Miracle-dz/UniFAR-FTUs huggingface dataset:
Suitability of magnetic microbolometers based on paramagnetic temperature sensors for CMB polarization measurements

Abstract. High-resolution maps of polarization anisotropies of the cosmic microwave background (CMB) are in high demand, since the discovery of primordial B-modes in the polarization patterns would confirm the inflationary phase of the universe that would have taken place before the last scattering of the CMB at the recombination epoch. Transition edge sensors (TES) and microwave kinetic inductance detectors (MKID) are the predominant detector technologies of cryogenic detector array-based CMB instruments that search for primordial B-modes. We propose another type of cryogenic detector to be used for CMB survey: a magnetic microbolometer (MMB) that is based on a paramagnetic temperature sensor. It is an adaption of state-of-the-art metallic magnetic calorimeters (MMCs) that are meanwhile a key technology for high resolution α, β, γ, and x-ray spectroscopy as well as the study of neutrino mass. The effort to adapt MMCs for CMB surveys is triggered by their lack of Johnson noise associated with the detector readout, the possibility of straightforward calibration and higher dynamic range given it possesses a broad and smooth responsivity dependence with temperature, and the absence of Joule dissipation which simplifies the thermal design. A brief proof of concept case study is analyzed, taking into account typical constraints in CMB measurements and reliable microfabrication processes, to assess the suitability of metallic magnetic sensors in CMB experiments. The results show that MMBs provide a promising technology for CMB polarization survey as their sensitivity can be tuned for background limited detection of the sky while simultaneously maintaining a low time response to avoid distortion of the point-source response of the telescope. As the sensor technology and its fabrication techniques are compatible with TES-based bolometric detector arrays, a change of detector technology would even come with very low cost.
Quantum Physics from Number Theory

The properties which give quantum mechanics its unique character - unitarity, complementarity, non-commutativity, uncertainty, nonlocality - derive from the algebraic structure of Hermitian operators acting on the wavefunction in complex Hilbert space. Because of this, the wavefunction cannot be shown to describe an ensemble of deterministic states where uncertainty simply reflects a lack of knowledge about which ensemble member describes reality. This has led to endless debates about the ontology of quantum mechanics. Here we derive these same quantum properties from number theoretic attributes of trigonometric functions applied to an explicitly ensemble-based representation of discretised complex Hilbert states. To avoid fine-tuning, the metric on state space must be $p$-adic rather than Euclidean where $1/p$ determines the fineness of the discretisation. This hints at both the existence of an underpinning fractal state-space geometry onto which states of the world are constrained. In this model, violation of Bell inequalities is a manifestation of this geometric constraint and does not imply a breakdown of local space-time causality. Because the discretised wavefunction describes an ensemble of states, there is no collapse of the wavefunction. Instead measurement describes a nonlinear clustering of state-space trajectories on the state-space geometry. In this model, systems with mass greater than the Planck mass will not exhibit quantum properties and instead behave classically. The geometric constraint suggests that the exponential increase in the size of state space with qubit number may break down with qubit numbers as small as a few hundred. Quantum mechanics is itself a singular limit of this number-theoretic model at $p=\infty$. A modification of general relativity, consistent with this discretised model of quantum physics, is proposed.
User recommendation system based on MIND dataset

Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.
Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition

Attention layers are an integral part of modern end-to-end automatic speech recognition systems, for instance as part of the Transformer or Conformer architecture. Attention is typically multi-headed, where each head has an independent set of learned parameters and operates on the same input feature sequence. The output of multi-headed attention is a fusion of the outputs from the individual heads. We empirically analyze the diversity between representations produced by the different attention heads and demonstrate that the heads become highly correlated during the course of training. We investigate a few approaches to increasing attention head diversity, including using different attention mechanisms for each head and auxiliary training loss functions to promote head diversity. We show that introducing diversity-promoting auxiliary loss functions during training is a more effective approach, and obtain WER improvements of up to 6% relative on the Librispeech corpus. Finally, we draw a connection between the diversity of attention heads and the similarity of the gradients of head parameters.
Origin of Enhanced Zone Lines in Field Desorption Maps

