Miracle-dz/UniFAR-FTUs huggingface dataset:
Pressure Interference Tests in a Fractured Geothermal Reservoir: Oguni Geothermal Field, Northern Kyushu, Japan

The subsurface stratigraphy in the Oguni Geothermal Field of the northwestern Hohi geothermal region, Japan, consists of a sequence of indurated sediments and volcanics overlying a granitic basement. The Hohi formation and the upper part of the Shishimuta formation constitute the principal geothermal aquifers. The feedzone pressures indicate that the northwestern Hohi region consists of two pressure zones: i.e., a high-pressure zone in the southern part of the Oguni Geothermal Field, and a low-pressure zone in the central and northern parts of the northwestern Hohi area. To delineate the permeability structure for the Oguni Geothermal Field, numerous pressure transient tests were performed. Analyses of pressure interference data indicate that the low-pressure zone has a transmissivity of 100-250 x 10 -2 m 3 . In contrast to the high transmissivity of the low-pressure zone, the pressure interference data imply that the high-pressure zone has only a modest transmissivity of 8-15 x 10 -12 m 3 . The pressure interference data are consistent with the presence of one or more no-flux boundaries between the low- and high-pressure zones. In addition, a significant degree of permeability heterogeneity is indicated within both the low- and high-pressure parts of the northwestern Hohi geothermal region, reflecting the fractured nature of the geothermal resource.
Language Models As Semantic Indexers

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text encoders and then deriving IDs based on the embeddings. However, each step introduces potential information loss and there is usually an inherent mismatch between the distribution of embeddings within the latent space produced by text encoders and the anticipated distribution required for semantic indexing. Nevertheless, it is non-trivial to design a method that can learn the document's semantic representations and its hierarchical structure simultaneously, given that semantic IDs are discrete and sequentially structured, and the semantic supervision is deficient. In this paper, we introduce LMINDEXER, a self-supervised framework to learn semantic IDs with a generative language model. We tackle the challenge of sequential discrete ID by introducing a semantic indexer capable of generating neural sequential discrete representations with progressive training and contrastive learning. In response to the semantic supervision deficiency, we propose to train the model with a self-supervised document reconstruction objective. The learned semantic indexer can facilitate various downstream tasks, such as recommendation and retrieval. We conduct experiments on three tasks including recommendation, product search, and document retrieval on five datasets from various domains, where LMINDEXER outperforms competitive baselines significantly and consistently.
Ocean winds from RADARSAT-1 ScanSAR

This paper discusses an algorithm designed to retrieve high-resolution wind fields from scanning synthetic aperture radar (ScanSAR) data acquired on board the Canadian satellite RADARSAT-1. The ScanSAR operates at C-band with horizontal polarization. The wind directions are extracted from wind-induced streaks, e.g., from atmospheric boundary layer rolls or wind shadowing, which are approximately in line with the mean wind direction near the ocean surface. The wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated ScanSAR images, together with the local wind direction retrieved from the image. Therefore the semi-empirical C-band model CMOD4, which describes the dependency of the NRCS on wind and image geometry, is used. The CMOD4 was originally developed for the scatterometer of the European remote sensing satellites ERS-l and ERS-2 operating at C-band with vertical polarization. Consequently, the CMOD4 required modification for horizontal polarization, which is performed by considering the polarization ratio. To demonstrate the applicability of the algorithm, wind fields were computed from 20 RADARSAT-1 ScanSAR wide-swath images and compared to co-located results from the Danish high-resolution limited-area model (HIRLAM). In addition, the error sources in ScanSAR wind retrieval are discussed and sensitivity studies were carried out to estimate wind speed errors due to uncertainties in the NRCS, wind direction, and incidence angles.
Moduli stacks of Higgs bundles on stable curves

In this article, we construct a flat degeneration of the derived moduli stack of Higgs bundles on smooth curves using the stack of expanded degenerations of Jun Li. We show that there is an intrinsic relative log-symplectic form on the degeneration and we compare it with the one constructed by the second author. We show that the Hitchin map of the degeneration we construct has complete fibers. Furthermore, we show that the Hitchin map is flat and that a suitable open subset of the smooth locus of the reduced nilpotent cone is Lagrangian. We also extend the construction of the moduli of Higgs bundles along with the relative log-symplectic form over the universal moduli stack of stable curves.
Distributed Estimation for Large-Scale Cox Regression with Poisson Subsampling

