Miracle-dz/UniFAR-FTUs huggingface dataset:
GLoRE: Evaluating Logical Reasoning of Large Language Models

Recently, large language models (LLMs), including notable models such as GPT-4 and burgeoning community models, have showcased significant general language understanding abilities. However, there has been a scarcity of attempts to assess the logical reasoning capacities of these LLMs, an essential facet of natural language understanding. To encourage further investigation in this area, we introduce GLoRE, a meticulously assembled General Logical Reasoning Evaluation benchmark comprised of 12 datasets that span three different types of tasks. Our experimental results show that compared to the performance of human and supervised fine-tuning, the logical reasoning capabilities of open LLM models necessitate additional improvement; ChatGPT and GPT-4 show a strong capability of logical reasoning, with GPT-4 surpassing ChatGPT by a large margin. We propose a self-consistency probing method to enhance the accuracy of ChatGPT and a fine-tuned method to boost the performance of an open LLM. We release the datasets and evaluation programs to facilitate future research.
Cladoceran-inferred environmental change during the LGM to Holocene transition from Onepoto maar paleolake, Auckland, New Zealand

Abstract Subfossil cladocerans have rarely been used for paleoenvironmental reconstruction from New Zealand lake sediments, and here we detail the first examination of the response of cladocerans to past environments from an Auckland maar paleolake. Cladoceran remains were examined in the upper 230 cm of the lacustrine sediments spanning c. 9–31 cal. ka BP from Onepoto maar during which time the lake underwent significant changes. Lake level was relatively high during the Last Glacial Coldest Period (c. 28–18 cal. ka BP), with limited forest in the catchment, shoreline vegetation and low water temperature indicated by the presence of a planktonic taxon, Bosmina meridionalis, and a cold tolerant chydorid cladoceran Alona sp. (affinis type). However, the climate during the last glacial termination after c. 17.9 cal. ka BP changed abruptly, perhaps becoming extremely dry and windy, resulting in increased production of cladoceran resting eggs. Between c. 17.6 and 14.1 cal. ka BP, a gradually ameliorating climate with sustained windiness and dryness might have been unfavourable for hatching of chydorid cladoceran eggs and their recolonisation. After c. 14.1 cal. ka BP, continued climatic amelioration was accompanied by reduced lake levels inferred from high cladoceran littoral: planktonic ratios as well as increased cladoceran diversity and abundance of less cold tolerant chydorid taxa: Alona guttata, Alona sp. (intermedia type) and Alonella excisa. At c. 9 cal. ka BP, the maar crater rim was breached by the marine waters commensurate with postglacial sea-level rise, resulting in degraded water quality and production of a large number of cladoceran ephippia in the sediments.
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising

Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectiveness and practicality of synthetic data. Currently, physics-based noise modeling struggles to characterize the entire real noise distribution, while learning-based noise modeling impractically depends on paired real data. In this paper, we propose a novel strategy: learning the noise model from dark frames instead of paired real data, to break down the data dependency. Based on this strategy, we introduce an efficient physics-guided noise neural proxy (PNNP) to approximate the real-world sensor noise model. Specifically, we integrate physical priors into neural proxies and introduce three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution loss (DDL). PND decouples the dark frame into different components and handles different levels of noise flexibly, which reduces the complexity of noise modeling. PPM incorporates physical priors to constrain the generated noise, which promotes the accuracy of noise modeling. DDL provides explicit and reliable supervision for noise distribution, which promotes the precision of noise modeling. PNNP exhibits powerful potential in characterizing the real noise distribution. Extensive experiments on public datasets demonstrate superior performance in practical low-light raw image denoising. The code will be available at \url{https://github.com/fenghansen/PNNP}.
MEASUREMENT OF MOLECULAR DIFFUSION OF SALT IN UNSATURATED SOILS

