Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical proofs for the properties of the probability mass function of the Poisson distribution of order $k$

The Poisson distribution of order $k$ is a special case of a compound Poisson distribution. For $k=1$ it is the standard Poisson distribution. Our main result is a proof that for sufficiently small values of the rate parameter $\lambda$, the probability mass function (pmf) decreases monotonically for all $n\ge k$ (it is known that the pmf increases strictly for $1\le n \le k$, for fixed $k\ge2$ and all $\lambda>0$). The second main result is a partial proof that the difference (mean $-$ mode) does not exceed $k$. The term `partial proof' signifies that the derivation is conditional on an assumption which, although plausible and supported by numerical evidence, is as yet not proved. This note also presents new inequalities, and sharper bounds for some published inequalities, for the Poisson distribution of order $k$.
CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Models

Joint source-channel coding schemes based on deep neural networks (DeepJSCC) have recently achieved remarkable performance for wireless image transmission. However, these methods usually focus only on the distortion of the reconstructed signal at the receiver side with respect to the source at the transmitter side, rather than the perceptual quality of the reconstruction which carries more semantic information. As a result, severe perceptual distortion can be introduced under extreme conditions such as low bandwidth and low signal-to-noise ratio. In this work, we propose CommIN, which views the recovery of high-quality source images from degraded reconstructions as an inverse problem. To address this, CommIN combines Invertible Neural Networks (INN) with diffusion models, aiming for superior perceptual quality. Through experiments, we show that our CommIN significantly improves the perceptual quality compared to DeepJSCC under extreme conditions and outperforms other inverse problem approaches used in DeepJSCC.
Optimistic Online Caching for Batched Requests

In this paper we study online caching problems where predictions of future requests, e.g., provided by a machine learning model, are available. Typical online optimistic policies are based on the Follow-The-Regularized-Leader algorithm and have higher computational cost than classic ones like LFU, LRU, as each update of the cache state requires to solve a constrained optimization problem. In this work we analysed the behaviour of two different optimistic policies in a batched case, i.e., when the cache is updated less frequently in order to amortize the update cost over time or over multiple requests. Experimental results show that such an optimistic batched approach outperforms classical caching policies both on stationary and real traces.
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model

Multimodal large language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-textual data, including images and videos. This study seeks to extend the application of MLLMs to the realm of autonomous driving by introducing DriveGPT4, a novel interpretable end-to-end autonomous driving system based on LLMs. Capable of processing multi-frame video inputs and textual queries, DriveGPT4 facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users. Furthermore, DriveGPT4 predicts low-level vehicle control signals in an end-to-end fashion. These advanced capabilities are achieved through the utilization of a bespoke visual instruction tuning dataset, specifically tailored for autonomous driving applications, in conjunction with a mix-finetuning training strategy. DriveGPT4 represents the pioneering effort to leverage LLMs for the development of an interpretable end-to-end autonomous driving solution. Evaluations conducted on the BDD-X dataset showcase the superior qualitative and quantitative performance of DriveGPT4. Additionally, the fine-tuning of domain-specific data enables DriveGPT4 to yield close or even improved results in terms of autonomous driving grounding when contrasted with GPT4-V. The code and dataset will be publicly available.
Geochemical Types, Petrology, and Genesis of Late Cenozoic Volcanic Rocks from the Kurile-Kamchatka Island-Arc System

The Kurile-Kamchatka Late Cenozoic volcanic rocks can be categorized as magmatic series of island arc and within-plate geochemical types, respectively. These types clearly can be identified on geochemical discriminant diagrams. Across-arc geochemical, mineralogical, and Sr-Nd-isotope zoning is characteristic of island-arc volcanics, but is not found for within-plate volcanics. Such zoning probably is the result of modification of fluid-phase composition, which originates in the underlying subducting plate (as a result of dehydration of water-bearing secondary minerals) and rises into the zone of island-arc magma generation in the mantle wedge. Along-arc Sr, Be, H. , and 0 isotopic zoning is noted for island-arc volcanics. Probably it is associated with various levels of island-arc magma contamination. 10Be data for modern island-arc lavas attest to the fact that young pelagic sediments from the subducting plate participate in island-arc magma genesis. Within-plate lavas are found in the region north of Ava.
Meteorological results from the surface of Mars: Viking 1 and 2

