Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehending Semantic Types in JSON Data with Graph Neural Networks

Semantic types are a more powerful and detailed way of describing data than atomic types such as strings or integers. They establish connections between columns and concepts from the real world, providing more nuanced and fine-grained information that can be useful for tasks such as automated data cleaning, schema matching, and data discovery. Existing deep learning models trained on large text corpora have been successful at performing single-column semantic type prediction for relational data. However, in this work, we propose an extension of the semantic type prediction problem to JSON data, labeling the types based on JSON Paths. Similar to columns in relational data, JSON Path is a query language that enables the navigation of complex JSON data structures by specifying the location and content of the elements. We use a graph neural network to comprehend the structural information within collections of JSON documents. Our model outperforms a state-of-the-art existing model in several cases. These results demonstrate the ability of our model to understand complex JSON data and its potential usage for JSON-related data processing tasks.
Slice profile effects in MR perfusion imaging using pulsed arterial spin labelling

Flow-sensitive experiments (FAIR) have been performed on a tube-flow phantom in order to validate quantitative perfusion measurements on humans. A straight-forward correspondence between perfusion and bulk-flow is found. It is shown that the flow phantom model only holds when the slice profiles of the involved RF pulses are taken into account. A small flow-independent off-set may be present in the data. The off-set is explained by the model. Based on the correspondence between the phantom and the in vivo models, it is shown that the lowest flow values that could be measured in the phantom correspond to perfusion values lower than the cortical perfusion in the brain. Thus, the experimental accuracy and the computational methods for quantitative perfusion measurements in vivo can be validated by a tube-flow phantom. © 2000 Elsevier Science Inc. All rights reserved.
Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion

As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally cheap, 2D Convolutional Neural Networks can only extract spatial information. In contrast, 3D CNNs can extract three-dimensional features, but they have higher computational costs and latency, which is a limitation for clinical practice that requires fast and efficient models. Inspired by the field of video action recognition we propose a new 2D-based model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three orthogonal planes of a volume and imposing a weights-sharing mechanism. The third dimension, which is neglected by the 2D convolution, is reincorporated by shifting a portion of the feature maps along the slices' axis. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in performance with state-of-the-art architectures.
Calculation of normal modes of the closed waveguides in general vector case

The article is devoted to the calculation of normal modes of the closed waveguides with an arbitrary filling ε, μ in the system of computer algebra Sage. Maxwell equations in the cylinder are reduced to the system of two bounded Helmholtz equations, the notion of weak solution of this system is given and then this system is investigated as a system of ordinary differential equations. The normal modes of this system are an eigenvectors of a matrix pencil. We suggest to calculate the matrix elements approximately and to truncate the matrix by usual way but further to solve the truncated eigenvalue problem exactly in the field of algebraic numbers. This approach allows to keep the symmetry of the initial problem and in particular the multiplicity of the eigenvalues. In the work would be presented some results of calculations.
Automatic Lobe Segmentation Using Attentive Cross Entropy and End-to-End Fissure Generation

The automatic lung lobe segmentation algorithm is of great significance for the diagnosis and treatment of lung diseases, however, which has great challenges due to the incompleteness of pulmonary fissures in lung CT images and the large variability of pathological features. Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function. In addition, we introduce an end-to-end pulmonary fissure generation method in the auxiliary pulmonary fissure segmentation task, without any additional network branch. Finally, we propose a registration-based loss function to alleviate the convergence difficulty of the Dice loss supervised pulmonary fissure segmentation task. We achieve 97.83% and 94.75% dice scores on our private dataset STLB and public LUNA16 dataset respectively.
An isomorphism of idempotent monads

We consider isomorphism between the idempotent measure monad based on the maximum and the addition operations and the idempotent measure monad based on the maximum and the multiplication operations. A one of the consequences of this result is the construction of a fuzzy integral based on the maximum and the addition operation. We also investigate convexities related to these monads.
ASSESSMENT OF INSULIN RESISTANCE INDEX (HOMA-IR) AND LIPID PROFILE AMONG SUDANESE WOMEN WITH GALLSTONES.

