Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifshitz transition enhanced triplet $p_z$-wave superconductivity in hydrogen doped KCr$_3$As$_3$

The recently synthesized air-insensitive hydrogen doped KCr 3 As 3 superconductor has aroused great research interests. This material has, for the ﬁrst time in the research area of the quasi-one-dimensional Cr-based superconductivity (SC), realized a tunability through charge doping, which will potentially signiﬁcantly push the development of this area. Here based on the band structure from ﬁrst-principle calculations, we construct a six-band tight-binding (TB) model equipped with multi-orbital Hubbard interactions, and adopt the random-phase-approximation approach to study the hydrogen-doping dependence of the pairing symmetry and superconducting T c . Under the rigid-band approximation, our pairing phase diagram is occupied by the triplet p z -wave pairing through out the hydrogen-doping regime x ∈ (0 . 4 , 1) in which SC has been experimentally detected. Remarkably, the x -dependence of T c shows a peak at the 3D-quasi-1D Lifshitz transition point, although the total density of state exhibits a dip there. The corresponding doping level is near the experimental estimation of the optimal doping level. A thorough investigation of the band structure reveals type-II van-Hove singularities (VHSs) in the γ band, which favor the formation of the triplet SC. It turns out that the γ - Fermi surface (FS) comprises two ﬂat quasi-1D FS sheets almost parallel to the k z = 0 plane and six almost perpendicular tube-like FS sheets, and the type-II VHSs just lies in the boundary between these two FS parts. Furthermore, the | k z | of the VH planes reaches the maximum near the Lifshitz-transition point, which pushes the T c of the p z -wave SC to the maximum. Our results appeal more experimental access into this intriguing superconductor.
Compact printed semi‐elliptical monopole antenna for super‐wideband applications

A compact semi‐elliptical patch antenna fed by a tapered coplanar waveguide is introduced for super‐wideband (SWB) applications. The simulated and experimental results demonstrate that the antenna possesses the desirable SWB characteristics, achieving a ratio bandwidth of 19.7:1 for VSWR ≤ 2, and exhibits a nearly omni‐directional radiation pattern, while its area is only about 0. 15λl × 0. 21λl, where λl is the wavelength of the lowest operating frequency. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2061–2063, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22644
Comment on"Observation of two-dimensional Anderson localisation of ultracold atoms"

Here I provide additional experimental information and criticise the analysis, modelling, interpretation and claims presented in the recent paper [1]. I argue that the authors have no clear experimental evidence of Anderson localisation (as they claim) and their numerical simulations are very far indeed from reproducing the experiment, as stated in their article. Furthermore, some effects that are ascribed to real physical mechanisms are revealed to be simply artefacts.
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

Automated hyperparameter optimization (HPO) has gained great popularity and is an important component of most automated machine learning frameworks. However, the process of designing HPO algorithms is still an unsystematic and manual process: new algorithms are often built on top of prior work, where limitations are identified and improvements are proposed. Even though this approach is guided by expert knowledge, it is still somewhat arbitrary. The process rarely allows for gaining a holistic understanding of which algorithmic components drive performance and carries the risk of overlooking good algorithmic design choices. We present a principled approach to automated benchmark-driven algorithm design applied to multifidelity HPO (MF-HPO). First, we formalize a rich space of MF-HPO candidates that includes, but is not limited to, common existing HPO algorithms and then present a configurable framework covering this space. To find the best candidate automatically and systematically, we follow a programming-by-optimization approach and search over the space of algorithm candidates via Bayesian optimization. We challenge whether the found design choices are necessary or could be replaced by more naive and simpler ones by performing an ablation analysis. We observe that using a relatively simple configuration (in some ways, simpler than established methods) performs very well as long as some critical configuration parameters are set to the right value.
Search for Optimal Systolic Arrays: A Comprehensive Automated Exploration Framework and Lessons Learned

Systolic arrays have been widely used for accelerating HPC and deep learning applications. There is a plethora of previous works on the performance tuning of systolic arrays, but usually based on a number of oversimplified assumptions (e.g., only considering divisors for loop tiling, pruning based on off-chip data communication) to reduce the design space. In this paper, we present a comprehensive design space exploration tool named Odyssey for systolic array optimization. Odyssey does not rely on artificial assumptions to limit the design space, and yet it is highly efficient and scalable with a hybrid optimization technique. For example, for a 1024x1024x1024 matrix multiplication, it finds designs that reach 90% of the optimal performance in 5 seconds with a single CPU thread. Moreover, using Odyssey, we unveil and quantify the suboptimality introduced by multiple commonly used oversimplifications in prior studies for systolic array design space exploration. For example, Odyssey results show that limiting to divisors for loop tiling leads to a 39% performance loss, and pruning based on off-chip data movement results in a 45% performance loss. We applied Odyssey to explore the architecture trade-offs for matrix multiplication and convolutional neural network, providing inspiration into possible optimizations for these two applications.
Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information

