to handle class imbalance for the detection of various types of network intrusions. Our deep learning model employs LSTM with fully connected layers to perform multi-class classification of network attacks. We enhance the representation of minority classes: i) through the application of the Synthetic Minority Over-sampling Technique (SMOTE), and ii) by employing categorical focal cross-entropy loss to apply a focal factor to down-weight examples of the majority classes and focus more on hard examples of the minority classes. Extensive experiments on KDD99 and CICIDS2017 datasets show promising results in detecting network intrusions (with many rare attack types, e.g., Probe, R2L, DDoS, PortScan, etc.).
Generative Autoencoding of Dropout Patterns

We propose a generative model termed Deciphering Autoencoders. In this model, we assign a unique random dropout pattern to each data point in the training dataset and then train an autoencoder to reconstruct the corresponding data point using this pattern as information to be encoded. Since the training of Deciphering Autoencoders relies solely on reconstruction error, it offers more stable training than other generative models. Despite its simplicity, Deciphering Autoencoders show comparable sampling quality to DCGAN on the CIFAR-10 dataset.