The molecular diffusion coefficients of salt were measured in Shonai sand dune sand and Kanto loam soils over a wide range of water contents using a transient state method. The measured relative diffusion coefficient, Dr, the ratio of diffusivity of salt in soil to the diffusivity in bulk water, varies from 0.31 to 2.98 × 10-3 for Shonai dune sand as volumetric water content decreases from 31.74% (0.00215 MPa) to 2.13% (5 MPa). For Kanto loam soil it varies from 0.17 to 6.25 × 10-3 as water content decreases from 60.89% (0.0021 MPa) to 20.22% (3.11 MPa suction). The relationship of Dr with water content in soil shows a linear relationship on log-log scale at higher water contents for both soils. However, it shows a break point at the water content that corresponds to a matric suction of about 0.09 MPa for both soils. The respective slopes of Dr(&thetas;) for Shonai dune sand and Kanto loam are 0.75 and 1.25 at higher water contents (> break point) and 3.99 and 4.1 at lower water contents. The relationship of Dr with matric suction is found to be less dependent on the soil texture.
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models

Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \emph{aligned} at a sample level, where squared bias is approximately \emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: calibration and neural collapse. We first show theoretically that under the assumption that the models are well calibrated, we can observe the bias-variance alignment. Second, starting from the picture provided by the neural collapse theory, we show an approximate correlation between bias and variance.
Voyager: MTD-Based Aggregation Protocol for Mitigating Poisoning Attacks on DFL

The growing concern over malicious attacks targeting the robustness of both Centralized and Decentralized Federated Learning (FL) necessitates novel defensive strategies. In contrast to the centralized approach, Decentralized FL (DFL) has the advantage of utilizing network topology and local dataset information, enabling the exploration of Moving Target Defense (MTD) based approaches. This work presents a theoretical analysis of the influence of network topology on the robustness of DFL models. Drawing inspiration from these findings, a three-stage MTD-based aggregation protocol, called Voyager, is proposed to improve the robustness of DFL models against poisoning attacks by manipulating network topology connectivity. Voyager has three main components: an anomaly detector, a network topology explorer, and a connection deployer. When an abnormal model is detected in the network, the topology explorer responds strategically by forming connections with more trustworthy participants to secure the model. Experimental evaluations show that Voyager effectively mitigates various poisoning attacks without imposing significant resource and computational burdens on participants. These findings highlight the proposed reactive MTD as a potent defense mechanism in the context of DFL.
The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS

The sampling strategy of the Transiting Exoplanet Survey Satellite (TESS) makes TESS light curves extremely valuable for investigating the high cadence optical variability of active galactic nuclei (AGN). However, because the TESS instrument was primarily designed for exoplanet science, the use of the satellite for other applications requires careful treatment of the data. In this paper, we introduce Quaver, a new software tool designed specifically to extract TESS light curves of extended and faint sources presenting stochastic variability. We then use this new tool to extract light curves of the nearby radio-loud AGN Pictor A, and perform a temporal and power spectral analysis of its high-cadence optical variability. The obtained light curves are well fit with a damped random walk (DRW) model, exhibiting both stochastic AGN variations and flaring behavior. The DRW characteristic timescales are τ DRW ∼ 3–6 days during more quiet periods, and τ DRW ∼ 0.8 day for periods with strong flares, even when the flares themselves are masked from the DRW fit. The observed timescales are consistent with the dynamical, orbital, and thermal timescales expected for the low black hole mass of Pictor A.
A one-query lower bound for unitary synthesis and breaking quantum cryptography

The Unitary Synthesis Problem (Aaronson-Kuperberg 2007) asks whether any $n$-qubit unitary $U$ can be implemented by an efficient quantum algorithm $A$ augmented with an oracle that computes an arbitrary Boolean function $f$. In other words, can the task of implementing any unitary be efficiently reduced to the task of implementing any Boolean function? In this work, we prove a one-query lower bound for unitary synthesis. We show that there exist unitaries $U$ such that no quantum polynomial-time oracle algorithm $A^f$ can implement $U$, even approximately, if it only makes one (quantum) query to $f$. Our approach also has implications for quantum cryptography: we prove (relative to a random oracle) the existence of quantum cryptographic primitives that remain secure against all one-query adversaries $A^{f}$. Since such one-query algorithms can decide any language, solve any classical search problem, and even prepare any quantum state, our result suggests that implementing random unitaries and breaking quantum cryptography may be harder than all of these tasks. To prove this result, we formulate unitary synthesis as an efficient challenger-adversary game, which enables proving lower bounds by analyzing the maximum success probability of an adversary $A^f$. Our main technical insight is to identify a natural spectral relaxation of the one-query optimization problem, which we bound using tools from random matrix theory. We view our framework as a potential avenue to rule out polynomial-query unitary synthesis, and we state conjectures in this direction.
Perturbing parameters to understand cloud contributions to climate change

