Miracle-dz/UniFAR-FTUs huggingface dataset:
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment

Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, integrates Contrastive Language-Image Pretraining (CLIP) and Image Caption (IC) into a unified framework, resulting in impressive results. CLIP imposes a bidirectional constraints on global representation of entire images and sentences. Although IC conducts an unidirectional image-to-text generation on local representation, it lacks any constraint on local text-to-image reconstruction, which limits the ability to understand images at a fine-grained level when aligned with texts. To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels. Specifically, we expand a Text-Guided Masked Image Modeling (TG-MIM) head based on ITC and IC heads. The improved SyCoCa can further leverage textual cues to reconstruct contextual images and visual cues to predict textual contents. When implementing bidirectional local interactions, the local contents of images tend to be cluttered or unrelated to their textual descriptions. Thus, we employ an attentive masking strategy to select effective image patches for interaction. Extensive experiments on five vision-language tasks, including image-text retrieval, image-captioning, visual question answering, and zero-shot/finetuned image classification, validate the effectiveness of our proposed method.
Simplified Information Geometry Approach for Massive MIMO-OFDM Channel Estimation - Part II: Convergence Analysis

In Part II of this two-part paper, we prove the convergence of the simplified information geometry approach (SIGA) proposed in Part I. For a general Bayesian inference problem, we first show that the iteration of the common second-order natural parameter (SONP) is separated from that of the common first-order natural parameter (FONP). Hence, the convergence of the common SONP can be checked independently. We show that with the initialization satisfying a specific but large range, the common SONP is convergent regardless of the value of the damping factor. For the common FONP, we establish a sufficient condition of its convergence and prove that the convergence of the common FONP relies on the spectral radius of a particular matrix related to the damping factor. We give the range of the damping factor that guarantees the convergence in the worst case. Further, we determine the range of the damping factor for massive MIMO-OFDM channel estimation by using the specific properties of the measurement matrices. Simulation results are provided to confirm the theoretical results.
On the maximal and minimal degree components of the cocenter of the cyclotomic KLR algebras

Let $\mathscr{R}_\alpha^\Lambda$ be the cyclotomic KLR algebra associated to a symmetrizable Kac-Moody Lie algebra $\mathfrak{g}$ and polynomials $\{Q_{ij}(u,v)\}_{i,j\in I}$. Shan, Varagnolo and Vasserot show that, when the ground field $K$ has characteristic $0$, the degree $d$ component of the cocenter $Tr(\mathscr{R}_\alpha^\Lambda)$ is nonzero only if $0\leq d\leq d_{\Lambda,\alpha}$. In this paper we show that this holds true for arbitrary ground field $K$, arbitrary $\mathfrak{g}$ and arbitrary polynomials $\{Q_{ij}(u,v)\}_{i,j\in I}$. We generalize our earlier results on the $K$-linear generators of $Tr(\mathscr{R}_\alpha^\Lambda), Tr(\mathscr{R}_\alpha^\Lambda)_0, Tr(\mathscr{R}_\alpha^\Lambda)_{d_{\Lambda,\alpha}}$ to arbitrary ground field $K$. Moreover, we show that the dimension of the degree $0$ component $Tr(\mathscr{R}_\alpha^\Lambda)_0$ is always equal to $\dim V(\Lambda)_{\Lambda-\alpha}$, where $V(\Lambda)$ is the integrable highest weight $U(\mathfrak{g})$-module with highest weight $\Lambda$, and we obtain a basis for $Tr(\mathscr{R}_\alpha^\Lambda)_0$.
Strong and Highly Switchable Soft Sticky Adhesives

Many biological systems can form strong adhesion to various materials with complex shapes. The adhesion is further switchable between strongly adhering and completely non-adhering in a simple and fast manner. By contrast, no engineering system has yet achieved the same robust adherence and switching. This limitation severely hinders the advancement of several emerging technologies including biomimetic robots, assembly-based manufacturing, precision medicine, wearable and implantable devices, as well as on-demand material dismantling and recycling for sustainability. Here we present a design approach for strong and highly switchable adhesion by synergizing the surface stickiness, bulk energy dissipation, and stimuli-responsive polymer chains in a thermo-switchable soft sticky adhesive. The adhesive has a high adhesion strength of about 80 kPa with diverse materials at room temperature. The adhesion is highly switchable to near-vanishing (about 0.6 kPa) at an elevated temperature due to the thermo-responsive surface polymer chain retraction. This adhesion switching is reversible and repeatable for many cycles, enabling selective pick-and-release of objects with various materials, shapes, sizes, and weights. The switching time is around 10 s with an adhesive layer of 1 mm, governed by thermal conduction through the adhesive, faster than or comparable to most state-of-the-art methods. The adhesive is self-healing, and can be recycled, dried, stored, reswollen, and reused with nearly intact adhesion and switching properties. The synergistic design combining strong adhesion and stimuli-responsive switching can be potentially extended to various polymer systems, and further enhanced by optimized surface architectures.
Phase space path integral approach to the kinetics of black hole phase transition

