Miracle-dz/UniFAR-FTUs huggingface dataset:
Learnable Encoder-Decoder Architecture for Dynamic Graph: A Survey

— In recent years, the prevalent online services generate a sheer volume of user activity data. Service providers collect these data in order to perform client behavior analysis, and offer better and more customized services. Majority of these data can be modeled and stored as graph, such as the social graph in Facebook, user-video interaction graph in Youtube. These graphs need to evolve over time to capture the dynamics in the real world, leading to the invention of dynamic graphs. However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them. Events staleness, temporal information learning and explicit time dimension usage are some example challenges in dynamic graph learning. In order to offer a convenient reference to both the industry and academia, this survey presents the Three Stages Recurrent Temporal Learning Framework based on dynamic graph evolution theories, so as to interpret the learning of temporal information with a generalized framework. Under this framework, this survey categories and reviews different learnable encoder-decoder architectures for supervised dynamic graph learning. We believe that this survey could supply useful guidelines to researchers and engineers in ﬁnding suitable graph structures for their dynamic learning tasks.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency.
White Paper on High Temperature Superconducting Bi-2212 Magnets for Energy Frontier Circular Colliders

As the only high-temperature superconducting (HTS) material available as an isotropic, twisted, multifilamentary round wire, Bi-2212 is very promising for expanding the high-field superconducting accelerator magnet toolbox beyond the round-wire, isotropic Nb-Ti and Nb3Sn conductors used by HEP so far. In this paper, we describe the important roles that Bi-2212 might play for future high energy circular colliders including both high energy proton and muon colliders. We describe its present technology status (conductor development, magnet design concepts, prototype magnet status) and then provide a ten-year plan for>15 T accelerator magnets and>25 T solenoid magnets within an integrated strategy to engage industry and the entire US and international scientific enterprise interested in HTS magnet applications.
On the Resolutions of Non-Dicritical Foliations

We introduce the jet schemes of a holomorphic foliation, and thereby prove an alternate characterisation of simple singularities of codimension-$1$ foliations, independent of any normal form. This leads to an equivalent condition for the existence of a desingularisation in the non-dicritical case. We then prove that such a desingularisation always exists, at least on the level of germs.
A Stochastic Planning Method for Low-carbon Building-level Integrated Energy System Considering Electric-Heat-V2G Coupling

supported by the National Natural Science Foundation of China (NSFC) (52007123) Corresponding authors: Xinwei Shen (sxw.tbsi@sz. tsinghua.edu.cn), Hongbin Sun (shb@tsinghua.edu.cn). Abstract-The concept of low-carbon building is proposed to ameliorate the climate change caused by environmental problems and realize carbon neutrality at the building level in urban areas. In addition, renewable energy curtailment in the power distribution system, as well as low efficiency due to independent operation of traditional energy systems, has been addressed by the application of integrated energy system (IES) to some extent. In this paper, we propose a planning method for low-carbon building-level IES, in which electric vehicles (EV) and the mode of Vehicle to Grid (V2G) are considered and further increase the flexibility of low-carbon buildings. The proposed planning model optimize the investment, operation costs and CO2 emission for building-level IES, so as to achieve the maximum benefit of the construction of the low-carbon building and help the realization of carbon neutrality. Moreover, we consider the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, then formulating a two-stage stochastic programming model with chance constraints, in which heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) method are applied. In case study, a real IES commercial building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as numerical example to verify the effectiveness of the proposed planning method, with functions of ESS and EV in IES are analyzed in detail in different operation scenarios.
Deterministic Bridge Regression for Compressive Classification

Pattern classification with compact representation is an important component in machine intelligence. In this work, an analytic bridge solution is proposed for compressive classification. The proposal has been based upon solving a penalized error formulation utilizing an approximated $\ell_p$-norm. The solution comes in a primal form for over-determined systems and in a dual form for under-determined systems. While the primal form is suitable for problems of low dimension with large data samples, the dual form is suitable for problems of high dimension but with a small number of data samples. The solution has also been extended for problems with multiple classification outputs. Numerical studies based on simulated and real-world data validated the effectiveness of the proposed solution.
A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation

Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task’s complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-specific systems tend to yield improved results over instrument-agnostic methods. Similarly, higher accuracy can be obtained when only estimating frame-wise f0 values and neglecting the harder note event detection. Despite their high accuracy, such specialized systems often cannot be deployed in the real-world. Storage and network constraints prohibit the use of multiple specialized models, while memory and run-time constraints limit their complexity. In this paper, we propose a lightweight neural network for musical instrument transcription, which supports polyphonic outputs and generalizes to a wide variety of instruments (including vocals). Our model is trained to jointly predict frame-wise onsets, multipitch and note activations, and we experimentally show that this multi-output structure improves the resulting frame-level note accuracy. Despite its simplicity, benchmark results show our system’s note estimation to be substantially better than a comparable baseline, and its frame-level accuracy to be only marginally below those of specialized state-of-the-art AMT systems. With this work we hope to encourage the community to further investigate low-resource, instrument-agnostic AMT systems.
Individualizing Head-Related Transfer Functions for Binaural Acoustic Applications