Overlapping speech diarization is always treated as a multi-label classification problem. In this paper, we reformulate this task as a single-label prediction problem by encoding the multi-speaker labels with power set. Specifically, we propose the speaker embedding-aware neural diarization (SEND) method, which predicts the power set encoded labels according to the similarities between speech features and given speaker embeddings. Our method is further extended and integrated with downstream tasks by utilizing the textual information, which has not been well studied in previous literature. The experimental results show that our method achieves lower diarization error rate than the target-speaker voice activity detection. When textual information is involved, the diarization errors can be further reduced. For the real meeting scenario, our method can achieve 34.11% relative improvement compared with the Bayesian hidden Markov model based clustering algorithm.
Efimovian states of three charged particles

When three particles in three dimensions interact with a short-range potential fine-tuned to an infinite scattering length, they form an infinite sequence of loosely bound states obeying discrete scale invariance known as Efimov states. Here we show that analogous states are formed by three charged particles carrying two equal charges and one opposite charge in one, two, and three dimensions without any fine-tuning. Our finding is based on the Born-Oppenheimer approximation, where an effective inverse-square attraction is induced as a consequence of the dipole-charge interaction between a hydrogenlike heavy-light atom and a far-separated heavy particle. Because the resulting Efimovian states emerge toward the second or higher dissociation threshold, they are to be realized as quasibound states and may be observed by exciting hydrogen molecular ions and trions in excitonic systems. We also consider the same system but with a logarithmic Coulomb potential relevant to quantum vortices in two-dimensional superfluids, where the Efimovian states are shown to emerge as genuine bound states toward the first dissociation threshold.
Tracing X‐ray and HI absorption in peaked spectrum sources

Present Address This is sample for present address text this is sample for present address text Recent studies have shown that both 21cm H I absorption and soft X-ray absorption serve as excellent tracers of the dense and dusty gas near the active nucleus of young radio galaxies, offering new insight into the physical nature of the circumnuclear medium. To date, a correlation between the column densities derived using these absorption processes has been observed within Compact Steep Spectrum (CSS) and Gigahertz-Peaked Spectrum (GPS) radio sources. While it is possible that this correlation exists within the broader radio population, many samples of radio galaxies make this difficult to test due to selection effects. This paper explores the possibility of a correlation in the broader radio population by analysing a historic sample of 168 radio sources compiled from the literature in such a way so as to minimise selection bias. From this historic sample we conclude that there is some evidence for a correlation between H I and soft X-ray absorption outside of peaked spectrum sources, but that the selection bias towards these sources makes further analysis difficult using current samples. We discuss this in the context of the SEAFOG project and how forthcoming data will change the landscape of future absorption studies.
Domain Adaptation of Networks for Camera Pose Estimation: Learning Camera Pose Estimation Without Pose Labels

One of the key criticisms of deep learning is that large amounts of expensive and difficult-to-acquire training data are required in order to train models with high performance and good generalization capabilities. Focusing on the task of monocular camera pose estimation via scene coordinate regression (SCR), we describe a novel method, Domain Adaptation of Networks for Camera pose Estimation (DANCE), which enables the training of models without access to any labels on the target task. DANCE requires unlabeled images (without known poses, ordering, or scene coordinate labels) and a 3D representation of the space (e.g., a scanned point cloud), both of which can be captured with minimal effort using off-the-shelf commodity hardware. DANCE renders labeled synthetic images from the 3D model, and bridges the inevitable domain gap between synthetic and real images by applying unsupervised image-level domain adaptation techniques (unpaired image-to-image translation). When tested on real images, the SCR model trained with DANCE achieved comparable performance to its fully supervised counterpart (in both cases using PnP-RANSAC for final pose estimation) at a fraction of the cost. Our code and dataset are available at https://github.com/JackLangerman/dance
High-resolution Near-infrared Spectroscopy of a Flare around the Ultracool Dwarf vB 10