We employ the approach of path integral in the phase space to study the kinetics of state switching associated with black hole phase transitions. Under the assumption that the state switching process of the black hole is described by the stochastic Langevin equation based on the free energy landscape, we derived the Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) functional and obtained the path integral expression of the transition probability. The MSRJD functional inherently represents the path integral in the phase space, allowing us to extract the effective Hamiltonian for the dynamics of state switching process. By solving the Hamiltonian equations of motion, we obtain the kinetic path in the phase space using an example of the RNAdS black hole. Furthermore, the dominant kinetic path within the configuration space is calculated. We also discuss the kinetic rate by using the functional formalism. Finally, we examine two further examples: Hawking-Page phase transition and Gauss-Bonnet black hole phase transition at the triple point. Our analysis demonstrates that, concerning the Hawking-Page phase transition, while a dominant kinetic path in the phase space from the large SAdS black hole to the thermal AdS space is present, there is no kinetic path for the inverse process. For the Gauss-Bonnet black hole phase transition at the triple point, the state switching processes between the small, the intermediate and the large Gauss-Bonnet black holes constitute a chemical reaction cycle.
Exploring the Unconventional Electron Distribution Patterns in Iron-based Superconductors

For more than a decade, the unusual distribution of electrons observed in ARPES (angle-resolved photoemission spectroscopy) data within the energy range of ~30meV to ~300meV below the Fermi level, known as the ARPES range, has remained a puzzle in the field of iron-based superconductivity. However, in this study, we have made a noteworthy observation: although the electron-phonon coupling alone is insufficient to account for the observed ARPES pattern, our analysis reveals that when the instantaneous electron-phonon coupling occurring in selective phonon channel is enhanced by the coexistence of antiferromagnetic spin density wave and charge density wave phenomena, the amplified interaction becomes comparable to the ARPES range. This finding suggests that the instantaneous interplay between these intricate phenomena should play a crucial role in generating the observed energy range in ARPES. Our work may provide a valuable clue towards achieving a deeper understanding of the complex relationship between electronic correlations, lattice structure, and superconductivity in iron-based materials for uncovering the origin of the unconventional ARPES pattern.
Survey of 3D Human Body Pose and Shape Estimation Methods for Contemporary Dance Applications

3D human body shape and pose estimation from RGB images is a challenging problem with potential applications in augmented/virtual reality, healthcare and fitness technology and virtual retail. Recent solutions have focused on three types of inputs: i) single images, ii) multi-view images and iii) videos. In this study, we surveyed and compared 3D body shape and pose estimation methods for contemporary dance and performing arts, with a special focus on human body pose and dressing, camera viewpoint, illumination conditions and background conditions. We demonstrated that multi-frame methods, such as PHALP, provide better results than single-frame method for pose estimation when dancers are performing contemporary dances.
Correctness Comparison of ChatGPT-4, Bard, Claude-2, and Copilot for Spatial Tasks

Generative AI including large language models (LLMs) have recently gained significant interest in the geo-science community through its versatile task-solving capabilities including coding, spatial computations, generation of sample data, time-series forecasting, toponym recognition, or image classification. So far, the assessment of LLMs for spatial tasks has primarily focused on ChatGPT, arguably the most prominent AI chatbot, whereas other chatbots received less attention. To narrow this research gap, this study evaluates the correctness of responses for a set of 54 spatial tasks assigned to four prominent chatbots, i.e., ChatGPT-4, Bard, Claude-2, and Copilot. Overall, the chatbots performed well on spatial literacy, GIS theory, and interpretation of programming code and given functions, but revealed weaknesses in mapping, code generation, and code translation. ChatGPT-4 outperformed other chatbots across most task categories.
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performance in both classification and regression tasks compared to traditional methods. The survey highlights a critical gap in deep neural TDL methods: the underrepresentation of latent correlations among data instances and feature values. GNNs, with their innate capability to model intricate relationships and interactions between diverse elements of tabular data, have garnered significant interest and application across various TDL domains. Our survey provides a systematic review of the methods involved in designing and implementing GNNs for TDL (GNN4TDL). It encompasses a detailed investigation into the foundational aspects and an overview of GNN-based TDL methods, offering insights into their evolving landscape. We present a comprehensive taxonomy focused on constructing graph structures and representation learning within GNN-based TDL methods. In addition, the survey examines various training plans, emphasizing the integration of auxiliary tasks to enhance the effectiveness of instance representations. A critical part of our discussion is dedicated to the practical application of GNNs across a spectrum of GNN4TDL scenarios, demonstrating their versatility and impact. Lastly, we discuss the limitations and propose future research directions, aiming to spur advancements in GNN4TDL. This survey serves as a resource for researchers and practitioners, offering a thorough understanding of GNNs' role in revolutionizing TDL and pointing towards future innovations in this promising area.
ModuleGuard: Understanding and Detecting Module Conflicts in Python Ecosystem

