Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of an basis-projected layer for sparse datasets in deep learning training using gc-ms spectra as a case study

Deep learning (DL) models encompass millions or even billions of parameters and learn complex patterns from big data. However, not all data are initially stored in a suitable formation to effectively train a DL model, e.g., gas chromatography-mass spectrometry (GC-MS) spectra and DNA sequence. These datasets commonly contain many zero values, and the sparse data formation causes difficulties in optimizing DL models. A DL module called the basis-projected layer (BPL) was proposed to mitigate the issue by transforming the sparse data into a dense representation. The transformed data is expected to facilitate the gradient calculation and finetuned process in a DL training process. The dataset, example of a sparse dataset, contained 362 specialty coffee odorant spectra detected from GC-MS. The BPL layer was placed at the beginning of the DL model. The tunable parameters in the layer were learnable projected axes that were the bases of a new representation space. The layer rotated these bases when its parameters were updated. When the number of the bases was the same as the original dimension, the increasing percentage of the F1 scores was 8.56%. Furthermore, when the number was set as 768 (the original dimension was 490), the increasing percentage of the F1 score was 11.49%. The layer not only maintained the model performance and even constructed a better representation space in analyzing sparse datasets.
Measurements and modeling of induced flow in collective vertical migration

Hydrodynamic interactions among swimming or flying organisms can lead to complex flows on the scale of the group. These emergent fluid dynamics are often more complex than a linear superposition of individual organism flows, especially at intermediate Reynolds numbers. This paper presents an approach to estimate the flow induced by multiple swimmer wakes in proximity using an analytical model that conserves mass and momentum in the aggregation. This analytical model was informed by and validated with empirical measurements of induced vertical migrations of brine shrimp, $\textit{Artemia salina}$. The response of individual swimmers to ambient background flow and light intensity was evaluated. In addition, the time-resolved three-dimensional spatial configuration of the swimmers was measured using a recently developed laser scanning system. Computational experiments using the analytical model found that the induced flow at the front of the aggregation was insensitive to the presence of downstream swimmers, with the induced flow reaching an asymptote beyond a threshold aggregation length. Closer swimmer spacing led to higher induced flow, in some cases leading to model predictions of induced flow exceeding swimmer speeds required to maintain a stable spatial configuration. This result was reconciled by comparing two different models for the near-wake of each swimmer. Our results demonstrate that aggregation-scale flows result from a complex, yet predictable interplay amongst organism-scale wake structure, swimmer spacing and configuration, and aggregation size.
Hydrodynamic behavior near dynamical criticality of a facilitated conservative lattice gas

We investigate a $2d$-conservative lattice gas exhibiting a dynamical active-absorbing phase transition with critical density $\rho_c$. We derive the hydrodynamic equation for this model, showing that all critical exponents governing the large scale behavior near criticality can be obtained from two independent ones. We show that as the supercritical density approaches criticality, distinct length scales naturally appear. Remarkably, this behavior is different from the subcritical one. Numerical simulations support the critical relations and the scale separation.
Detecting the third family of compact stars with normalizing flows

We explore the anomaly detection framework based on Normalizing Flows (NF) models introduced in \cite{PhysRevC.106. 065802} to detect the presence of a large (destabilising) dense matter phase transition in neutron star (NS) observations of masses and radii, and relate the feasibility of detection with parameters of the underlying mass-radius sequence, which is a functional of the dense matter equation of state. Once trained on simulated data featuring continuous $M(R)$ solutions (i.e., no phase transitions), NF is used to determine the likelihood of a first-order phase transition in a given set of $M(R)$ observations featuring a discontinuity, i.e., perform the anomaly detection. Different mock test sets, featuring two branch solutions in the $M(R)$ diagram, were parameterized by the NS mass at which the phase transition occurs, $M_c$, and the radius difference between the heaviest hadronic star and lightest hybrid star, $\Delta R$. We analyze the impact of these parameters on the NF performance in detecting the presence of a first-order phase transition. Among the results, we report that given a set of 15 stars with radius uncertainty of $0.2$ km, a detection of a two-branch solution is possible with 95\% accuracy if $\Delta R>0.4$ km.
pARam: Leveraging Parametric Design in Extended Reality to Support the Personalization of Artifacts for Personal Fabrication