The sensitivity of cloud feedbacks to atmospheric model parameters is evaluated using a CAM6 perturbed parameter ensemble (PPE). The CAM6 PPE perturbs 45 parameters across 262 simulations, 206 of which are used here. The spread in total cloud feedback and its six components across the CAM6 PPE are comparable to the spread across the CMIP6 and AMIP ensembles, indicating that parametric uncertainty mirrors structural uncertainty. However, the high-cloud altitude feedback is generally larger in the CAM6 PPE than WCRP assessment, CMIP6, and AMIP values. We evaluate the influence of each of the 45 parameters on the total cloud feedback and each of the six cloud feedback components. We also explore whether the CAM6 PPE can be used to constrain the total cloud feedback, with inconclusive results. Further, we find that despite the large parametric sensitivity of cloud feedbacks in CAM6, a substantial increase in cloud feedbacks from CAM5 to CAM6 is not a result of changes in parameter values. Notably, the CAM6 PPE is run with a more recent version of CAM6 (CAM6.3) than was used for AMIP (CAM6.0), and has a smaller total cloud feedback (0.56 W m−2 K−1) as compared to CAM6.0 (0.81 W m−2 K−1) owing primarily to reductions in low clouds over the tropics and middle latitudes. The work highlights the large sensitivity of cloud feedbacks to both parameter values and structural details in CAM6.
Neural activity in quarks language: Lattice Field Theory for a network of real neurons

Brain-computer interfaces surged extraordinary developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro- and meso-scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neurons activity, and generalize the maximum entropy model for neural networks so that also the time evolution of the system is taken into account. This is obtained by bridging particle physics and neuroscience, paving the way to particle physics-inspired models of neocortex.
CoLadder: Supporting Programmers with Hierarchical Code Generation in Multi-Level Abstraction

Programmers increasingly rely on Large Language Models (LLMs) for code generation. However, misalignment between programmers' goals and generated code complicates the code evaluation process and demands frequent switching between prompt authoring and code evaluation. Yet, current LLM-driven code assistants lack sufficient scaffolding to help programmers format intentions from their overarching goals, a crucial step before translating these intentions into natural language prompts. To address this gap, we adopted an iterative design process to gain insights into programmers' strategies when using LLMs for programming. Building on our findings, we created CoLadder, a system that supports programmers by facilitating hierarchical task decomposition, direct code segment manipulation, and result evaluation during prompt authoring. A user study with 12 experienced programmers showed that CoLadder is effective in helping programmers externalize their problem-solving intentions flexibly, improving their ability to evaluate and modify code across various abstraction levels, from goal to final code implementation.
Migmatite structures in the Central Gneiss Complex, Boca de Quadra, Alaska

Migmatite structures in the Coast Plutonic-Metamorphic Complex are well exposed in the inlet of Boca de Quadra, southeast Alaska. Two types of anatectic migmatites are present. Patch migmatites formed by in situ melting and subsequent crystallization of melt. Diktyonitic migmatites comprise a discontinuous veined network of leucocratic material, in which leucosomes enclose boudins of host rock. The margins of these boudins show the development of both melanosomes and shear band fabrics. Strain analysis of diktyonitic melanosomes indicates that these regions have undergone volume decreases of 20-27%. This volume decrease is attributed to melt extraction into the adjacent fracture-filling leucosomes. Thus, diktyonitic migmatites formed by shear-induced segregation of partial melt, whereas in patch migmatites the lack of shear stresses inhibited melt segregation. The variable structural style of anatectic migmatites in Boca de Quadra is not related to host-rock composition, but may be due to differences in the amount of differential stress during migmatization. These in turn may be controlled by host-rock strength and/or diachroneity of migmatization and deformation. Determination of volume changes during migmatization using strain analysis is potentially capable of discriminating intrusive and anatectic migmatites and consequently of documenting melt segregation and subsequent migration across crustal levels.
The effect of included participates on the spectral albedo of sea ice