Artifacts in the collective desorption map of the detector hits impede a truthful reconstruction, including enhanced"zone lines"with high atomic impact intensity. Since APT is destructive, simulation is the only approach to explain the origin of these zone lines, but previous work couldn't reproduce them. Here, we use a new simulation technique that adds the full electrostatic forces to the interatomic forces in a molecular-dynamics simulation and eliminates the previous ad-hoc assumptions. We find for the canonical example of tungsten that evaporation happens when the electrostatic force overpowers the interatomic force, and the misalignment of the two forces deviates the launch direction of the atoms in certain zones, giving rise to an accumulation of hit events around zone lines.
Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases

Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data. We present Expressive Reasoning Graph Store (ERGS) -- a graph store built on top of JanusGraph (a Property Graph store) that also allows storing and querying of RDF datasets. First, we describe how RDF data can be translated into a Property Graph representation and then describe a query translation module that converts SPARQL queries into a series of Gremlin traversals. The converters and translators thus developed can allow any Apache Tinkerpop compliant graph database to store and query RDF datasets. We demonstrate the effectiveness of our proposed approach using JanusGraph as the base Property Graph store and compare its performance with standard RDF systems.
A Gaussian Variational Inference Approach to Motion Planning

We propose a Gaussian variational inference framework for the motion planning problem. In this framework, motion planning is formulated as an optimization over the distribution of the trajectories to approximate the desired trajectory distribution by a tractable Gaussian distribution. Equivalently, the proposed framework can be viewed as a standard motion planning with an entropy regularization. Thus, the solution obtained is a transition from an optimal deterministic solution to a stochastic one, and the proposed framework can recover the deterministic solution by controlling the level of stochasticity. To solve this optimization, we adopt the natural gradient descent scheme. The sparsity structure of the proposed formulation induced by factorized objective functions is further leveraged to improve the scalability of the algorithm. We evaluate our method on several robot systems in simulated environments, and show that it achieves collision avoidance with smooth trajectories, and meanwhile brings robustness to the deterministic baseline results, especially in challenging environments and tasks.
PET image denoising based on denoising diffusion probabilistic model

Purpose Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising. Methods Under the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [ $$^{18}$$ 18 F]FDG datasets and 140 brain [ $$^{18}$$ 18 F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods. Results Quantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance. Conclusion DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.
SkIn: Skimming-Intensive Long-Text Classification Using BERT for Medical Corpus

BERT is a widely used pre-trained model in natural language processing. However, since BERT is quadratic to the text length, the BERT model is difficult to be used directly on the long-text corpus. In some fields, the collected text data may be quite long, such as in the health care field. Therefore, to apply the pre-trained language knowledge of BERT to long text, in this paper, imitating the skimming-intensive reading method used by humans when reading a long paragraph, the Skimming-Intensive Model (SkIn) is proposed. It can dynamically select the critical information in the text so that the sentence input into the BERT-Base model is significantly shortened, which can effectively save the cost of the classification algorithm. Experiments show that the SkIn method has achieved superior accuracy than the baselines on long-text classification datasets in the medical field, while its time and space requirements increase linearly with the text length, alleviating the time and space overflow problem of basic BERT on long-text data.
Deep Learning Training on Multi-Instance GPUs

Deep learning training is an expensive process that extensively uses GPUs, but not all model training saturates the modern powerful GPUs. Multi-Instance GPU (MIG) is a new technology introduced by NVIDIA that can partition a GPU to better fit workloads that don’t require all the memory and compute resources of a full GPU. In this paper, we examine the performance of a MIG-enabled A100 GPU under deep learning workloads of three sizes focusing on image recognition training with ResNet models. We investigate the behavior of these workloads when running in isolation on a variety of MIG instances allowed by the GPU in addition to running them in parallel on homogeneous instances co-located on the same GPU. Our results demonstrate that employing MIG can significantly improve the utilization of the GPU when the workload is too small to utilize the whole GPU in isolation. By training multiple small models in parallel, more work can be performed by the GPU per unit of time, despite the increase in time-per-epoch, leading to ∼ 3 times the throughput. In contrast, for medium and large-sized work-loads, which already utilize the whole GPU well on their own, MIG only provides marginal performance improvements. Nevertheless, we observe that training models in parallel using separate MIG partitions does not exhibit interference underlining the value of having a functionality like MIG on modern GPUs.
A stochastic volatility model for the valuation of temperature derivatives