We deal here primarily with the surface meteorological data for both Viking landers during the nominal missions (44 sols for lander 1 and 61 sols for lander 2). The diurnal patterns of wind, temperature, and pressure were strongly similar from sol to sol, as was expected in the summer. The chief characteristics of the wind data are that winds were light (a few meters per second), with a complex hodograph at VL-1 dominated by counterclockwise turning of the wind and a simpler hodograph at VL-2 marked by clockwise turning of the wind. This repetitive pattern of wind has begun to break down at VL-2 with advancing season, and several episodes of protracted northeasterly winds have occurred. Some of these are associated with lower than normal temperatures. Examples are given of wind and temperature traces over short periods, illustrating the effects of convection, static stability, and lander interference. We present a theoretical argument based upon the horizontal scale dictated by heating of slopes and upon vertical mixing of momentum to explain the different sense of rotation of the wind vectors at the two sites. Analysis of the semidiurnal pressure oscillation suggests that absorption of solar radiation is an important thermal drive but that convective heat flux from the surface is also significant. The seasonal variation of pressure extending past the end of the nominal missions shows a decrease of pressure to a minimum at Ls ≈ 149° with a rapid rise thereafter. This is clearly due to condensation and sublimation of CO2 on and from the southern polar cap.
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases towards the node degree: they usually perform satisfactorily on high-degree nodes with rich neighbor information but struggle with low-degree nodes. Existing works tackle this problem by deriving either designated GNN architectures or training strategies specifically for low-degree nodes. Though effective, these approaches unintentionally create an artificial out-of-distribution scenario, where models mainly or even only observe low-degree nodes during the training, leading to a downgraded performance for high-degree nodes that GNNs originally perform well at. In light of this, we propose a test-time augmentation framework, namely GraphPatcher, to enhance test-time generalization of any GNNs on low-degree nodes. Specifically, GraphPatcher iteratively generates virtual nodes to patch artificially created low-degree nodes via corruptions, aiming at progressively reconstructing target GNN's predictions over a sequence of increasingly corrupted nodes. Through this scheme, GraphPatcher not only learns how to enhance low-degree nodes (when the neighborhoods are heavily corrupted) but also preserves the original superior performance of GNNs on high-degree nodes (when lightly corrupted). Additionally, GraphPatcher is model-agnostic and can also mitigate the degree bias for either self-supervised or supervised GNNs. Comprehensive experiments are conducted over seven benchmark datasets and GraphPatcher consistently enhances common GNNs' overall performance by up to 3.6% and low-degree performance by up to 6.5%, significantly outperforming state-of-the-art baselines. The source code is publicly available at https://github.com/jumxglhf/GraphPatcher.
Stability conditions for mean-field limiting vorticities of the Ginzburg-Landau equations in 2D

We analyse the limit of stable solutions to the Ginzburg-Landau (GL) equations when $\varepsilon$, the inverse of the GL parameter, goes to zero and in a regime where the applied magnetic field is of order $|\log \varepsilon |$ whereas the total energy is of order $|\log \varepsilon|^2$. In order to do that we pass to the limit in the second inner variation of the GL energy. The main difficulty is to understand the convergence of quadratic terms involving derivatives of functions converging only weakly in $H^1$. We use an assumption of convergence of energies, the limiting criticality conditions obtained by Sandier-Serfaty by passing to the limit in the first inner variation and properties of limiting vorticities to find the limit of all the desired quadratic terms. At last we investigate the limiting stability condition we have obtained. In the case with magnetic field we study an example of an admissible limiting vorticity supported on a line in a square $\Omega=(-L,L)^2$ and show that if $L$ is small enough this vorticiy satisfies the limiting stability condition whereas when $L$ is large enough it stops verifying that condition. In the case without magnetic field we use a result of Iwaniec-Onninen to prove that every measure in $H^{-1}(\Omega)$ satisfying the first order limiting criticality condition also verifies the second order limiting stability condition.
Monitoring the high‐altitude cusp with the Low Energy Neutral Atom imager: Simultaneous observations from IMAGE and Polar