We present high-resolution observations of a flaring event in the M8 dwarf vB 10 using the near-infrared Habitable-zone Planet Finder (HPF) spectrograph on the Hobby-Eberly Telescope. The high stability of HPF enables us to accurately subtract a vB 10 quiescent spectrum from the flare spectrum to isolate the flare contributions and study the changes in the relative energy of the Ca ii infrared triplet, several Paschen lines, the He λ10830 triplet lines, and to select iron and magnesium lines in HPF's bandpass. Our analysis reveals the presence of a red asymmetry in the He λ10830 triplet, which is similar to signatures of coronal rain in the Sun. Photometry of the flare derived from an acquisition camera before spectroscopic observations and the ability to extract spectra from up-the-ramp observations with the HPF infrared detector enable us to perform time-series analysis of part of the flare and provide coarse constraints on the energy and frequency of such flares. We compare this flare with historical observations of flares around vB 10 and other ultracool M dwarfs and attempt to place limits on flare-induced atmospheric mass loss for hypothetical planets around vB 10.
Fast and Informative Model Selection Using Learning Curve Cross-Validation

Common cross-validation (CV) methods like k-fold cross-validation or Monte Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing it on the remaining data. These techniques have two major drawbacks. First, they can be unnecessarily slow on large datasets. Second, beyond an estimation of the final performance, they give almost no insights into the learning process of the validated algorithm. In this article, we present a new approach for validation based on learning curves (LCCV). Instead of creating train-test splits with a large portion of training data, LCCV iteratively increases the number of instances used for training. In the context of model selection, it discards models that are unlikely to become competitive. In a series of experiments on 75 datasets, we could show that in over 90% of the cases using LCCV leads to the same performance as using 5/10-fold CV while substantially reducing the runtime (median runtime reductions of over 50%); the performance using LCCV never deviated from CV by more than 2.5%. We also compare it to a racing-based method and successive halving, a multi-armed bandit method. Additionally, it provides important insights, which for example allows assessing the benefits of acquiring more data.
DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use the raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to divide the point cloud into several segments and extract the features of the segments from both spatial distribution and shape differences. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods.
Infinite dimensionality of the post-processing order of measurements on a general state space

For a partially ordered set (S, ⪯), the order (monotone) dimension is the minimum cardinality of total orders (respectively, real-valued order monotone functions) on S that characterize the order ⪯. In this paper we consider an arbitrary generalized probabilistic theory and the set of finite-outcome measurements on it, which can be described by effect-valued measures, equipped with the classical post-processing orders. We prove that the order and order monotone dimensions of the post-processing order are (countably) infinite if the state space is not a singleton (and is separable in the norm topology). This result gives a negative answer to the open question for quantum measurements posed in (Guff et al 2021 J. Phys. A: Math. Theor. 54 225301). We also consider the quantum post-processing relation of channels with a fixed input quantum system described by a separable Hilbert space H and show that the order (monotone) dimension is countably infinite when dimH⩾2 .
Design of New Resonant Haloscopes in the Search for the Dark Matter Axion: A Review of the First Steps in the RADES Collaboration

With the increasing interest in dark matter axion detection through haloscopes, in which different international groups are currently involved, the RADES group was established in 2016 with the goal of developing very sensitive detection systems to be operated in dipole magnets. This review deals with the work developed by this collaboration during its first five years: from the first designs—based on the multi-cavity concept, aiming to increase the haloscope volume, and thereby improve sensitivity—to their evolution, data acquisition design, and finally, the first experimental run. Moreover, the envisaged work within RADES for both dipole and solenoid magnets in the short and medium term is also presented.
Demand Endogenization of Intermediate Products in Supply Chains through a System-Dynamics-based Modularization Concept

A huge variety of System Dynamics models have been developed to endogenize short-term demand variations in supply chains. The Beer Game, used as a standard System Dynamics introduction in management courses, is co-responsible for having made these models wide-spread and of high-quality. Surprisingly, models that deal with long-term demand development aren’t seen that often. Even in the Special Issue on Supply Chain Management (SCM) in the System Dynamics Review 2004, the editorially responsible team of authors summarizes that this issue plagues firms in innovation driven industries and is often ignored in conventional SCM research. In this paper a suggestion of a long-term demand endogenizing framework is provided, helping to close this gap in recent research. The framework is designed to deal with the diffusion processes of innovative intermediate products. Although most product transactions are intermediate product transactions, many diffusion models are apparently designed to deal with the diffusion processes of end products and not with intermediates as evidenced by the structural causality of these models. With this focus on intermediate products also new ground is claimed. Additionally, generic reusable models and guidelines how to apply the concept to a case under study, makes the aim of the paper, helping to optimize production capacity and inventory planning, practically implementable.
Semi‐empirical model for site effects on acceleration time histories at soft‐soil sites. Part 2: calibration