Extended Reality (XR) allows in-situ previewing of designs to be manufactured through Personal Fabrication (PF). These in-situ interactions exhibit advantages for PF, like incorporating the environment into the design process. However, design-for-fabrication in XR often happens through either highly complex 3D-modeling or is reduced to rudimentary adaptations of crowd-sourced models. We present pARam, a tool combining parametric designs (PDs) and XR, enabling in-situ configuration of artifacts for PF. In contrast to modeling- or search-focused approaches, pARam supports customization through embodied and practical inputs (e.g., gestures, recommendations) and evaluation (e.g., lighting estimation) without demanding complex 3D-modeling skills. We implemented pARam for HoloLens 2 and evaluated it (n=20), comparing XR and desktop conditions. Users succeeded in choosing context-related parameters and took their environment into account for their configuration using pARam. We reflect on the prospects and challenges of PDs in XR to streamline complex design methods for PF while retaining suitable expressivity.
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency

As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks which can be achieved by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy which can result in more robust models. This adversarial robustness comes with additional computational costs required to design adversarial attacks during training. The two objectives -- adversarial robustness and computational efficiency -- then appear to be in conflict of each other. In this work, we explore the effects of two different model compression methods -- structured weight pruning and quantization -- on adversarial robustness. We specifically explore the effects of fine-tuning on compressed models, and present the trade-off between standard fine-tuning and adversarial fine-tuning. Our results show that compression does not inherently lead to loss in model robustness and adversarial fine-tuning of a compressed model can yield large improvement to the robustness performance of models. We present experiments on two benchmark datasets showing that adversarial fine-tuning of compressed models can achieve robustness performance comparable to adversarially trained models, while also improving computational efficiency.
Enhanced One-Step Neville Algorithm with Access to the Convergence Rate

The recursive Neville algorithm allows one to calculate interpolating functions recursively. Upon a judicious choice of the abscissas used for the interpolation (and extrapolation), this algorithm leads to a method for convergence acceleration. For example, one can use the Neville algorithm in order to successively eliminate inverse powers of the upper limit of the summation from the partial sums of a given, slowly convergent input series. Here, we show that, for a particular choice of the abscissas used for the extrapolation, one can replace the recursive Neville scheme by a simple one-step transformation, while also obtaining access to subleading terms for the transformed series after convergence acceleration. The matrix-based, unified formulas allow one to estimate the rate of convergence of the partial sums of the input series to their limit. In particular, Bethe logarithms for hydrogen are calculated to 100 decimal digits.
Folding $\pi$

It is well known that the set of origami constructible numbers is larger than the classical straight-edge and compass constructible numbers. However, the Huzita-Justin-Hatori origami constructible numbers remain algebraic so that the transcendental number $\pi$ can only be approximated using a finite number of straight line folds. Using these methods we give a convergent sequence for folding $\pi$ as well as other methods to approximate $\pi$. Folding along curved creases, however, allows for the construction of transcendental numbers. We here give a method to construct $\pi$ exactly by folding along a parabola, and we discuss generalizations for folding other transcendental numbers such as $\Gamma(1/4)$.
Observation and differential cross section measurement of neutral current DIS events with an empty hemisphere in the Breit frame

The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged boson defines the coordinate system such that the struck quark is back-scattered along the virtual photon momentum direction. In Quantum Chromodynamics (QCD), higher order perturbative or non-perturbative effects can change this picture drastically. As Bjorken-$x$ decreases below one half, a rather peculiar event signature is predicted with increasing probability, where no radiation is present in one of the two Breit-frame hemispheres and all emissions are to be found in the other hemisphere. At higher orders in $\alpha_s$ or in the presence of soft QCD effects, predictions of the rate of these events are far from trivial, and that motivates measurements with real data. We report on the first observation of the empty current hemisphere events in electron-proton collisions at the HERA collider using data recorded with the H1 detector at a center-of-mass energy of 319 GeV. The fraction of inclusive neutral-current DIS events with an empty hemisphere is found to be $0.0112 \pm 3.9\,\%_\text{stat} \pm 4.5\,\%_\text{syst} \pm 1.6\,\%_\text{mod}$ in the selected kinematic region of $150<Q^2<1500$ GeV$^2$ and inelasticity $0.14<y<0.7$. The data sample corresponds to an integrated luminosity of 351.1 pb$^{-1}$, sufficient to enable differential cross section measurements of these events. The results show an enhanced discriminating power at lower Bjorken-$x$ among different Monte Carlo event generator predictions.
The Algebraic Page Curve