Python has become one of the most popular programming languages for software development due to its simplicity, readability, and versatility. As the Python ecosystem grows, developers face increasing challenges in avoiding module conflicts, which occur when different packages have the same namespace modules. Unfortunately, existing work has neither investigated the module conflict comprehensively nor provided tools to detect the conflict. Therefore, this paper systematically investigates the module conflict problem and its impact on the Python ecosystem. We propose a novel technique called InstSimulator, which leverages semantics and installation simulation to achieve accurate and efficient module extraction. Based on this, we implement a tool called ModuleGuard to detect module conflicts for the Python ecosystem. For the study, we first collect 97 MC issues, classify the characteristics and causes of these MC issues, summarize three different conflict patterns, and analyze their potential threats. Then, we conducted a large-scale analysis of the whole PyPI ecosystem (4.2 million packages) and GitHub popular projects (3,711 projects) to detect each MC pattern and analyze their potential impact. We discovered that module conflicts still impact numerous TPLs and GitHub projects. This is primarily due to developers' lack of understanding of the modules within their direct dependencies, not to mention the modules of the transitive dependencies. Our work reveals Python's shortcomings in handling naming conflicts and provides a tool and guidelines for developers to detect conflicts.
Instruct-Imagen: Image Generation with Multi-modal Instruction

This paper presents instruct-imagen, a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision. It uses natural language to amalgamate disparate modalities (e.g., text, edge, style, subject, etc.), such that abundant generation intents can be standardized in a uniform format. We then build instruct-imagen by fine-tuning a pre-trained text-to-image diffusion model with a two-stage framework. First, we adapt the model using the retrieval-augmented training, to enhance model's capabilities to ground its generation on external multimodal context. Subsequently, we fine-tune the adapted model on diverse image generation tasks that requires vision-language understanding (e.g., subject-driven generation, etc.), each paired with a multi-modal instruction encapsulating the task's essence. Human evaluation on various image generation datasets reveals that instruct-imagen matches or surpasses prior task-specific models in-domain and demonstrates promising generalization to unseen and more complex tasks.
Time Projection Chamber for GADGET II

Background: The established GADGET detection system, designed for measuring weak, low-energy $\beta$-delayed proton decays, features a gaseous Proton Detector with MICROMEGAS readout for calorimetric particle detection, surrounded by a Segmented Germanium Array for high-resolution prompt $\gamma$-ray detection. Purpose: To upgrade GADGET's Proton Detector to operate as a compact Time Projection Chamber (TPC) for the detection, 3D imaging and identification of low-energy $\beta$-delayed single- and multi-particle emissions mainly of interest to astrophysical studies. Method: A new high granularity MM board with 1024 pads has been designed, fabricated, installed and tested. A high-density data acquisition system based on Generic Electronics for TPCs has been installed and optimized to record and process the gas avalanche signals collected on the readout pads. The TPC's performance has been tested using a $^{220}$Rn $\alpha$-particle source and cosmic-ray muons. In addition, decay events in the TPC have been simulated by adapting the ATTPCROOT data analysis framework. Further, a novel application of 2D convolutional neural networks for GADGET II event classification is introduced. Results: The GADGET II TPC is capable of detecting and identifying $\alpha$-particles, as well as measuring their track direction, range, and energy. It has also been demonstrated that the GADGET II TPC is capable of tracking cosmic-ray muons. In addition to being one of the first generation of micro pattern gaseous detectors to utilize a resistive anode applied to low-energy nuclear physics, the GADGET II TPC will also be the first TPC surrounded by a high-efficiency array of high-purity germanium $\gamma$-ray detectors. \textbf{Conclusions:} The TPC of GADGET II has been designed, fabricated, tested, and is ready for operation at the FRIB for radioactive beam-line experiments.
On the Hilbert Space of Dyons

We revisit the construction of the Hilbert space of non-relativistic particles moving in three spatial dimensions. This is given by the space of sections of a line bundle that can in general be topologically non-trivial. Such bundles are classified by a set of integers--one for each pair of particles--and arise physically when we describe the interactions of dyons, particles which carry both electric and magnetic charges. The choice of bundle fixes the representation of the Euclidean group carried by the Hilbert space. These representations are shown to recover the 'pairwise helicity' formalism recently discussed in the literature.
Estimating continuous data of wrist joint angles using ultrasound images