A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localization and 3D sound spatialization. Unfortunately, conventional approaches for HRTF estimation rely on specialized devices or lengthy measurement processes. This work proposes a novel lightweight method for HRTF individual-ization that can be implemented using commercial-off-the-shelf components and performed by average users in home settings. The proposed method has two key components: a generative neural network model that can be individualized to predict HRTFs of new subjects from sparse measurements, and a lightweight measurement procedure that collects HRTF data from spatial locations. Exten-sive experiments using a public dataset and in house measurement data from 10 subjects of different ages and genders, show that the individualized models significantly outperform a baseline model in the accuracy of predicted HRTFs. To further demonstrate the advantages of individualized HRTFs, we implement two prototype applications for binaural localization and acoustic spatialization. We find that the performance of a localization model is improved by 15° after trained with individualized HRTFs. Furthermore, in hearing tests, the success rate of correctly identifying the azimuth direction of incoming sounds increases by 183% after individualization.
A new perspective on probabilistic image modeling

We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMM instances exhibit a CNN-like layered structure, in which the principal building blocks are convolutional Gaussian Mixture (cGMM) layers. A key innovation w.r. t. related models like sum-product networks (SPNs) and probabilistic circuits (PCs) is that each cGMM layer optimizes an independent loss function and therefore has an independent probabilistic interpretation. This modular approach permits intervening transformation layers to harness the full spectrum of (potentially non-invertible) mappings available to CNNs, e.g., max-pooling or half-convolutions. DCGMM sampling and inference are realized by a deep chain of hierarchical priors, where a sample generated by a given cGMM layer defines the parameters of sampling in the next-lower cGMM layer. For sampling through non-invertible transformation layers, we introduce a new gradient-based sharpening technique that exploits redundancy (overlap) in, e.g., half-convolutions. DCGMMs can be trained end-to-end by SGD from random initial conditions, much like CNNs. We show that DCGMMs compare favorably to several recent PC and SPN models in terms of inference, classification and sampling, the latter particularly for challenging datasets such as SVHN. We provide a public TF2 implementation.
Analyzing EEG Data with Machine and Deep Learning: A Benchmark

Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectures to the available data, or designing custom architectures. In both cases, to speed up the research process, it is useful to know which type of models work best for a specific problem and/or data type. By focusing on EEG signal analysis, and for the first time in literature, in this paper a benchmark of machine and deep learning for EEG signal classification is proposed. For our experiments we used the four most widespread models, i.e., multilayer perceptron, convolutional neural network, long short-term memory, and gated recurrent unit, highlighting which one can be a good starting point for developing EEG classification models.
Partial least square method and BP neural network coupled model and its application to prediction of pH value of acid rains

By taking advantages of the partial least square method for effective treatment of the multi-correlations between independent variables and BP neural network for effectively solving the nonlinear problems,a partial least square method and BP neural network coupled prediction model was developed. The application of the model to prediction of the pH value of urban acid rains shows that the precision of the model is high,and that it is superior to the partial least square regression model and the artificial neural network model.
Compositional Synthesis of Signal Temporal Logic Tasks via Assume-Guarantee Contracts

In this paper, we focus on the problem of compositional synthesis of controllers enforcing signal temporal logic (STL) tasks over a class of continuous-time nonlinear interconnected systems. By leveraging the idea of funnel-based control, we show that a fragment of STL specifications can be formulated as assume-guarantee contracts. A new concept of contract satisfaction is then defined to establish our compositionality result, which allows us to guarantee the satisfaction of a global contract by the interconnected system when all subsystems satisfy their local contracts. Based on this compositional framework, we then design closed-form continuous-time feedback controllers to enforce local contracts over subsystems in a decentralized manner. Finally, we demonstrate the effectiveness of our results on a numerical example.
Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel k-means, and Heat Kernel

We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. For graph cut based spectral clustering, it is common to model real-valued data into graph by modeling pairwise similarities using kernel function. This is because the kernel function has a theoretical connection to the graph cut. For problems where using multi-way similarities are more suitable than pairwise ones, it is natural to model as a hypergraph, which is generalization of a graph. However, although the hypergraph cut is well-studied, there is not yet established a hypergraph cut based framework to model multi-way similarity. In this paper, we formulate multi-way similarities by exploiting the theoretical foundation of kernel function. We show a theoretical connection between our formulation and hypergraph cut in two ways, generalizing both weighted kernel k-means and the heat kernel, by which we justify our formulation. We also provide a fast algorithm for spectral clustering. Our algorithm empirically shows better performance than existing graph and other heuristic modeling methods.
Highly Adaptive and Automated Tetrahedral Mesh Generator for Multi-Compartment Human Head Model with Deep Brain Structures in EEG