To ensure privacy protection and alleviate computational burden, we propose a Poisson-subsampling based distributed estimation procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines.
Integration or fragmentation? A closer look at euro area financial markets

This paper examines the degree of integration at euro area financial markets. To that end, we estimate overall and country-specific integration indices based on a panel vector-autoregression with factor stochastic volatility. Our results indicate a more heterogeneous bond market compared to the market for lending rates. At both markets, the global financial crisis and the sovereign debt crisis led to a severe decline in financial integration, which fully recovered since then. We furthermore identify countries that deviate from their peers either by responding differently to crisis events or by taking on different roles in the spillover network. The latter analysis reveals two set of countries, namely a main body of countries that receives and transmits spillovers and a second, smaller group of spillover absorbing economies. Finally, we demonstrate by estimating an augmented Taylor rule that euro area short-term interest rates are positively linked to the level of integration on the bond market.
Three Dimensional Natures of Massive Star Envelopes

In this paper, we review our current understanding of the outer envelope structures of massive stars based on three-dimensional (3D) radiation hydrodynamic simulations. We briefly summarize the fundamental issues in constructing hydrostatic one-dimensional (1D) stellar evolution models when stellar luminosity approaches the Eddington value. Radiation hydrodynamic simulations in 3D covering the mass range from 13M⊙ to 80M⊙ always find a dynamic envelope structure with the time-averaged radial profiles matching 1D models with an adjusted mixing-length parameter when convection is subsonic. Supersonic turbulence and episodic mass loss are generally found in 3D models when stellar luminosity is super-Eddington locally due to the opacity peaks and convection being inefficient. Turbulent pressure plays an important role in supporting the outer envelope, which makes the photosphere more extended than predictions from 1D models. Massive star lightcurves are always found to vary with a characteristic timescale consistent with the thermal time scale at the location of the iron opacity peak. The amplitude of the variability as well as the power spectrum can explain the commonly observed stochastic low-frequency variability of mass stars observed by TESS over a wide range of parameters in an HR diagram. The 3D simulations can also explain the ubiquitous macro-turbulence that is needed for spectroscopic fitting in massive stars. Implications of 3D simulations for improving 1D stellar evolution models are also discussed.
Computational Intelligence in Design and Manufacturing

From the Publisher: The design of products, processes, and systems is the focus of successful time-based competition. This design process which focuses on problem-solving and continued innovation brings together a multitude of variables, including customer needs, scheduling, modeling, and process analysis, which must be implemented concurrently to be successful. This new book is one of the first to provide an in-depth understanding of this entire spectrum and provides a unique view of product design as a process.
Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN

A morph is a combination of two separate facial images and contains the identity information of two different people. When used in an identity document, both people can be authenticated by a biometric face recognition (FR) system. Morphs can be generated using either a landmark-based approach or approaches based on deep learning, such as generative adversarial networks (GANs). In a recent paper, we introduced a worst-case upper bound on how challenging morphing attacks can be for an FR system. The closer morphs are to this upper bound, the bigger the challenge they pose to FR. We introduced an approach with which it was possible to generate morphs that approximate this upper bound for a known FR system (white box) but not for unknown (black box) FR systems. In this paper, we introduce a morph generation method that can approximate worst-case morphs even when the FR system is not known. A key contribution is that we include the goal of generating difficult morphs during training. Our method is based on adversarially learned inference (ALI) and uses concepts from Wasserstein GANs trained with gradient penalty, which were introduced to stabilise the training of GANs. We include these concepts to achieve a similar improvement in training stability and call the resulting method Wasserstein ALI (WALI). We finetune WALI using loss functions designed specifically to improve the ability to manipulate identity information in facial images and show how it can generate morphs that are more challenging for FR systems than landmark- or GAN-based morphs. We also show how our findings can be used to improve MIPGAN, an existing StyleGAN-based morph generator.
One-loop Double Copy Relation in String Theory