Ultrasound imaging has recently been introduced as a sensing interface for joint motion estimation. The use of ultrasound images as an estimation method is expected to improve the control performance of assistive devices and human--machine interfaces. This study aimed to estimate continuous wrist joint angles using ultrasound images. Specifically, in an experiment, joint angle information was obtained during extension--flexion movements, and ultrasound images of the associated muscles were acquired. Using the features obtained from ultrasound images, a multivariate linear regression model was used to estimate the joint angles. The coordinates of the feature points obtained using optical flow from the ultrasound images were used as explanatory variables of the multivariate linear regression model. The model was trained and tested for each trial by each participant to verify the estimation accuracy. The results show that the mean and standard deviation of the estimation accuracy for all trials were root mean square error (RMSE)=1.82 $\pm$ 0.54 deg and coefficient of determination (R2)=0.985 $\pm$ 0.009. Our method achieves a highly accurate estimation of joint angles compared with previous studies using other signals, such as surface electromyography, while the multivariate linear regression model is simple and both computational and model training costs are low.
Signal Detection for Ultra-Massive MIMO: An Information Geometry Approach

In this paper, we propose an information geometry approach (IGA) for signal detection (SD) in ultra-massive multiple-input multiple-output (MIMO) systems. We formulate the signal detection as obtaining the marginals of the a posteriori probability distribution of the transmitted symbol vector. Then, a maximization of the a posteriori marginals (MPM) for signal detection can be performed. With the information geometry theory, we calculate the approximations of the a posteriori marginals. It is formulated as an iterative m-projection process between submanifolds with different constraints. We then apply the central-limit-theorem (CLT) to simplify the calculation of the m-projection since the direct calculation of the m-projection is of exponential-complexity. With the CLT, we obtain an approximate solution of the m-projection, which is asymptotically accurate. Simulation results demonstrate that the proposed IGA-SD emerges as a promising and efficient method to implement the signal detector in ultra-massive MIMO systems.
Use of Satellite Imagery in Archaeological Surveys

Abstract One of the problems faced by archaeologists engaged in field surveys is the need to gain an overall view of a study area together with data on the character of its terrain. Air-photographs have for 50 years or more provided a valuable source of such data but they are often difficult to obtain or too expensive to commission. Satellite imagery has since the early 1970s been available and it is suggested that such imagery together with well tried methods of terrain analysis and land evaluation could provide the basis of economical strategies for archaeological surveys. It is also demonstrated that such an approach can help in predicting the level of settlement in different types of terrain.
New multivariate Gini's indices

The Gini's mean difference was defined as the expected absolute difference between a random variable and its independent copy. The corresponding normalized version, namely Gini's index, denotes two times the area between the egalitarian line and the Lorenz curve. Both are dispersion indices because they quantify how far a random variable and its independent copy are. Aiming to measure dispersion in the multivariate case, we define and study new Gini's indices. For the bivariate case we provide several results and we point out that they are"dependence-dispersion"indices. Covariance representations are exhibited, with an interpretation also in terms of conditional distributions. Further results, bounds and illustrative examples are discussed too. Multivariate extensions are defined, aiming to apply both indices in more general settings. Then, we define efficiency Gini's indices for any semi-coherent system and we discuss about their interpretation. Empirical versions are considered in order as well to apply multivariate Gini's indices to data.
Simplified Information Geometry Approach for Massive MIMO-OFDM Channel Estimation - Part I: Algorithm and Fixed Point Analysis

In this two-part paper, we investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In Part I, we revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix in the massive MIMO-OFDM channel estimation and the asymptotic analysis, we find that the second-order natural parameters of the distributions on all the auxiliary manifolds are equivalent to each other at each iteration of IGA, and the first-order natural parameters of the distributions on all the auxiliary manifolds are asymptotically equivalent to each other at the fixed point of IGA. Motivated by these results, we simplify the iterative process of IGA and propose a simplified IGA for massive MIMO-OFDM channel estimation. It is proved that at the fixed point, the a posteriori mean obtained by the simplified IGA is asymptotically optimal. The simplified IGA allows efficient implementation with fast Fourier transformation (FFT). Simulations confirm that the simplified IGA can achieve near the optimal performance with low complexity in a limited number of iterations.
MAGNETIC RESPONSE OF TWISTED BILAYER GRAPHENE

We consider the Bistritzer-MacDonald model for twisted bilayer graphene (TBG) in external magnetic fields and study spectral properties of the model and a semiclassical description of the density of states (DOS). The explicit expansion of the DOS enables us to study magnetic response properties such as magnetic oscillations which includes Shubnikov-de Haas and de Haas van Alphen oscillations as well as the quantum Hall effect. In particular, we find very different behaviour when studying tunnelling interactions of type AA’/BB’ compared to AB’/BA’ (chiral/anti-chiral interactions) shedding light on experimentally observed degeneracies of Landau levels.