Purpose: This paper introduces a highly adaptive and fast approach for generating a ﬁnite element (FE) discretization mesh for a given multi-compartment human head model obtained through a magnetic resonance imaging (MRI) dataset. The goal is to create accurate deep brain structures for electroencephalographic (EEG) source localization and applications. Methods: We present a recursive application of solid angle labeling for surface segmentation with an adaptive scheme, i.e., a set of smoothing, inﬂation, and optimization tasks to enhance the mesh quality. Results: The results indicate that our approach can produce a FE mesh with an accuracy greater than 1.0 millimeters, a signiﬁcant milestone for 3D structure discretization and EEG source localization estimation. Conclusions: Our method, implemented in the Matlab-based Zeﬃro Interface, can manage the labeling aspect remarkably
Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks

Video-based analysis of facial expressions has been increasingly applied to infer health states of individuals, such as depression and pain. Among the existing approaches, deep learning models composed of structures for multiscale spatiotemporal processing have shown strong potential for encoding facial dynamics. However, such models have high computational complexity, making for a difficult deployment of these solutions. To address this issue, we introduce a new technique to decompose the extraction of multiscale spatiotemporal features. Particularly, a building block structure called Decomposed Multiscale Spatiotemporal Network (DMSN) is presented along with three variants: DMSN-A, DMSN-B, and DMSN-C blocks. The DMSN-A block generates multiscale representations by analyzing spatiotemporal features at multiple temporal ranges, while the DMSN-B block analyzes spatiotemporal features at multiple ranges, and the DMSN-C block analyzes spatiotemporal features at multiple spatial sizes. Using these variants, we design our DMSN architecture which has the ability to explore a variety of multiscale spatiotemporal features, favoring the adaptation to different facial behaviors. Our extensive experiments on challenging datasets show that the DMSN-C block is effective for depression detection, whereas the DMSN-A block is efficient for pain estimation. Results also indicate that our DMSN architecture provides a cost-effective solution for expressions that range from fewer facial variations over time, as in depression detection, to greater variations, as in pain estimation.
Extreme multistability in a memristive circuit

A memristive Chua's circuit regarded as a paradigm is reconsidered to exhibit its extreme multistability in this Letter. Memristor initial state-dependent dynamics is analysed and the coexistence of infinitely many attractors related to memristor initial states is revealed by numerical simulations and circuit simulations. The dynamical behaviour just reflects the emergence of extreme multistability in the memristive circuit.
Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and Ethics

In recent years, many works have investigated the feasibility of conversational robots for performing specific tasks, such as healthcare and interview. Along with this development comes a practical issue: how should we synthesize robotic voices to meet the needs of different situations? In this paper, we discuss this issue from three perspectives: 1) the difficulties of synthesizing non-verbal and interaction-oriented speech signals, particularly backchannels; 2) the scenario classification for robotic voice synthesis; 3) the ethical issues regarding the design of robot voice for its emotion and identity. We present the findings of relevant literature and our prior work, trying to bring the attention of human-robot interaction researchers to design better conversational robots in the future.
Self-Consistency Improves Chain of Thought Reasoning in Language Models

Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer. Our extensive empirical evaluation shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%).
Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis

We consider the task of object grasping with a prosthetic hand capable of multiple grasp types. In this setting, communicating the intended grasp type often requires a high user cognitive load which can be reduced adopting shared autonomy frameworks. Among these, so-called eye-in-hand systems automatically control the hand pre-shaping before the grasp, based on visual input coming from a camera on the wrist. In this paper, we present an eye-in-hand learning-based approach for hand pre-shape classification from RGB sequences. Differently from previous work, we design the system to support the possibility to grasp each considered object part with a different grasp type. In order to overcome the lack of data of this kind and reduce the need for tedious data collection sessions for training the system, we devise a pipeline for rendering synthetic visual sequences of hand trajectories. We develop a sensorized setup to acquire real human grasping sequences for benchmarking and show that, compared on practical use cases, models trained with our synthetic dataset achieve better generalization performance than models trained on real data. We finally integrate our model on the Hannes prosthetic hand and show its practical effectiveness. We make publicly available the code and dataset to reproduce the presented results11https://github.com/hsp-iit/prosthetic-grasping-simulation.
Kinematic Structures in Planet-Forming Disks

The past 5 years have dramatically changed our view of the disks of gas and dust around young stars. Observations with the Atacama Large Millimeter/submillimeter Array (ALMA) and extreme adaptive optics systems have revealed that disks are dynamical systems. Most disks contain resolved structures, both in gas and dust, including rings, gaps, spirals, azimuthal dust concentrations, shadows cast by misaligned inner disks, as well as deviations from Keplerian rotation. The origin of these structures and how they relate to the planet formation process remain poorly understood. Spatially resolved kinematic studies offer a new and necessary window to understand and quantify the physical processes (turbulence, winds, radial and meridional flows, stellar multiplicity, instabilities) at play during planet formation and disk evolution. Recent progress, driven mainly by resolved ALMA observations, includes the detection and mass determination of embedded planets, the mapping of the gas flow around the accreting planets, the confirmation of tidal interactions and warped disk geometries, and stringent limits on the turbulent velocities. In this chapter, we will review our current understanding of these dynamical processes and highlight how kinematic mapping provides new ways to observe planet formation in action.