Miracle-dz/UniFAR-FTUs huggingface dataset:
Velocity structure of the Long Valley caldera from the inversion of local earthquake P and S travel times

A high-resolution tomographic study of the Long Valley region began in 1989 with the installation of a special fan array of eight three-component borehole (80–160 m depths) receivers on the northwestern rim of the caldera to provide the data necessary for a joint inversion for hypocenters and the three-dimensional P and S wave velocity structure in and below the caldera. The experiment specifically targeted the subsurface location of the previously inferred magma chamber beneath the Resurgent Dome. Additional coverage was provided by existing U.S. Geological Survey and University of Nevada, Reno seismographic networks. We employed the progressive inversion scheme of Thurber (1983), with cubic spline interpolation after Michelini and McEvilly (1991). The final model is based on data from 280 well-distributed local earthquakes. Some 6900 arrival times were used, including about 700 S wave times from the three-component stations. The resulting velocity structure reflects the known geology, defining the low-velocity caldera fill to 2 km depth, contrasting markedly with the surrounding higher-velocity Sierran block and highland terrane. No isolated distinct low-velocity anomalies are revealed beneath the caldera floor, although a diffuse zone of reduced velocity persists to a depth of about 8 km. These lower velocities may be related to hydrothermal alteration and/or extensive fractures. The Vp/Vs structure contains significant lateral variation within and beneath the caldera to about 8 km depth, and these variations can be related to accepted models of the active geothermal system. The lack of a significant S wave velocity anomaly, along with the normal or low values of the Vp/Vs ratio, argues against the presence of a sizable and distinct magma body at shallow to midcrustal depth beneath Long Valley caldera, although a very low percentage of partial melt cannot be precluded.
Safe Deep Policy Adaptation

A critical goal of autonomy and artificial intelligence is enabling autonomous robots to rapidly adapt in dynamic and uncertain environments. Classic adaptive control and safe control provide stability and safety guarantees but are limited to specific system classes. In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and generalizability but presents safety and robustness challenges. We propose SafeDPA, a novel RL and control framework that simultaneously tackles the problems of policy adaptation and safe reinforcement learning. SafeDPA jointly learns adaptive policy and dynamics models in simulation, predicts environment configurations, and fine-tunes dynamics models with few-shot real-world data. A safety filter based on the Control Barrier Function (CBF) on top of the RL policy is introduced to ensure safety during real-world deployment. We provide theoretical safety guarantees of SafeDPA and show the robustness of SafeDPA against learning errors and extra perturbations. Comprehensive experiments on (1) classic control problems (Inverted Pendulum), (2) simulation benchmarks (Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate great superiority of SafeDPA in both safety and task performance, over state-of-the-art baselines. Particularly, SafeDPA demonstrates notable generalizability, achieving a 300% increase in safety rate compared to the baselines, under unseen disturbances in real-world experiments.
Credit Blockchain for Faster Transactions in P2P Energy Trading

P2P trading of energy can be a good alternative to incentivize distributed non-conventional energy production and meet the burgeoning energy demand. For efficient P2P trading, a free market for trading needs to be established while ensuring the information reliability, security, and privacy. Blockchain has been used to provide this framework, but it consumes very high energy and is slow. Further, until now, no blockchain model has considered the role of conventional electric utility companies in P2P trading. In this paper, we have introduced a credit blockchain that reduces energy consumption by employing a new mechanism to update transactions and increases speed by providing interest free loans to buyers. This model also integrates the electric utility companies within the P2P trading framework, thereby increasing members trading options. We have also discussed the pricing strategies for trading. All the above assertions have been verified through simulations, demonstrating that this model will promote P2P trading by providing enhanced security, speed, and greater trading options. The proposed model will also help trade energy at prices beneficial for both sellers and buyers.
A Multifaceted Look at Starlink Performance