This paper develops a new stochastic volatility model for the temperature that is a natural extension of the Ornstein-Uhlenbeck model proposed by Benth and Benth (2007). This model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on Conditional Least Squares to estimate the parameters on daily data and estimate our model on eight major European cities. We then show how to calculate efficiently the average payoff of weather derivatives both by Monte-Carlo and Fourier transform techniques. This new model allows to better assess the risk related to temperature volatility.
Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects

Interactions with articulated objects are a challenging but important task for mobile robots. To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates manipulation priors from affordance estimation with sampling-based whole-body control. We introduce the concept of agent-aware affordances which fully reflect the agent's capabilities and embodiment and we show that they outperform their state-of-the-art counterparts which are only conditioned on the end-effector geometry. Additionally, closed-loop affordance inference is found to allow the agent to divide a task into multiple non-continuous motions and recover from failure and unexpected states. Finally, the pipeline is able to perform long-horizon mobile manipulation tasks, i.e. opening and closing an oven, in the real world with high success rates (opening: 71%, closing: 72%).
Exploring the use of Transition Path Theory in building an oil spill prediction scheme

The Transition Path Theory (TPT) of complex systems has proven to be a robust means to statistically characterize the ensemble of trajectories that connect any two preset flow regions, say 𝒜 and ℬ, directly. More specifically, transition paths are such that they start in 𝒜 and then go to ℬ without detouring back to 𝒜 or ℬ. This way, they make an effective contribution to the transport from 𝒜 to ℬ. Here, we explore its use for building a scheme that enables predicting the evolution of an oil spill in the ocean. This involves appropriately adapting TPT such that it includes a reservoir that pumps oil into a typically open domain. Additionally, we lift up the restriction of the oil not to return to the spill site en route to a region that is targeted to be protected. TPT is applied on oil trajectories available up to the present, e.g., as integrated using velocities produced by a data assimilative system or as inferred from high-frequency radars, to make a prediction of transition oil paths beyond, without relying on forecasted oil trajectories. As a proof of concept, we consider a hypothetical oil spill in the Trion oil field, under development within the Perdido Foldbelt in the northwestern Gulf of Mexico, and the Deepwater Horizon oil spill. This is done using trajectories integrated from climatological and hindcast surface velocity and winds as well as produced by satellite-tracked surface drifting buoys, in each case discretized into a Markov chain that provides a framework for the TPT-based prediction.
SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias

Real-time music accompaniment generation has a wide range of applications in the music industry, such as music education and live performances. However, automatic real-time music accompaniment generation is still understudied and often faces a trade-off between logical latency and exposure bias. In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias. Specifically, SongDriver divides one accompaniment generation task into two phases: 1) The arrangement phase, where a Transformer model first arranges chords for input melodies in real-time, and caches the chords for the next phase instead of playing them out. 2) The prediction phase, where a CRF model generates playable multi-track accompaniments for the coming melodies based on previously cached chords. With this two-phase strategy, SongDriver directly generates the accompaniment for the upcoming melody, achieving zero logical latency. Furthermore, when predicting chords for a timestep, SongDriver refers to the cached chords from the first phase rather than its previous predictions, which avoids the exposure bias problem. Since the input length is often constrained under real-time conditions, another potential problem is the loss of long-term sequential information. To make up for this disadvantage, we extract four musical features from a long-term music piece before the current time step as global information. In the experiment, we train SongDriver on some open-source datasets and an original àiMusic Dataset built from Chinese-style modern pop music sheets. The results show that SongDriver outperforms existing SOTA (state-of-the-art) models on both objective and subjective metrics, meanwhile significantly reducing the physical latency.
PointScatter: Point Set Representation for Tubular Structure Extraction

