Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL

The divergence of the Q-value estimation has been a prominent issue in offline RL, where the agent has no access to real dynamics. Traditional beliefs attribute this instability to querying out-of-distribution actions when bootstrapping value targets. Though this issue can be alleviated with policy constraints or conservative Q estimation, a theoretical understanding of the underlying mechanism causing the divergence has been absent. In this work, we aim to thoroughly comprehend this mechanism and attain an improved solution. We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL. Then, we propose a novel Self-Excite Eigenvalue Measure (SEEM) metric based on Neural Tangent Kernel (NTK) to measure the evolving property of Q-network at training, which provides an intriguing explanation of the emergence of divergence. For the first time, our theory can reliably decide whether the training will diverge at an early stage, and even predict the order of the growth for the estimated Q-value, the model's norm, and the crashing step when an SGD optimizer is used. The experiments demonstrate perfect alignment with this theoretic analysis. Building on our insights, we propose to resolve divergence from a novel perspective, namely improving the model's architecture for better extrapolating behavior. Through extensive empirical studies, we identify LayerNorm as a good solution to effectively avoid divergence without introducing detrimental bias, leading to superior performance. Experimental results prove that it can still work in some most challenging settings, i.e. using only 1 transitions of the dataset, where all previous methods fail. Moreover, it can be easily plugged into modern offline RL methods and achieve SOTA results on many challenging tasks. We also give unique insights into its effectiveness.
Coupled linear Schr\"odinger equations: Control and stabilization results

This article presents some controllability and stabilization results for a system of two coupled linear Schr\"odinger equations in the one-dimensional case where the state components are interacting through the Kirchhoff boundary conditions. Considering the system in a bounded domain, the null boundary controllability result is shown. The result is achieved thanks to a new Carleman estimate, which ensures a boundary observation. Additionally, this boundary observation together with some trace estimates, helps us to use the Gramian approach, with a suitable choice of feedback law, to prove that the system under consideration decays exponentially to zero at least as fast as the function $e^{-2\omega t}$ for some $\omega>0$.
Harnessing the Power of ChatGPT in Fake News: An In-Depth Exploration in Generation, Detection and Explanation

The rampant spread of fake news has adversely affected society, resulting in extensive research on curbing its spread. As a notable milestone in large language models (LLMs), ChatGPT has gained significant attention due to its exceptional natural language processing capabilities. In this study, we present a thorough exploration of ChatGPT's proficiency in generating, explaining, and detecting fake news as follows. Generation -- We employ four prompt methods to generate fake news samples and prove the high quality of these samples through both self-assessment and human evaluation. Explanation -- We obtain nine features to characterize fake news based on ChatGPT's explanations and analyze the distribution of these factors across multiple public datasets. Detection -- We examine ChatGPT's capacity to identify fake news. We explore its detection consistency and then propose a reason-aware prompt method to improve its performance. Although our experiments demonstrate that ChatGPT shows commendable performance in detecting fake news, there is still room for its improvement. Consequently, we further probe into the potential extra information that could bolster its effectiveness in detecting fake news.
CONGRuENTS (COsmic-ray, Neutrino, Gamma-ray and Radio Non-Thermal Spectra). II. Population-level correlations between galactic infrared, radio, and {\gamma}-ray emission

Galaxies obey a number of empirical correlations between their radio, {\gamma}-ray, and infrared emission, but the physical origins of these correlations remain uncertain. Here we use the CONGRuENTS model for broadband non-thermal emission from star-forming galaxies, which self-consistently calculates energy-dependent transport and non-thermal emission from cosmic ray hadrons and leptons, to predict radio and {\gamma}-ray emission for a synthetic galaxy population with properties drawn from a large deep-field survey. We show that our synthetic galaxies reproduce observed relations such as the FIR-radio correlation, the FIR-{\gamma} correlation, and the distribution of radio spectral indices, and we use the model to explain the physical origins of these relations. Our results show that the FIR-radio correlation arises because the amount of cosmic ray electron power ultimately radiated as synchrotron emission varies only weakly with galaxy star formation rate as a result of the constraints imposed on gas properties by hydrostatic balance and turbulent dynamo action; the same physics dictates the extent of proton calorimetry in different galaxies, and thus sets the FIR-{\gamma}-ray correlation. We further show that galactic radio spectral indices result primarily from competition between thermal free-free emission and energy-dependent loss of cosmic ray electrons to bremsstrahlung and escape into galactic halos, with shaping of the spectrum by inverse Compton, synchrotron, and ionisation processes typically playing a sub-dominant role. In addition to explaining existing observations, we use our analysis to predict a heretofore unseen correlation between the curvature of galaxies' radio spectra and their pion-driven {\gamma}-ray emission, a prediction that will be testable with upcoming facilities.
Quantile-based Maximum Likelihood Training for Outlier Detection

Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning. Furthermore, unsupervised generative modeling of inliers in pixel space has shown limited success for outlier detection. In this work, we introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood. The experimental evaluation demonstrates the effectiveness of our method as it surpasses the performance of the state-of-the-art unsupervised methods for outlier detection. The results are also competitive compared with a recent self-supervised approach for outlier detection. Our work allows to reduce dependency on well-sampled negative training data, which is especially important for domains like medical diagnostics or remote sensing.
On the Amplification of Cache Occupancy Attacks in Randomized Cache Architectures

In this work, we explore the applicability of cache occupancy attacks and the implications of secured cache design rationales on such attacks. In particular, we show that one of the well-known cache randomization schemes, MIRAGE, touted to be resilient against eviction-based attacks, amplifies the chances of cache occupancy attack, making it more vulnerable compared to contemporary designs. We leverage MIRAGE's global eviction property to demonstrate covert channel with byte-level granularity, with far less cache occupancy requirement (just $10\%$ of LLC) than other schemes. For instance, ScatterCache (a randomisation scheme with lesser security guarantees than MIRAGE) and generic set-associative caches require $40\%$ and $30\%$ cache occupancy, respectively, to exhibit covert communication. Furthermore, we extend our attack vectors to include side-channel, template-based fingerprinting of workloads in a cross-core setting. We demonstrate the potency of such fingerprinting on both inhouse LLC simulator as well as on SPEC2017 workloads on gem5. Finally, we pinpoint implementation inconsistencies in MIRAGE's publicly available gem5 artifact which motivates a re-evaluation of the performance statistics of MIRAGE with respect to ScatterCache and baseline set-associative cache. We find MIRAGE, in reality, performs worse than what is previously reported in literature, a concern that should be addressed in successor generations of secured caches.
CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis

Analyzing model performance in various unseen environments is a critical research problem in the machine learning community. To study this problem, it is important to construct a testbed with out-of-distribution test sets that have broad coverage of environmental discrepancies. However, existing testbeds typically either have a small number of domains or are synthesized by image corruptions, hindering algorithm design that demonstrates real-world effectiveness. In this paper, we introduce CIFAR-10-Warehouse, consisting of 180 datasets collected by prompting image search engines and diffusion models in various ways. Generally sized between 300 and 8,000 images, the datasets contain natural images, cartoons, certain colors, or objects that do not naturally appear. With CIFAR-10-W, we aim to enhance the evaluation and deepen the understanding of two generalization tasks: domain generalization and model accuracy prediction in various out-of-distribution environments. We conduct extensive benchmarking and comparison experiments and show that CIFAR-10-W offers new and interesting insights inherent to these tasks. We also discuss other fields that would benefit from CIFAR-10-W.
Global Permian tetrapod biostratigraphy and biochronology

Abstract The most extensive Permian tetrapod (amphibian and reptile) fossil records from the western United States (New Mexico-Texas) and South Africa provide the basis for definition of 10 land-vertebrate faunachrons that encompass Permian time. These are (in ascending order): the Coyotean, Seymouran, Mitchellcreekian, Redtankian, Littlecrotonian, Kapteinskraalian, Gamkan, Hoedemakeran, Steilkransian and Platbergian. These faunachrons provide a biochronological framework with which to determine and discuss the age relationships of Permian tetrapod faunas. Their correlation to the marine time scale and its numerical calibrations indicate that the Coyotean is a relatively long time interval of about 20 Ma, whereas most of the other faunachrons are much shorter, about 1–2 Ma long each. The Platbergian may also be relatively long, 14 Ma, although this is not certain. This suggests slow rates of terrestrial tetrapod faunal turnover during most of the Early Permian and late Middle to Late Permian, but more rapid rates of turnover during the latest Early and most of the Middle Permian, especially during the explosive initial diversification of therapsids.
A short report on preconditioned Anderson acceleration method

In this report, we present a versatile and efficient preconditioned Anderson acceleration (PAA) method for fixed-point iterations. The proposed framework offers flexibility in balancing convergence rates (linear, super-linear, or quadratic) and computational costs related to the Jacobian matrix. Our approach recovers various fixed-point iteration techniques, including Picard, Newton, and quasi-Newton iterations. The PAA method can be interpreted as employing Anderson acceleration (AA) as its own preconditioner or as an accelerator for quasi-Newton methods when their convergence is insufficient. Adaptable to a wide range of problems with differing degrees of nonlinearity and complexity, the method achieves improved convergence rates and robustness by incorporating suitable preconditioners. We test multiple preconditioning strategies on various problems and investigate a delayed update strategy for preconditioners to further reduce the computational costs.
The challenge of modeling pool–riffle morphologies in channels with different densities of large woody debris and boulders