The Page curve describing the process of black hole evaporation is derived in terms of a family, parametrized in terms of the evaporation time, of finite type II_1 factors, associated, respectively, to the entanglement wedges of the black hole and the radiation. The so defined Page curve measures the relative continuous dimension of the black hole and the radiation along the evaporation process. The transfer of information is quantitatively defined in terms of the Murray von Neumann parameter describing the change of the spatial properties of the factors during the evaporation. In the simplest case the generator of the evaporation process is defined in terms of the action of the fundamental group of the hyperfinite type II_1 factor. In this setup the Page curve describes a phase transition with the transfer of information as order parameter. We discuss the limits of either a type I or a type III description of the black hole evaporation.
D-YOLO a robust framework for object detection in adverse weather conditions

Adverse weather conditions including haze, snow and rain lead to decline in image qualities, which often causes a decline in performance for deep-learning based detection networks. Most existing approaches attempts to rectify hazy images before performing object detection, which increases the complexity of the network and may result in the loss in latent information. To better integrate image restoration and object detection tasks, we designed a double-route network with an attention feature fusion module, taking both hazy and dehazed features into consideration. We also proposed a subnetwork to provide haze-free features to the detection network. Specifically, our D-YOLO improves the performance of the detection network by minimizing the distance between the clear feature extraction subnetwork and detection network. Experiments on RTTS and FoggyCityscapes datasets show that D-YOLO demonstrates better performance compared to the state-of-the-art methods. It is a robust detection framework for bridging the gap between low-level dehazing and high-level detection.
Efficient size-prescribed k-core search

$k$-core is a subgraph where every node has at least $k$ neighbors within the subgraph. The $k$-core subgraphs has been employed in large platforms like Network Repository to comprehend the underlying structures and dynamics of the network. Existing studies have primarily focused on finding $k$-core groups without considering their size, despite the relevance of solution sizes in many real-world scenarios. This paper addresses this gap by introducing the size-prescribed $k$-core search (SPCS) problem, where the goal is to find a subgraph of a specified size that has the highest possible core number. We propose two algorithms, namely the {\it TSizeKcore-BU} and the {\it TSizeKcore-TD}, to identify cohesive subgraphs that satisfy both the $k$-core requirement and the size constraint. Our experimental results demonstrate the superiority of our approach in terms of solution quality and efficiency. The {\it TSizeKcore-BU} algorithm proves to be highly efficient in finding size-prescribed $k$-core subgraphs on large datasets, making it a favorable choice for such scenarios. On the other hand, the {\it TSizeKcore-TD} algorithm is better suited for small datasets where running time is less critical.
Governance of Generative Artificial Intelligence for Companies

Generative Artificial Intelligence (GenAI), specifically large language models like ChatGPT, has swiftly entered organizations without adequate governance, posing both opportunities and risks. Despite extensive debates on GenAI's transformative nature and regulatory measures, limited research addresses organizational governance, encompassing technical and business perspectives. This review paper fills this gap by surveying recent works. It goes beyond mere summarization by developing a framework for GenAI governance within companies. Our framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities and mitigate risks associated with GenAI integration. This research contributes a focused approach to GenAI governance, offering practical insights for companies navigating the challenges of responsible AI adoption. It is also valuable for a technical audience to broaden their perspective as increasingly ethical and business concerns gain in prevalence and allow them to identify novel research directions.
Spectral inequalities for Schr\"odinger equations with various potentials

We study the spectral inequalities of Schr\"odinger operator in the whole space for different potentials, which can be power growth or continuously vanishing at infinity. The spectral inequalities quantitatively depend on the density of the sensor sets with positive measure, growth rate of the potentials and spectrum (or eigenvalues). One important component in the poof is the adaptation of propagation of smallness argument for gradients in \cite{LM18}. As an application, we apply the spectral inequalities to obtain quantitative observability inequalities for heat equations.
Enabling Waypoint Generation for Collaborative Robots using LLMs and Mixed Reality

Programming a robotic is a complex task, as it demands the user to have a good command of specific programming languages and awareness of the robot's physical constraints. We propose a framework that simplifies robot deployment by allowing direct communication using natural language. It uses large language models (LLM) for prompt processing, workspace understanding, and waypoint generation. It also employs Augmented Reality (AR) to provide visual feedback of the planned outcome. We showcase the effectiveness of our framework with a simple pick-and-place task, which we implement on a real robot. Moreover, we present an early concept of expressive robot behavior and skill generation that can be used to communicate with the user and learn new skills (e.g., object grasping).
HEX: High-pressure Elemental Xstals, a complete Database