In recent years, Low-Earth Orbit (LEO) mega-constellations have emerged as a promising network technology and have ushered in a new era for democratizing Internet access. The Starlink network from SpaceX stands out as the only consumer-facing LEO network with over 2M+ customers and more than 4000 operational satellites. In this paper, we conduct the first-of-its-kind extensive multi-faceted analysis of Starlink network performance leveraging several measurement sources. First, based on 19.2M crowdsourced M-Lab speed test measurements from 34 countries since 2021, we analyze Starlink global performance relative to terrestrial cellular networks. Second, we examine Starlink's ability to support real-time web-based latency and bandwidth-critical applications by analyzing the performance of (i) Zoom video conferencing, and (ii) Luna cloud gaming, comparing it to 5G and terrestrial fiber. Third, we orchestrate targeted measurements from Starlink-enabled RIPE Atlas probes to shed light on the last-mile Starlink access and other factors affecting its performance globally. Finally, we conduct controlled experiments from Starlink dishes in two countries and analyze the impact of globally synchronized"15-second reconfiguration intervals"of the links that cause substantial latency and throughput variations. Our unique analysis provides revealing insights on global Starlink functionality and paints the most comprehensive picture of the LEO network's operation to date.
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where quantization and LoRA fine-tuning are applied together on a pre-trained model. In such cases it is common to observe a consistent gap in the performance on downstream tasks between full fine-tuning and quantization plus LoRA fine-tuning approach. In response, we propose LoftQ (LoRA-Fine-Tuning-aware Quantization), a novel quantization framework that simultaneously quantizes an LLM and finds a proper low-rank initialization for LoRA fine-tuning. Such an initialization alleviates the discrepancy between the quantized and full-precision model and significantly improves generalization in downstream tasks. We evaluate our method on natural language understanding, question answering, summarization, and natural language generation tasks. Experiments show that our method is highly effective and outperforms existing quantization methods, especially in the challenging 2-bit and 2/4-bit mixed precision regimes. The code is available on https://github.com/yxli2123/LoftQ.
"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models

Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing tasks. However, these models have been shown to harbor inherent societal biases, or stereotypes, which can adversely affect their performance in their many downstream applications. In this paper, we introduce a novel, purely prompt-based approach to uncover hidden stereotypes within any arbitrary LLM. Our approach dynamically generates a knowledge representation of internal stereotypes, enabling the identification of biases encoded within the LLM's internal knowledge. By illuminating the biases present in LLMs and offering a systematic methodology for their analysis, our work contributes to advancing transparency and promoting fairness in natural language processing systems.
A physics-infused Immersed Boundary Method using online sequential Data Assimilation

A physics-infused strategy relying on the Ensemble Kalman Filter (EnKF) is here used to augment the accuracy of a continuous Immersed Boundary Method (IBM). The latter is a classical penalty method accounting for the presence of the immersed body via a volume source term which is included in the Navier-Stokes equations. The model coefficients of the penalization method, which are usually selected by the user, are optimized here using an EnKF data-driven strategy. The parametric inference is governed by the physical knowledge of local and global features of the flow, such as the no-slip condition and the shear stress at the wall. The C++ library CONES (Coupling OpenFOAM with Numerical EnvironmentS) developed by the team is used to perform an online investigation, coupling on-the-fly data from synthetic sensors with results from an ensemble of coarse-grained numerical simulations. The analysis is performed for a classical test case, namely the turbulent channel flow with $Re_\tau = 550$. The comparison of the results with a high-fidelity Direct Numerical Simulation (DNS) shows that the data-driven procedure exhibits remarkable accuracy despite the relatively low grid resolution of the ensemble members.
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval

Dense embedding-based retrieval is now the industry standard for semantic search and ranking problems, like obtaining relevant web documents for a given query. Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query. These two stages are disjoint; the learned embeddings might be ill-suited for the ANNS method and vice-versa, leading to suboptimal performance. In this work, we propose End-to-end Hierarchical Indexing -- EHI -- that jointly learns both the embeddings and the ANNS structure to optimize retrieval performance. EHI uses a standard dual encoder model for embedding queries and documents while learning an inverted file index (IVF) style tree structure for efficient ANNS. To ensure stable and efficient learning of discrete tree-based ANNS structure, EHI introduces the notion of dense path embedding that captures the position of a query/document in the tree. We demonstrate the effectiveness of EHI on several benchmarks, including de-facto industry standard MS MARCO (Dev set and TREC DL19) datasets. For example, with the same compute budget, EHI outperforms state-of-the-art (SOTA) in by 0.6% (MRR@10) on MS MARCO dev set and by 4.2% (nDCG@10) on TREC DL19 benchmarks.
Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis. Unsupervised contrastive learning has garnered significant interest in learning effective representations from time series data with limited labels. The prevalent approach in existing contrastive learning methods consists of two separate stages: pre-training the encoder on unlabeled datasets and fine-tuning the well-trained model on a small-scale labeled dataset. However, such two-stage approaches suffer from several shortcomings, such as the inability of unsupervised pre-training contrastive loss to directly affect downstream fine-tuning classifiers, and the lack of exploiting the classification loss which is guided by valuable ground truth. In this paper, we propose an end-to-end model called SLOTS (Semi-supervised Learning fOr Time clasSification). SLOTS receives semi-labeled datasets, comprising a large number of unlabeled samples and a small proportion of labeled samples, and maps them to an embedding space through an encoder. We calculate not only the unsupervised contrastive loss but also measure the supervised contrastive loss on the samples with ground truth. The learned embeddings are fed into a classifier, and the classification loss is calculated using the available true labels. The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier. We evaluate SLOTS by comparing it with ten state-of-the-art methods across five datasets. The results demonstrate that SLOTS is a simple yet effective framework. When compared to the two-stage framework, our end-to-end SLOTS utilizes the same input data, consumes a similar computational cost, but delivers significantly improved performance. We release code and datasets at https://anonymous.4open. science/r/SLOTS-242E.
UAV Routing for Enhancing the Performance of a Classifier-in-the-loop