A previously developed simplified model of ground motion amplification is applied to the simulation of acceleration time histories at several soft‐soil sites in the Valley of Mexico, on the basis of the corresponding records on firm ground. The main objective is to assess the ability of the model to reproduce characteristics such as effective duration, frequency content and instantaneous intensity. The model is based on the identification of a number of parameters that characterize the complex firm‐ground to soft‐soil transfer function, and on the adjustment of these parameters in order to account for non‐linear soil behavior. Once the adjusted model parameters are introduced, the statistical properties of the simulated and the recorded ground motions agree reasonably well. For the sites and for the seismic events considered in this study, it is concluded that non‐linear soil behavior may have a significant effect on the amplification of ground motion. The non‐linear soil behavior significantly affects the effective ground motion duration for the components with the higher intensities, but it does not have any noticeable influence on the lengthening of the dominant ground period. Copyright © 2004 John Wiley & Sons, Ltd.
Multiport Sock Samplers: A Low‐Cost Technology for Effective Multilevel Ground Water Sampling

The importance of obtaining depth‐specific ground water samples is now well recognized among practitioners and scientists alike. Many methods and technologies are available for level discrete or depth‐specific ground water sampling in consolidated aquifers. All methods have their associated advantages and drawbacks, however. One common disadvantage is that they are expensive. A large number of point discrete ground water samples were required for a UK research project aimed at quantifying natural attenuation processes in ground water contaminated by a former coal carbonization plant. Based on experience from a previous project to develop novel level accurate sampling methodologies for use in existing boreholes, the Ground Water Protection and Restoration Research Unit (GWPRRU) produced and tested a low‐cost design multiport sock sampler for ground water monitoring. The sock sampler design allowed the recovery of multiple depth‐specific ground water samples from depths of 150 feel (45 m) from individual boreholes in the sandstone aquifer at the field site. Because of their use of inexpensive materials, simple design, installation and use that does not require gravel packs, packers, or grouting, sock samplers were found to be the most cost effective, convenient, and reliable method of obtaining multiple depth‐specific ground water samples at the project field site.
Neurocognition and its association with adverse childhood experiences and familial risk of mental illness

Background: Neurocognitive deficits are considered an endophenotype for several psychiatric disorders, typically studied in unaffected first-degree relatives (FDRs). Environmental factors such as adverse childhood experiences (ACEs) may also affect neurocognition. This study examines the effect of ACEs on neurocognitive performance in FDRs of patients with severe mental illness in order to determine whether familial risk has a moderating effect on the relationship between ACEs and neurocognition. Methods: The sample consists of a total of 512 individuals composed of unaffected FDRs from multiplex families with severe mental illnesses (schizophrenia, bipolar disorder, obsessive-compulsive disorder or alcohol use disorder) and healthy controls (with no familial risk). Neurocognitive tests included processing speed (Color Trails), new learning (Auditory Verbal Learning Test), working memory (N-Back), and Theory of Mind (SOCRATIS). ACEs were measured using the WHO ACE-International Questionnaire (ACE-IQ). Regression models adjusted for age, gender and education were done to predict each neurocognitive domain by the effect of familial risk, ACE-IQ Total Score and the interaction (familial risk x ACE-IQ Total score). Results: When all FDRs were examined as a group, the main effect of familial risk predicted poor performance in all domains of neurocognition (p <0.01), and the ACEs x familial risk interaction had a significant negative association with global neurocognition, processing speed & working memory. This interaction effect was driven predominantly by the familial risk of AUD. In FDRs of schizophrenia & bipolar disorder, only the main effects of familial risk were significant (working memory, theory of mind & global neurocognition), with no impact of ACEs or its interaction in both these sub-groups. Conclusions: The impact of childhood adversity on neurocognition is moderated by familial risk of psychiatric disorders. Genetic or familial vulnerability may play a greater role in disorders such as schizophrenia and bipolar disorder, while the interaction between ACEs and family history may be more relevant in the case of disorders with greater environmental risk, such as substance use.
Holomorphic contact via Pascal map and operator theory

We give an intrinsic definition of point-wise contact between holomorphic Hermitian vector bundles by introducing a canonical bundle map, called the Pascal map, on their jet bundles. Combined with a holomorphic gluing condition, point-wise contact is further extended to contact along an analytic hyper-surface and we characterize it by the curvatures and their covariant derivatives on the vector bundles. The Pascal map has a realization in operator theory where our study applies to unitary equivalence of shift operators on function spaces.