We discuss relations between closed and open string amplitudes at one-loop. While at tree-level these relations are known as Kawai-Lewellen-Tye (KLT) and/or double copy relations, here we investigate how such relations are manifested at one-loop. While there exist examples of one-loop closed string amplitudes that can strikingly be written as sum over squares of one-loop open string amplitudes, generically the one-loop closed string amplitudes assume a form reminiscent from the one-loop doubly copy structure of gravitational amplitudes involving a loop momentum. This double copy structure represents the one-loop generalization of the KLT relations.
Jointly Optimized Global-Local Visual Localization of UAVs

Navigation and localization of UAVs present a challenge when global navigation satellite systems (GNSS) are disrupted and unreliable. Traditional techniques, such as simultaneous localization and mapping (SLAM) and visual odometry (VO), exhibit certain limitations in furnishing absolute coordinates and mitigating error accumulation. Existing visual localization methods achieve autonomous visual localization without error accumulation by matching with ortho satellite images. However, doing so cannot guarantee real-time performance due to the complex matching process. To address these challenges, we propose a novel Global-Local Visual Localization (GLVL) network. Our GLVL network is a two-stage visual localization approach, combining a large-scale retrieval module that finds similar regions with the UAV flight scene, and a fine-grained matching module that localizes the precise UAV coordinate, enabling real-time and precise localization. The training process is jointly optimized in an end-to-end manner to further enhance the model capability. Experiments on six UAV flight scenes encompassing both texture-rich and texture-sparse regions demonstrate the ability of our model to achieve the real-time precise localization requirements of UAVs. Particularly, our method achieves a localization error of only 2.39 meters in 0.48 seconds in a village scene with sparse texture features.
PolyTask: Learning Unified Policies through Behavior Distillation

Unified models capable of solving a wide variety of tasks have gained traction in vision and NLP due to their ability to share regularities and structures across tasks, which improves individual task performance and reduces computational footprint. However, the impact of such models remains limited in embodied learning problems, which present unique challenges due to interactivity, sample inefficiency, and sequential task presentation. In this work, we present PolyTask, a novel method for learning a single unified model that can solve various embodied tasks through a 'learn then distill' mechanism. In the 'learn' step, PolyTask leverages a few demonstrations for each task to train task-specific policies. Then, in the 'distill' step, task-specific policies are distilled into a single policy using a new distillation method called Behavior Distillation. Given a unified policy, individual task behavior can be extracted through conditioning variables. PolyTask is designed to be conceptually simple while being able to leverage well-established algorithms in RL to enable interactivity, a handful of expert demonstrations to allow for sample efficiency, and preventing interactive access to tasks during distillation to enable lifelong learning. Experiments across three simulated environment suites and a real-robot suite show that PolyTask outperforms prior state-of-the-art approaches in multi-task and lifelong learning settings by significant margins.
Hyperparameter Adaptive Search for Surrogate Optimization: a Self-Adjusting Approach

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.
Fast Search Method for Large Polarization Kernels

A novel search method for large polarization kernels is proposed. The algorithm produces a kernel with given partial distances by employing the depth-first search combined with the computation of coset leaders weight tables and sufficient conditions of code non-equivalence. Using the proposed method, we improved all existing lower bounds on the maximum error exponent for kernels of size from 17 to 29. We also obtained kernels which admit low complexity processing by the recently proposed recursive trellis algorithm. Numerical results demonstrate the advantage of polar codes with the obtained kernels compared with shortened polar codes and polar codes with small kernels.
Measurements of energy dissipation adjacent to an island

Measurements of velocity shear microstructure were taken March 3–7, 1975, adjacent to the island of Santa Maria in the Azores. From the data it is possible to estimate the energy dissipation over 5-m vertical intervals. Measurements within 5 km of the island show regions of high dissipation (up to 10−3 W/m3) as much as 45 m thick at the base of the mixed layer. The maximum dissipations 80 km from the island are not in the seasonal thermocline but rather at depths exceeding 250 m, where the values averaged over 5 m reach 10−4 W/m3 or more. The vertical thickness scales of the dissipative patches are of the order of 10 m. Statistical analysis shows that 80% of the estimates of ∈ are 10−5 W/m3 or less; the other 20% of the water column is responsible for 85% of the total energy dissipation.
Discerning Temporal Difference Learning