Some human-machine systems are designed so that machines (robots) gather and deliver data to remotely located operators (humans) through an interface in order to aid them in classification. The performance of a human as a (binary) classifier-in-the-loop is characterized by probabilities of correctly classifying objects of type $T$ and $F$. These two probabilities depend on the dwell time, $d$, spent collecting information at a point of interest (POI or interchangeably, target). The information gain associated with collecting information at a target is then a function of dwell time $d$ and discounted by the revisit time, $R$, i.e., the duration between consecutive revisits to the same target. The objective of the problem of routing for classification is to optimally route the vehicles and determine the optimal dwell time at each target so as to maximize the total discounted information gain while visiting every target at least once. In this paper, we make a simplifying assumption that the information gain is discounted exponentially by the revisit time; this assumption enables one to decouple the problem of routing with the problem of determining optimal dwell time at each target for a single vehicle problem. For the multi-vehicle problem, we provide a fast heuristic to obtain the allocation of targets to each vehicle and the corresponding dwell time.
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks

This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggregation, and homomorphic encryption often risk privacy compromises. The central aim is to defend the network against potential privacy breaches while maintaining model robustness against adversarial manipulations. We show that incorporating distributed noise, grounded in the privacy guarantees in federated settings, enables the development of a adversarially robust model that also meets federated privacy standards. We conducted comprehensive evaluations across diverse attack scenarios, parameters, and use cases in cancer imaging, concentrating on pathology, meningioma, and glioma. The results reveal that the incorporation of distributed noise allows for the attainment of security levels comparable to those of conventional adversarial training while requiring fewer retraining samples to establish a robust model.
Nonspreading relativistic electron wavepacket in a strong laser field

A solution of the Dirac equation in a strong laser field presenting a nonspreading wave packet in the rest frame of the electron is derived. It consists of a generalization of the self-accelerating free electron wave packet [Kaminer et al. Nature Phys. 11, 261 (2015)] to the case with the background of a strong laser field. Built upon the notion of nonspreading for an extended relativistic wavepacket, the concept of Born rigidity for accelerated motion in relativity is the key ingredient of the solution. At its core, the solution comes from the connection between the self-accelerated free electron wave packet and the eigenstate of a Dirac electron in a constant and homogeneous gravitational field via the equivalence principle. The solution is an essential step towards the realization of the laser-driven relativistic collider [Meuren et al. PRL 114, 143201 (2015)], where the large spreading of a common Gaussian wave packet during the excursion in a strong laser field strongly limits the expectable yields.
In situ measurements of impact‐induced pressure waves in sandstone targets

In the present study we introduce an innovative method for the measurement of impact‐induced pressure waves within geological materials. Impact experiments on dry and water‐saturated sandstone targets were conducted at a velocity of 4600 m/s using 12 mm steel projectiles to investigate amplitudes, decay behavior, and speed of the waves propagating through the target material. For this purpose a special kind of piezoresistive sensor capable of recording transient stress pulses within solid brittle materials was developed and calibrated using a Split‐Hopkinson pressure bar. Experimental impact parameters (projectile size and speed) were kept constant and yielded reproducible signal curves in terms of rise time and peak amplitudes. Pressure amplitudes decreased by 3 orders of magnitude within the first 250 mm (i.e., 42 projectile radii). The attenuation for water‐saturated sandstone is higher compared to dry sandstone which is attributed to dissipation effects caused by relative motion between bulk material and interstitial water. The proportion of the impact energy radiated as seismic energy (seismic efficiency) is in the order of 10−3. The present study shows the feasibility of real‐time measurements of waves caused by hypervelocity impacts on geological materials. Experiments of this kind lead to a better understanding of the processes in the crater subsurface during a hypervelocity impact.
Attacks Meet Interpretability (AmI) Evaluation and Findings