The aim of this study was to assess insulin resistance index (HOMA-IR) and lipid profile level in Sudanese gallstones (GSs) patients and their association with study variables. In case control study 80 clinically diagnosed GSs patients were recruited, and 80 apparently health age matched women as control group. Fasting insulin, glucose and lipid profile (TC, TG, HDL-C and LDL-C) were measured using TOSOH-311 ® and Mindray-380 ® analyzers. IR-index was calculated using HOMA-IR formula. and level were significantly increased Sudanese than and 0.000 respectively. Significant positive associations between LDL-C and HOMA-IR were observed p =0.001, r=437 and p =0.001, r=0.419 respectively. BMI also showed positive association with TC, TG and LDL-C. In conclusion, Sudanese females with GSs had higher HOMA-IR, TC, TG and LDL-C. Obesity may be the first risk factor of GS in Sudanese women. Thus could lead to GSs pathogenesis especially in postmenopausal.
A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation

In this paper, we strive to answer the question ‘how to collaboratively learn convolutional neural network (CNN)-based and vision transformer (ViT)-based models by selecting and exchanging the reliable knowledge between them for semantic segmentation? ’ Accordingly, we propose an online knowledge distillation (KD) framework that can simultaneously learn compact yet effective CNN-based and ViT-based models with two key technical breakthroughs to take full advantage of CNNs and ViT while compensating their limitations. Firstly, we propose heterogeneous feature distillation (HFD) to improve students’ consistency in low-layer feature space by mimicking heterogeneous features between CNNs and ViT. Secondly, to facilitate the two students to learn reliable knowledge from each other, we propose bidirectional selective distillation (BSD) that can dynamically transfer selective knowledge. This is achieved by 1) region-wise BSD determining the directions of knowledge transferred between the corresponding regions in the feature space and 2) pixel-wise BSD discerning which of the prediction knowledge to be transferred in the logit space. Extensive experiments on three benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art online distillation methods by a large margin, and shows its efficacy in learning collaboratively between ViT-based and CNN-based models.
Practical Low-density Codes for PB-ToF Sensing

An indirect Pulse-based Time-of-Flight camera can be modelled as a linear sensing system in which the target's depth is recovered from few measurements through a sensing matrix formed by a set of demodulation functions. Each demodulation function is the result of the convolution of a (0,1)-binary code and a cross-correlation function which models the entire modulation-demodulation process. In this paper, we present a practical scheme for the construction of the sensing matrix which relies on the optimization of the coherence, and is based on low-density codes. We demonstrate that our methodology eliminates the intrinsic variability of random and pseudo-random approaches, and allows for the recovery of the target's depth in a grid much finer than the number of distinct elements in the binary codes.
Applying Superfluid Helium to Light Dark Matter Searches: Demonstration of the HeRALD Detector Concept

The SPICE/HeRALD collaboration is performing R&D to enable studies of sub-GeV dark matter models using a variety of target materials. Here we report our recent progress on instrumenting a superfluid $^4$He target mass with a transition-edge sensor based calorimeter to detect both atomic signals (e.g. scintillation) and $^4$He quasiparticle (phonon and roton) excitations. The sensitivity of HeRALD to the critical"quantum evaporation"signal from $^4$He quasiparticles requires us to block the superfluid film flow to the calorimeter. We have developed a heat-free film-blocking method employing an unoxidized Cs film, which we implemented in a prototype"HeRALD v0.1"detector of $\sim$10~g target mass. This article reports initial studies of the atomic and quasiparticle signal channels. A key result of this work is the measurement of the quantum evaporation channel's gain of $0.15 \pm 0.012$, which will enable $^4$He-based dark matter experiments in the near term. With this gain the HeRALD detector reported here has an energy threshold of 145~eV at 5 sigma, which would be sensitive to dark matter masses down to 220~MeV/c$^2$.
The Firs Room-Temperature Ambient-Pressure Superconductor