Temporal difference learning (TD) is a foundational concept in reinforcement learning (RL), aimed at efficiently assessing a policy's value function. TD(λ), a potent variant, incorporates a memory trace to distribute the prediction error into the historical context. However, this approach often neglects the significance of historical states and the relative importance of propagating the TD error, influenced by challenges such as visitation imbalance or outcome noise. To address this, we propose a novel TD algorithm named discerning TD learning (DTD), which allows flexible emphasis functions—predetermined or adapted during training—to allocate efforts effectively across states. We establish the convergence properties of our method within a specific class of emphasis functions and showcase its promising potential for adaptation to deep RL contexts. Empirical results underscore that employing a judicious emphasis function not only improves value estimation but also expedites learning across diverse scenarios.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation under challenging scenes

Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically designed to train and test on clear and pristine datasets, overlooking the impact of various adverse conditions prevalent in real-world scenarios. As a result, it is commonly observed that most selfsupervised monocular depth estimation methods struggle to perform adequately under challenging conditions. To address this issue, we present EC-Depth, a novel self-supervised twostage training framework to achieve a robust depth estimation, starting from the foundation of depth prediction consistency under different perturbations. Leveraging the proposed perturbation-invariant depth consistency constraint module and the consistency-based pseudo-label selection module, our model attains accurate and consistent depth predictions in both standard and challenging scenarios. Extensive experiments substantiate the effectiveness of the proposed method. Moreover, our method surpasses existing state-of-the-art methods on KITTI, KITTI-C and DrivingStereo benchmarks, demonstrating its potential for enhancing the reliability of self-supervised monocular depth estimation models in real-world applications.
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms

Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of solutions to approximate the Pareto front, leaving a decision maker (DM) with the task of selecting a preferred solution. However, this process can be inefficient and time-consuming, especially when there are many objectives or the DM has subjective preferences. To address this issue, interactive MOEAs (iMOEAs) combine decision making into the optimization process, i.e., update the population with the help of the DM. In contrast to their wide applications, there has existed only two pieces of theoretical works on iMOEAs, which only considered interactive variants of the two simple single-objective algorithms, RLS and (1+1)-EA. This paper provides the first running time analysis (the essential theoretical aspect of EAs) for practical iMOEAs. Specifically, we prove that the expected running time of the well-developed interactive NSGA-II (called R-NSGA-II) for solving the OneMinMax, OneJumpZeroJump problems are all asymptotically faster than the traditional NSGA-II. Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II. These results provide theoretical justification for the effectiveness of iMOEAs while identifying situations where they may fail. Experiments are also conducted to validate the theoretical results.
Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices

Voice conversion aims to convert source speech into a target voice using recordings of the target speaker as a reference. Newer models are producing increasingly realistic output. But what happens when models are fed with non-standard data, such as speech from a user with a speech impairment? We investigate how a recent voice conversion model performs on non-standard downstream voice conversion tasks. We use a simple but robust approach called k-nearest neighbors voice conversion (kNN-VC). We look at four non-standard applications: stuttered voice conversion, cross-lingual voice conversion, musical instrument conversion, and text-to-voice conversion. The latter involves converting to a target voice specified through a text description, e.g."a young man with a high-pitched voice". Compared to an established baseline, we find that kNN-VC retains high performance in stuttered and cross-lingual voice conversion. Results are more mixed for the musical instrument and text-to-voice conversion tasks. E.g., kNN-VC works well on some instruments like drums but not on others. Nevertheless, this shows that voice conversion models - and kNN-VC in particular - are increasingly applicable in a range of non-standard downstream tasks. But there are still limitations when samples are very far from the training distribution. Code, samples, trained models: https://rf5.github. io/sacair2023-knnvc-demo/.