To investigate the effectiveness of the model explanation in detecting adversarial examples, we reproduce the results of two papers, Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples and Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples. And then conduct experiments and case studies to identify the limitations of both works. We find that Attacks Meet Interpretability(AmI) is highly dependent on the selection of hyperparameters. Therefore, with a different hyperparameter choice, AmI is still able to detect Nicholas Carlini's attack. Finally, we propose recommendations for future work on the evaluation of defense techniques such as AmI.
Book Review: Does vegetation suit our models? Data and model assumptions and the assessment of species distribution in space

generally well written and clear. However, there are some rather obvious omissions. First, and most obviously, the eco side is only represented by plants and yet the linkages between lower plants, invertebrates and vertebrates is a major part of ecosystems and, surprisingly in some cases, theories that attempt to relate hydrology, plants and animals (such as the river continuum concept) are poorly covered. Another major omission is soil hydrology, which is variably covered in several chapters but never in adequate depth. A moot point is whether remote sensing should have been covered in a separate chapter. These omissions are made more obvious by the title and the questions it raises However, overall this is a valuable collection of contributions providing an overview of the interactions of vegetation with hydrological processes. It will be particularly useful for hydrologists and geomorphologists. All the chapters are written by authorities in the field and most contain both a revision of the traditional theory and some new research. The balance varies, and some contain recent experimental or case studies which extend the value of the chapters. Particularly valuable, or rather unusual, sections include the discussion of statistical downscaling techniques (Chap. 2), the effects of plants on channel flow (Chap. 7) and mathematical onanism (Chap. 9)!
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus on finding Nash Equilibrium or the optimal policy under uncertainty in one environment variable (i.e. action, state or reward). This is because a multi-agent system itself is highly complex and unstationary. However, in real-world situation uncertainty can occur in multiple environment variables simultaneously. This work is the first to formulate the generalised problem of robustness to multi-modal environment uncertainty in MARL. To this end, we propose a general robust training approach for multi-modal uncertainty based on curriculum learning techniques. We handle two distinct environmental uncertainty simultaneously and present extensive results across both cooperative and competitive MARL environments, demonstrating that our approach achieves state-of-the-art levels of robustness.
Simple method to generate magnetically charged ultrastatic traversable wormholes without exotic matter in Einstein-scalar-Gauss-Bonnet gravity

All the magnetically charged ultrastatic and spherically symmetric spacetime solutions in the framework of linear/nonlinear electrodynamics, with an arbitrary electromagnetic Lagrangian density $\mathcal{L}(\mathcal{F})$ depending only of the electromagnetic invariant $\mathcal{F}\! =\! F_{\alpha\beta}F^{\alpha\beta}\! /4$, minimally coupled to Einstein-scalar-Gauss-Bonnet gravity [EsGB-$\mathcal{L}(\mathcal{F})$], are found. We also show that a magnetically charged ultrastatic and spherically symmetric EsGB-$\mathcal{L}(\mathcal{F})$ solution with invariant $\mathcal{F}$ having a strict global maximum value $\mathcal{F}_{_{0}}$ in the entire domain of the solution, and such that $\mathcal{L}_{_{0}}=\mathcal{L}(\mathcal{F}_{_{0}})>0$, can be interpreted as an ultrastatic wormhole spacetime geometry with throat radius determined by the scalar charge and the quantity $\mathcal{L}_{_{0}}$. We provide some examples, including Maxwell's theory of electrodynamics (linear electrodynamics) $\mathcal{L}_{_{_{\mathrm{LED}}}} \! =\! \mathcal{F}$, producing the magnetic dual of the purely electric Ellis-Bronnikov EsGB Maxwell wormhole derived in [P. Ca\~nate, J. Sultana, D. Kazanas, Phys. Rev. D {\bf100}, 064007 (2019)]; and the nonlinear electrodynamics (NLED) models given by Born-Infeld $\mathcal{L}_{_{_{\mathrm{BI}}}} \! =\! -4\beta^{2} + 4\beta^{2} \sqrt{ 1 + \mathcal{F}\! /\! (2\beta^{2})~}$, and Euler-Heisenberg in the approximation of the weak-field limit $\mathcal{L}_{_{_{\mathrm{EH}}}} \! =\! \mathcal{L}_{_{_{\mathrm{LED}}}} + \gamma \mathcal{F}^{2}\! /2$. With those NLED models, two novel magnetically charged ultrastatic traversable wormholes (EsGB Born-Infeld and EsGB Euler-Heisenberg wormholes) are presented as exact solutions without exotic matter in EsGB-$\mathcal{L}(\mathcal{F})$ gravity.