Sediments and other participates are often entrained into sea ice formed over shallow shelves in the Arctic, causing significant changes in the albedo of the ice and in the amount of shortwave radiation absorbed and transmitted by the ice. A structural-optical model was used in conjunction with a four-stream radiative transfer model to examine the effects of such particulates on the optical properties of sea ice. Albedo data from well-characterized ice with moderate particulate loading were combined with model calculations to infer a spectral absorption coefficient and effective size for the particulates. Results indicate that sediment particles contained in the ice have an effective radius (R) of ∼9 μm, assuming absorption coefficients similar to those of Saharan dust. With these values, model predictions are in close agreement with spectral albedo observations over a broad range of particulate loading. For particle size distributions commonly observed in sea ice, the calculations indicate that particles with R>30 μm have little effect on the bulk optical properties of the ice. The albedo data also suggest that even apparently “clean” ice contains trace amounts (5–10 g m−3) of particulates that reduce albedos by as much as 5–10% in the visible part of the spectrum. The calculations show that particulates in sea ice primarily affect radiative transfer at visible wavelengths, whereas apparent optical properties in the near-infrared tend to be governed by ice structure rather than by the presence of particulates. Particle-bearing layers occurring below ∼20–30 cm are found to have little effect on albedo, although they can still have a substantial effect on transmission. Estimates of total particle loading cannot be obtained from reflectance data without some additional information on particle size, vertical distribution, and ice structure.
Rapid water table responses to rainfall in a northern peatland ecosystem

: Two types of rapid water table responses to rain were observed in a northern Michigan peatland. The first, called the Lisse effect, occurred during rains of high intensity when the infiltrating water acted as a tightly closing lid that forced the water table to rise to the level required to compensate for the pressure increase. The second, called the Wieringermeer effect, was a rapid rise of the water table to the surface due to the conversion of capillary to phreatic water and was always followed by an equally rapid decline after cessation of the rainfall. We simulated these phenomena in the laboratory and estimated the critical parameters that determine their occurrence. The recognition of the importance of the capillary fringe is essential in evaluating the role of wetlands in flood control and in wastewater treatment.
Perturbatively including inhomogeneities in axion inflation

Axion inflation, i.e. an axion-like inflaton coupled to an Abelian gauge field through a Chern-Simons interaction, comes with a rich and testable phenomenology. This is particularly true in the strong backreaction regime, where the gauge field production heavily impacts the axion dynamics. Lattice simulations have recently demonstrated the importance of accounting for inhomogeneities of the axion field in this regime. We propose a perturbative scheme to account for these inhomogeneities while maintaining high computational efficiency. Our goal is to accurately capture deviations from the homogeneous axion field approximation within the perturbative regime as well as self-consistently determine the onset of the non-perturbative regime.
Rank-DETR for High Quality Object Detection

Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.
The Unsteady Lift Produced by a Flat-Plate Wing Translating Past Finite Obstacles

The unsteady lift of a high-angle-of-attack, flat-plate wing encountering finite-length obstacles is studied using towing-tank force measurements. The wing translates from rest and interacts with a rectangular channel, ceiling, or ground obstacle. Variations with angle of attack, obstacle length, mid-chord height to the obstacle, and starting distance between the wing leading edge (LE) and obstacle (typically 1 chord) are examined. For channels, as the gap height decreases, circulatory-lift peaks attributed to leading-edge vortices (LEVs) are the largest, and from the second peak onward occur earliest. This is likely from wing blockage enhancing the flow speed. The lift reduces while exiting a channel, and is lowest afterward if exiting during a lift peak. For ceilings, the first circulatory-lift maximum increases for smaller LE-to-ceiling gaps, but for gaps of 0.5 chords or less, subsequent peaks are below the no-obstacle case yet still earlier. For grounds, with lower wing height the first circulatory-lift peak is larger but the second peak's behavior varies with angle of attack, and the lift decreases near the ground end. Grounds affect peak timing the least, indicating a reduced influence on the LEV. Changing the starting distance to a channel alters the lift, likely from different LEV timing.