For the first time in the world, we succeeded in synthesizing the room-temperature superconductor (Tc above 400 K, 127 oC) working at ambient pressure with a modified lead-apatite (LK-99) structure. The superconductivity of LK-99 is proved with the Critical temperature (Tc), Zero-resistivity, Critical current (Ic), Critical magnetic field (Hc), and the Meissner effect. The superconductivity of LK-99 originates from minute structural distortion by a slight volume shrinkage (0.48 %), not by external factors such as temperature and pressure. The shrinkage is caused by Cu2+ substitution of Pb2+(2) ions in the insulating network of Pb(2)-phosphate and it generates the stress. It concurrently transfers to Pb(1) of the cylindrical column resulting in distortion of the cylindrical column interface, which creates superconducting quantum wells (SQWs) in the interface. The heat capacity results indicated that the new model is suitable for explaining the superconductivity of LK-99. The unique structure of LK-99 that allows the minute distorted structure to be maintained in the interfaces is the most important factor that LK-99 maintains and exhibits superconductivity at room temperatures and ambient pressure.
Elastic Representation and Generative Shape Models for Tree-like 3D Objects

—How can one analyze detailed 3D biological objects, such as neuronal and botanical trees, that exhibit complex geometrical and topological variation? In this paper, we develop a novel mathematical framework for representing, comparing, and computing geodesic deformations between the shapes of such tree-like 3D objects. A hierarchical organization of subtrees characterizes these objects – each subtree has a main branch with some side branches attached – and one needs to match these structures across objects for meaningful comparisons. We propose a novel representation that extends the Square-Root Velocity Function (SRVF), initially developed for Euclidean curves, to tree-shaped 3D objects. We then deﬁne a new metric that quantiﬁes the bending, stretching, and branch sliding needed to deform one tree-shaped object into the other. Compared to the current metrics such as the Quotient Euclidean Distance (QED) and the Tree Edit Distance (TED), the proposed representation and metric capture the full elasticity of the branches ( i.e., bending and stretching) as well as the topological variations ( i.e., branch death/birth and sliding). It completely avoids the shrinkage that results from the edge collapse and node split operations of the QED and TED metrics. We demonstrate the utility of this framework in comparing, matching, and computing geodesics between biological objects such as neuronal and botanical trees. We also demonstrate its application to various shape analysis tasks such as (i) symmetry analysis and symmetrization of tree-shaped 3D objects, (ii) computing summary statistics (means and modes of variations) of populations of tree-shaped 3D objects, (iii) ﬁtting parametric probability distributions to such populations, and (iv) ﬁnally synthesizing novel tree-shaped 3D objects through random sampling from estimated probability distributions.
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents

Task-oriented dialogue (TOD) models have made significant progress in recent years. However, previous studies primarily focus on datasets written by annotators, which has resulted in a gap between academic research and real-world spoken conversation scenarios. While several small-scale spoken TOD datasets are proposed to address robustness issues such as ASR errors, they ignore the unique challenges in spoken conversation. To tackle the limitations, we introduce SpokenWOZ, a large-scale speech-text dataset for spoken TOD, containing 8 domains, 203k turns, 5.7k dialogues and 249 hours of audios from human-to-human spoken conversations. SpokenWOZ further incorporates common spoken characteristics such as word-by-word processing and reasoning in spoken language. Based on these characteristics, we present cross-turn slot and reasoning slot detection as new challenges. We conduct experiments on various baselines, including text-modal models, newly proposed dual-modal models, and LLMs, e.g., ChatGPT. The results show that the current models still have substantial room for improvement in spoken conversation, where the most advanced dialogue state tracker only achieves 25.65% in joint goal accuracy and the SOTA end-to-end model only correctly completes the user request in 52.1% of dialogues. The dataset, code, and leaderboard are available: https://spokenwoz.github. io/.
Real-Time Neural Video Recovery and Enhancement on Mobile Devices

As mobile devices become increasingly popular for video streaming, it's crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techniques are focused solely on super-resolution and cannot handle partial or complete loss or corruption of video frames, which is common on the Internet and wireless networks. To overcome these challenges, we present a novel approach in this paper. Our approach consists of (i) a novel video frame recovery scheme, (ii) a new super-resolution algorithm, and (iii) a receiver enhancement-aware video bit rate adaptation algorithm. We have implemented our approach on an iPhone 12, and it can support 30 frames per second (FPS). We have evaluated our approach in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows that our approach enables real-time enhancement and results in a significant increase in video QoE (Quality of Experience) of 24\% - 82\% in our video streaming system.
Bow control and playability of a two-polarisation time domain physical model of a bowed string