This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its flexibility and representation ability, which would not be restricted by the fixed grid as the mask. Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task. PointScatter splits the image into scatter regions and parallelly predicts points for each scatter region. We further propose the greedy-based region-wise bipartite matching algorithm to train the network end-to-end and efficiently. We benchmark the PointScatter on four public tubular datasets, and the extensive experiments on tubular structure segmentation and centerline extraction task demonstrate the effectiveness of our approach. Code is available at https://github.com/zhangzhao2022/pointscatter.
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes

In this paper, we find a sample complexity bound for learning a simplex from noisy samples. Assume a dataset of size $n$ is given which includes i.i. d. samples drawn from a uniform distribution over an unknown simplex in $\mathbb{R}^K$, where samples are assumed to be corrupted by a multi-variate additive Gaussian noise of an arbitrary magnitude. We prove the existence of an algorithm that with high probability outputs a simplex having a $\ell_2$ distance of at most $\varepsilon$ from the true simplex (for any $\varepsilon>0$). Also, we theoretically show that in order to achieve this bound, it is sufficient to have $n\ge\left(K^2/\varepsilon^2\right)e^{\Omega\left(K/\mathrm{SNR}^2\right)}$ samples, where $\mathrm{SNR}$ stands for the signal-to-noise ratio. This result solves an important open problem and shows as long as $\mathrm{SNR}\ge\Omega\left(K^{1/2}\right)$, the sample complexity of the noisy regime has the same order to that of the noiseless case. Our proofs are a combination of the so-called sample compression technique in \citep{ashtiani2018nearly}, mathematical tools from high-dimensional geometry, and Fourier analysis. In particular, we have proposed a general Fourier-based technique for recovery of a more general class of distribution families from additive Gaussian noise, which can be further used in a variety of other related problems.
Signal readout for Transition-Edge Sensor X-ray imaging spectrometers

Arrays of low-temperature microcalorimeters provide a promising technology for X-ray astrophysics: the imaging spectrometer. A camera with at least several thousand pixels, each of which has an energy-resolving power ($E/\Delta E\urss{FWHM}$) of a few thousand across a broad energy range (200~eV to 10~keV or higher), would be a revolutionary instrument for the study of energetic astrophysical objects and phenomena. Signal readout is a critical enabling technology. Multiplexed readout, in which signals from multiple pixels are combined into a single amplifier channel, allows a kilo pixel-scale microcalorimeter array to meet the stringent requirements for power consumption, mass, volume, and cooling capacity in orbit. This chapter describes three different multiplexed-readout technologies for transition-edge-sensor microcalorimeters: time-division multiplexing, frequency-domain multiplexing, and microwave-SQUID multiplexing. For each multiplexing technique, we present the basic method, discuss some design considerations and parameters, and show the state of the art. The chapter concludes with a brief discussion of future prospects.
Self test using unequiprobable random patterns

In this paper we present a module generating unequiprobable random patterns, which can also perform signature analysis and work like a normal register similar to the well known BILBO. The hardware overhead of this module has the same magnitude as a conventional BILBO. Thus the class of self testable circuits is enlarged without additional costs.
KSG: Knowledge and Skill Graph

The knowledge graph (KG) is an essential form of knowledge representation that has grown in prominence in recent years. Because it concentrates on nominal entities and their relationships, traditional knowledge graphs are static and encyclopedic in nature. On this basis, event knowledge graph (Event KG) models the temporal and spatial dynamics by text processing to facilitate downstream applications, such as question-answering, recommendation and intelligent search. Existing KG research, on the other hand, mostly focuses on text processing and static facts, ignoring the vast quantity of dynamic behavioral information included in photos, movies, and pre-trained neural networks. In addition, no effort has been done to include behavioral intelligence information into the knowedge graph for deep reinforcement learning (DRL) and robot learning. In this paper, we propose a noval dynamic knowledge and skill graph (KSG), and then we develop a basic and specific KSG based on CN-DBpedia. The nodes are divided into entity and attribute nodes, with entity nodes containing the agent, environment, and skill (DRL policy or policy representation), and attribute nodes containing the entity description, pre-train network, and offline dataset. KSG can search for different agents' skills in various environments and provide transferable information for acquiring new skills. This is the first study that we are aware of that looks into dynamic KSG for skill retrieval and learning. Extensive experimental results on new skill learning show that KSG boosts new skill learning efficiency.