Studies on pool morphologies include reports of over 80% or 90% of pools being associated with structural controls and large obstructions that include boulders, bedrock outcrops and large woody debris (LWD). A Monte Carlo simulation approach and developmental computer model was created to predict pool formation, spacing and the percentage length covered by pools, riffles, scour holes and runs based on input data that include channel slope, width, the number of small and large boulders, and the number of 10–30 cm, 30–60 cm and >60 cm pieces of wood. The statistical‐empirical model is founded on the idea that boulders, bedrock outcrops and large woody debris provide a physical framework that then controls local water‐surface slopes, velocity patterns and the locations of pools and riffles. The spacing values of individual types and sizes of obstructions are modeled as log‐normal distributions with separate distributions for each obstruction type. Pools are assigned different probabilities of development depending on the obstruction type. Pool and riffle lengths used to create the subsequent morphology follow their own slope‐dependent, log‐normal trends. A minimum distance develops between successive pools because of the backwater and turbulent conditions needed for pool formation. The total number and spacing of pools, riffles and scour holes thus reflects the number and locations of obstructions and characteristics of the pool–riffle couplet. The simulation model accurately captures the number of pools in the modeled data range at 65% of all the verification field sites, and 86% of the verification field sites with a more limited range of width and slope characteristics. Lower levels of prediction capabilities are associated with modeled numbers of scour holes and log jams. The model accurately mimics some statistical attributes of pool spacing, and future versions of the model could be developed to improve overall predictive capabilities. Copyright © 2011 John Wiley & Sons, Ltd.
Empirical Determination of the Time Constant, Heat Capacity, and Sensitivity of Earth's Climate System

Energy balance models permit determination of the equilibrium sensitivity of Earth's climate to radiative perturbations as the quotient of the relaxation time constant of the system and the pertinent global heat capacity. Here the time constant pertinent to changes in global mean surface temperature, GMST, is determined to be 5 ± 1 yr (all uncertainties are 1-sigma estimates) as obtained from analysis of autocorrelation of time series of that quantity over 1880-2004. The heat capacity pertinent to climate change on the decadal time scale, obtained as the regression slope of ocean heat content over 1960-2002 (Levitus et al., 2005) vs. GMST over the same period is 6 ± 1.5 W yr m K-, equivalent to the heat capacity of 64 m of ocean water for an ocean fractional area of 71%. Inclusion of other sources of heat uptake raises the effective heat capacity to 7.1 ± 1.8 W yr m K. The resultant equilibrium climate sensitivity is 0.70 ± 0.14 K/(W m) corresponding, for the forcing of doubled CO2 taken as 3.7 W m, to an equilibrium temperature increase of 2.6 ± 0.8 K. The short time constant for change of GMST implies that GMST is in near equilibrium with the applied forcing and hence that the net climate forcing over the twentieth century can be obtained from the observed temperature increase over this period 0.57 ± 0.08 K (Folland et al., 2001) as 0.81 ± 0.29 W m. For this net forcing considered the sum of radiative forcing by incremental greenhouse gases and other forcings and for the greenhouse gas forcing over this period taken as 2.2 ± 0.3 W m, the forcing from other sources over the twentieth century, thought to be due mainly to incremental tropospheric aerosols, is inferred to be – 1.4 ± 0.35 W m. The observed increase in GMST may thus be interpreted as a warming of + 1.54 ± 0.37 K due to incremental greenhouse gases offset by a cooling of 0.97 ± 0.30 K due mainly to incremental aerosols.
Do prompt positions really matter?