Many studies have explored bowed string control, first in theory, and later through experiments and simulations. Bowing control is most commonly described in terms of 3 parameters: downward bow force, bow position, and transverse bow velocity. The correlation between these parameters and the production of a note is generally referred to as the playability of the instrument. The musically-useful region within this parameter space where Helmholtz motion is achieved has been extensively researched. The coordination of these player-controlled parameters is crucial for the existence of steady-state oscillations, but also for their behaviour during note onset. Time domain models of bowed strings require the dynamic input of these physical control parameters. A detailed exploration of this parameter space is therefore of importance for playable physical models. In this work, a two-polarisation bowed string is coupled to a fully dynamic nonlinear lumped bow model. A finite difference scheme is used to solve the discretised equations of motion in the time domain. The bow-controlled input to the model is based on a two-dimensional force vector applied to the bow itself, coupling the two string polarisations. One force component is applied downwards, and the other orthogonally across the string axis, in contrast to the common convention of imposing a downward bow force and transverse bow velocity. The new model has the potential to allow for more realistic and dynamic gesture control. This study explores the parameter space implied by the model, relating it to the playability of the virtual instrument. Analysis of the steady state bow-string dynamics leads to classification of the playing regime, and a graphical representation inspired by the well known Schelleng diagram. The resulting playability-force diagrams may be used as an aid to control of the sound synthesis algorithm.
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.
Long Term Survival Probabilities and Kaplan-Meier Estimator

The Kaplan-Meier nonparametric estimator has become a standard tool for estimating a survival time distribution in a right censoring schema. However, if the censoring rate is high, this estimator do not provide a reliable estimation of the extreme survival probabilities. In this paper we propose to combine the nonparametric Kaplan-Meier estimator and a parametric-based model into one con- struction. The idea is to fit the tail of the survival function with a parametric model while for the remaining to use the Kaplan-Meier estimator. A procedure for the automatic choice of the location of the tail based on a goodness-of-fit test is proposed. This technique allows us to improve the es- timation of the survival probabilities in the mid and long term. We perform numerical simulations which confirm the advantage of the proposed method.
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data

We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate new samples. These generative algorithms are particle-based and are constructed as gradient flows of Lipschitz-regularized Kullback-Leibler or other $f$-divergences, where data from a source distribution can be stably transported as particles, towards the vicinity of the target distribution. As a highlighted result in data integration, we demonstrate that the proposed algorithms correctly transport gene expression data points with dimension exceeding 54K, while the sample size is typically only in the hundreds.
Anytime Model Selection in Linear Bandits

Model selection in the context of bandit optimization is a challenging problem, as it requires balancing exploration and exploitation not only for action selection, but also for model selection. One natural approach is to rely on online learning algorithms that treat different models as experts. Existing methods, however, scale poorly ($\text{poly}M$) with the number of models $M$ in terms of their regret. Our key insight is that, for model selection in linear bandits, we can emulate full-information feedback to the online learner with a favorable bias-variance trade-off. This allows us to develop ALEXP, which has an exponentially improved ($\log M$) dependence on $M$ for its regret. ALEXP has anytime guarantees on its regret, and neither requires knowledge of the horizon $n$, nor relies on an initial purely exploratory stage. Our approach utilizes a novel time-uniform analysis of the Lasso, establishing a new connection between online learning and high-dimensional statistics.
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Adam is a commonly used stochastic optimization algorithm in machine learning. However, its convergence is still not fully understood, especially in the non-convex setting. This paper focuses on exploring hyperparameter settings for the convergence of vanilla Adam and tackling the challenges of non-ergodic convergence related to practical application. The primary contributions are summarized as follows: firstly, we introduce precise definitions of ergodic and non-ergodic convergence, which cover nearly all forms of convergence for stochastic optimization algorithms. Meanwhile, we emphasize the superiority of non-ergodic convergence over ergodic convergence. Secondly, we establish a weaker sufficient condition for the ergodic convergence guarantee of Adam, allowing a more relaxed choice of hyperparameters. On this basis, we achieve the almost sure ergodic convergence rate of Adam, which is arbitrarily close to $o(1/\sqrt{K})$. More importantly, we prove, for the first time, that the last iterate of Adam converges to a stationary point for non-convex objectives. Finally, we obtain the non-ergodic convergence rate of $O(1/K)$ for function values under the Polyak-Lojasiewicz (PL) condition. These findings build a solid theoretical foundation for Adam to solve non-convex stochastic optimization problems.