This paper introduces the HEX (High-pressure Elemental Xstals) database, a complete database of the ground-state crystal structures of the first 57 elements of the periodic table, from H to La, at 0, 100, 200 and 300 GPa. HEX aims to provide a unified reference for high-pressure research, by compiling all available experimental information on elements at high pressure, and complementing it with the results of accurate evolutionary crystal structure prediction runs based on Density Functional Theory. Besides offering a much-needed reference, our work also serves as a benchmark of the accuracy of current ab-initio methods for crystal structure prediction. We find that, in 98 % of the cases in which experimental information is available, ab-initio crystal structure prediction yields structures which either coincide or are degenerate in enthalpy to within 300 K with experimental ones. The main manuscript contains synthetic tables and figures, while the Crystallographic Information File (cif) for all structures will be available on a figshare online repository when the paper will be published.
Reverse em-problem based on Bregman divergence and its application to classical and quantum information theory

The recent paper (IEEE Trans. IT 69, 1680) introduced an analytical method for calculating the channel capacity without the need for iteration. This method has certain limitations that restrict its applicability. Furthermore, the paper does not provide an explanation as to why the channel capacity can be solved analytically in this particular case. In order to broaden the scope of this method and address its limitations, we turn our attention to the reverse em-problem, proposed by Toyota (Information Geometry, 3, 1355 (2020)). This reverse em-problem involves iteratively applying the inverse map of the em iteration to calculate the channel capacity, which represents the maximum mutual information. However, several open problems remained unresolved in Toyota's work. To overcome these challenges, we formulate the reverse em-problem based on Bregman divergence and provide solutions to these open problems. Building upon these results, we transform the reverse em-problem into em-problems and derive a non-iterative formula for the reverse em-problem. This formula can be viewed as a generalization of the aforementioned analytical calculation method. Importantly, this derivation sheds light on the information geometrical structure underlying this special case. By effectively addressing the limitations of the previous analytical method and providing a deeper understanding of the underlying information geometrical structure, our work significantly expands the applicability of the proposed method for calculating the channel capacity without iteration.
MOTPose: Multi-object 6D Pose Estimation for Dynamic Video Sequences using Attention-based Temporal Fusion

Cluttered bin-picking environments are challenging for pose estimation models. Despite the impressive progress enabled by deep learning, single-view RGB pose estimation models perform poorly in cluttered dynamic environments. Imbuing the rich temporal information contained in the video of scenes has the potential to enhance models ability to deal with the adverse effects of occlusion and the dynamic nature of the environments. Moreover, joint object detection and pose estimation models are better suited to leverage the co-dependent nature of the tasks for improving the accuracy of both tasks. To this end, we propose attention-based temporal fusion for multi-object 6D pose estimation that accumulates information across multiple frames of a video sequence. Our MOTPose method takes a sequence of images as input and performs joint object detection and pose estimation for all objects in one forward pass. It learns to aggregate both object embeddings and object parameters over multiple time steps using cross-attention-based fusion modules. We evaluate our method on the physically-realistic cluttered bin-picking dataset SynPick and the YCB-Video dataset and demonstrate improved pose estimation accuracy as well as better object detection accuracy
Digital isochrons of the world's ocean floor

We have created a digital age grid of the ocean floor with a grid node interval of 6 arc min using a self-consistent set of global isochrons and associated plate reconstruction poles. The age at each grid node was determined by linear interpolation between adjacent isochrons in the direction of spreading. Ages for ocean floor between the oldest identified magnetic anomalies and continental crust were interpolated by estimating the ages of passive continental margin segments from geological data and published plate models. We have constructed an age grid with error estimates for each grid cell as a function of (1) the error of ocean floor ages identified from magnetic anomalies along ship tracks and the age of the corresponding grid cells in our age grid, (2) the distance of a given grid cell to the nearest magnetic anomaly identification, and (3) the gradient of the age grid: i.e., larger errors are associated with high age gradients at fracture zones or other age discontinuities. Future applications of this digital grid include studies of the thermal and elastic structure of the lithosphere, the heat loss of the Earth, ridge-push forces through time, asymmetry of spreading, and providing constraints for seismic tomography and mantle convection models.
Quality factor of dielectric optical resonators

We show analytically that the quality ($Q$) factor of magnetic and electric Mie modes in a lossless dielectric spherical resonator with high refractive index ($n \gg 1$) scales as $n^{2j+1}$ and $n^{2j+3}$ respectively, where $j$ denotes the multipolar order. We numerically validate these results and show that our high-$n$ analytical relation is accurate for the dipolar modes when $n>5$. For higher multipolar orders, the analytical relation becomes valid for increasingly lower $n$. We study the dependence of the $Q$ factor on absorption losses and determine a general functional form that describes the $Q$ factor for all Mie modes for any complex refractive index. Finally, we observe that this functional form predicts a multipolar-dependent singular value of optical gain which gives rise to a lasing condition with an infinite $Q$ factor.