Prompt-based models have gathered a lot of attention from researchers due to their remarkable advancements in the fields of zero-shot and few-shot learning. Developing an effective prompt template plays a critical role. However, prior studies have mainly focused on prompt vocabulary selection or embedding initialization within a predefined template with the prompt position fixed. In this empirical study, we conduct the most comprehensive analysis to date of prompt position for diverse natural language process tasks. Our findings quantify the substantial impact prompt position has on model performance. We observe that the prompt position used in prior studies is often sub-optimal. These findings suggest prompt position optimisation as a valuable research direction to fill the gap in existing prompt engineering methodologies.
Source Scaling Relationships of Microearthquakes at Parkfield, CA, Determined Using the SAFOD Pilot Hole Seismic Array

We estimate the source parameters of 34 microearthquakes at Parkfield, CA, ranging in size from M -0.2 to M 2.1, by analyzing seismograms recorded by the 32-level, 3-component seismic array installed in the SAFOD Pilot Hole. We succeeded in obtaining stable spectral ratios by stacking the ratios calculated from the moving windows taken along the record following the direct waves. These spectral ratios were modeled to determine seismic moments and corner frequencies assuming an omega-squared model. Static stress drops and apparent stresses of microearthquakes at Parkfield display moment-independent scaling in agreement with scaling laws reported for moderate and large earthquakes. It is likely that the dynamics of microearthquakes at Parkfield is macroscopically similar to that of larger tectonic earthquakes.
The response of birds to the fire regimes of mulga woodlands in central Australia

"In this thesis I investigate the key assumptions of the fire mosaic hypothesis using a model system, the mulga woodland/mulga bird community of central Australia. Mulga woodland is an ideal model system for this question because it is structurally and floristically simple, yet supports a rich avifauna. I tested how avian diversity (variety and number) was influenced by 1) time-since-fire; 2) patch size; and 3) the boundary between burnt and unburnt mulga woodland (pyric edge). " - Abstract
Learning a Better Control Barrier Function Under Uncertain Dynamics

Using control barrier functions (CBFs) as safety filters provides a computationally inexpensive yet effective method for constructing controllers in safety-critical applications. However, using CBFs requires the construction of a valid CBF, which is well known to be a challenging task, and accurate system dynamics, which are often unavailable. This paper presents a learning-based approach to learn a valid CBF and the system dynamics starting from a conservative handcrafted CBF (HCBF) and the nominal system dynamics. We devise new loss functions that better suit the CBF refinement pipeline and are able to produce well-behaved CBFs with the usage of distance functions. By adopting an episodic learning approach, our proposed method is able to learn the system dynamics while not requiring additional interactions with the environment. Additionally, we provide a theoretical analysis of the quality of the learned system dynamics. We show that our proposed learning approach can effectively learn a valid CBF and an estimation of the actual system dynamics. The effectiveness of our proposed method is empirically demonstrated through simulation studies on three systems, a double integrator, a unicycle, and a two-link arm.
Measurements of the Columnar Electron Contents of the Ionosphere and Plasmasphere

Modulation phase and Faraday rotation observations at Boulder of the ATS 6 radio beacon transmissions have measured the total slant electron content (NT) to the satellite and the content up to an altitude of about 2000 km (NF). Monthly median hourly values of NT, the residual plasmaspheric content above 2000 km (NP), and a factor F related to the electron profile shape and the earth' magnetic field are reported for July 1974 to April 1975. The slab thickness τ was calculated by using Boulder ionograms and numerical mapping estimates of the geographic variations of foF2. The diurnal and seasonal variations of NT, NP, F, and τ are discussed in terms of analytical models for the electron density profile shape. Representative models for day and night profiles in the summer, equinoctial, and winter seasons are presented, and their reliability is discussed.
DeltaScore: Fine-Grained Story Evaluation with Perturbations

Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness. In this paper, we introduce DELTASCORE, a novel methodology that employs perturbation techniques for the evaluation of nuanced story aspects. Our central proposition posits that the extent to which a story excels in a specific aspect (e.g., fluency) correlates with the magnitude of its susceptibility to particular perturbations (e.g., the introduction of typos). Given this, we measure the quality of an aspect by calculating the likelihood difference between pre- and post-perturbation states using pre-trained language models. We compare DELTASCORE with existing metrics on storytelling datasets from two domains in five fine-grained story aspects: fluency, coherence, relatedness, logicality, and interestingness. DELTASCORE demonstrates remarkable performance, revealing a surprising finding that a specific perturbation proves highly effective in capturing multiple aspects.
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks

Foundation models have revolutionized the landscape of Deep Learning (DL), serving as a versatile platform which can be adapted to a wide range of downstream tasks. Despite their adaptability, applications of foundation models to downstream graph-based tasks have been limited, and there remains no convenient way to leverage large-scale non-graph pretrained models in graph-structured settings. In this work, we present a new framework which we term Foundation-Informed Message Passing (FIMP) to bridge the fields of foundational models and GNNs through a simple concept: constructing message-passing operators from pretrained foundation model weights. We show that this approach results in improved performance for graph-based tasks in a number of data domains, allowing graph neural networks to leverage the knowledge of foundation models.