[1] The Low Energy Neutral Atom (LENA) imager on the IMAGE spacecraft in the dayside magnetosphere can detect neutral particles that are emitted in the magnetosheath flow. During a period of dynamic pressure of 4–6 nPa and interplanetary magnetic field (IMF) Bz of −5 to 3 nT on 12 April 2001, LENA on IMAGE at (XGSM, YGSM, ZGSM) ∼ (4 RE, 0 RE, 6 RE) observed significant emission in the direction of the high-latitude magnetosheath. Detailed analyses have revealed that the high-latitude sheath emission consists of two parts: the stable emission at the higher latitudes and the lower-latitude emission that occurs on and off. During the interval of this event, the Polar spacecraft was located at somewhat lower latitudes than IMAGE in similar noon meridian, and the plasma observations with the Thermal Ions Dynamic Experiment showed that the entry of the cusp ions happens in concurrence with the appearance of the lower-latitude LENA emission. This coincidence strongly suggests that the cusp ions flowing earthward charge exchange with the hydrogen exosphere. For the higher-latitude emission, its stability suggests that the source is associated with the structure persistently existing, which is consistent with the recent result showing that the sheath flow in the cusp indentation can create neutral atom emissions. Comparison of the LENA emission and ACE solar wind suggests that the lower-latitude LENA emission occurs during the southward tilting of dawnward IMF, indicating that this emission is associated with the earthward ion flux along the newly reconnected field lines. Hence this unique event for the simultaneous observations strongly suggests that LENA monitors the entry of the ions in the cusp, which is triggered by the southward tilting of the IMF, and that the significant flux of the cusp ion entry occurs equatorward of and separately from the cusp indentation.
Application of frozen large-scale models to multimodal task-oriented dialogue

In this study, we use the existing Large Language Models ENnhanced to See Framework (LENS Framework) to test the feasibility of multimodal task-oriented dialogues. The LENS Framework has been proposed as a method to solve computer vision tasks without additional training and with fixed parameters of pre-trained models. We used the Multimodal Dialogs (MMD) dataset, a multimodal task-oriented dialogue benchmark dataset from the fashion field, and for the evaluation, we used the ChatGPT-based G-EVAL, which only accepts textual modalities, with arrangements to handle multimodal data. Compared to Transformer-based models in previous studies, our method demonstrated an absolute lift of 10.8% in fluency, 8.8% in usefulness, and 5.2% in relevance and coherence. The results show that using large-scale models with fixed parameters rather than using models trained on a dataset from scratch improves performance in multimodal task-oriented dialogues. At the same time, we show that Large Language Models (LLMs) are effective for multimodal task-oriented dialogues. This is expected to lead to efficient applications to existing systems.
An OrthoBoXY-method for various alternative box geometries.

We have shown in a recent contribution [Busch and Paschek, J. Phys. Chem. B, 2023 127, 7983-7987] that for molecular dynamics (MD) simulations of isotropic fluids based on orthorhombic periodic boundary conditions with "magical" box length ratios of Lz/Lx = Lz/Ly = 2.7933596497, the computed self-diffusion coefficients Dx and Dy in x- and y-direction become system size independent. They thus represent the true self-diffusion coefficient D0 = (Dx + Dy)/2, while the shear viscosity can be determined from diffusion coefficients in x-, y-, and z-direction, using the expression η = kBT·8.1711245653/[3πLz(Dx + Dy - 2Dz)]. Here we present a more generalized version of this "OrthoBoXY"-approach, which can be applied to any orthorhombic MD box of any shape. In particular, we would like to test, how the efficiency is affected by using a shape more akin to the cubic form, albeit with different box length ratios Lx/Lz ≠ Ly/Lz and Lx < Ly < Lz. We use NVT and NpT simulations of systems of 1536 TIP4P/2005 water molecules as a benchmark and explore different box geometries to determine the influence of the box shape on the computed statistical uncertainties for D0 and η. Moreover, another "magical" set of box length ratios is discovered with Ly/Lz = 0.57804765578 and Lx/Lz = 0.33413909235, where the self-diffusion coefficient in x-direction becomes system size independent, such that D0 = Dx.
Towards Robust Cardiac Segmentation using Graph Convolutional Networks

Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer variability. However, this architecture still generates large outliers that are often anatomically incorrect. This work uses the concept of graph convolutional neural networks that predict the contour points of the structures of interest instead of labeling each pixel. We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional architecture and an evaluation of clinical measurements on the clinical HUNT4 dataset. Finally, we propose to use the inter-model agreement of the U-Net and the graph network as a predictor of both the input and segmentation quality. We show this predictor can detect out-of-distribution and unsuitable input images in real-time. Source code is available online: https://github.com/gillesvntnu/GCN_multistructure
Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance

Training graph neural networks (GNNs) on large-scale graph data holds immense promise for numerous real-world applications but remains a great challenge. Several disk-based GNN systems have been built to train large-scale graphs in a single machine. However, they often fall short in terms of performance, especially when training on terabyte-scale graphs. This is because existing disk-based systems either overly focus on minimizing the number of SSD accesses or do not fully overlap SSD accesses with GNN training, thus resulting in substantial unnecessary overhead on the CPU side and then low GPU utilization. To this end, we propose Helios, a system that can train GNN on terabyte graphs in a single machine while achieving throughput comparable with in-memory systems. To achieve this, we first present a GPU-initiated asynchronous disk IO stack, allowing the GPU to directly access graph data on SSD. This design only requires about 30% GPU cores to reach the almost maximal disk IO throughput and wastes no GPU cores between IO submission and IO completion such that the majority of GPU cores are left for other GNN kernels. Second, we design a GPU-managed heterogeneous cache that extends the cache hierarchy to heterogeneous CPU and GPU memory and thus enhances cache lookup throughput significantly by GPU parallelism. Finally, we build a deep GNN-aware pipeline that seamlessly integrates the computation and communication phases of the entire GNN training process, maximizing the utility of GPU computation cycles. Experimental results demonstrate that Helios can match the training throughput of in-memory GNN systems, even for terabyte-scale graphs. Remarkably, Helios surpasses the state-of-the-art GPU-managed baselines by up to 6.43x and exceeds CPU-managed baselines by over 182x on all terabyte-scale graphs.
Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss

Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer representations. Previous works have used trainable prototypes to help improve test accuracy of SCL when training under imbalance. In this work, we propose the use of fixed prototypes to help engineering the feature geometry when training with SCL. We gain further insights by considering a limiting scenario where the number of prototypes far outnumber the original batch size. Through this, we establish a connection to CE loss with a fixed classifier and normalized embeddings. We validate our findings by conducting a series of experiments with deep neural networks on benchmark vision datasets.
Programmable order by disorder effect and underlying phases through dipolar quantum simulators

In this work, we study two different quantum simulators through various theoretical and numerical tools. Our first result provides knowledge upon the quantum order by disorder effect of the $S=1/2$ system, which is programmable in a quantum simulator composed of circular Rydberg atoms. When the total magnetization is zero, a set of sub-extensive degenerate ground states is present in the classical limit, composed of continuous strings whose configuration enjoys a large degree of freedom. Among all possible configurations, we focus on the stripe (up and down spins aligning straightly) and kinked (up and down spins forming zigzag spin chains) patterns. Adopting the real space perturbation theory, we estimate the leading order energy correction when the nearest-neighbor spin exchange coupling, $J$, is considered, and the overall model becomes an effective XXZ model with a spatial anisotropy. Our calculation demonstrates a lifting of the degeneracy, favoring the stripe configuration. When $J$ becomes larger, we adopt the infinite projected entangled-pair state and numerically check the effect of degeneracy lifting, showing that even when the spin exchange coupling term is strong the stripe pattern is still favored. Next, we study the dipolar bosonic model with tilted polar angle which can be realized through a quantum simulator composed of cold atomic gas with dipole-dipole interaction in an optical lattice. By placing the atoms in a triangular lattice and tilting the polar angle, the diagonal anisotropy can also be realized in the bosonic system. With our cluster mean-field theory calculation, we provide various phase diagrams with different tilted angles, showing the abundant underlying phases including the supersolid. Our proposals indicate realizable scenarios through quantum simulators in studying the quantum effect as well as extraordinary phases.
Exact Ground States and Phase Diagram of the Quantum Compass Model under an in-plane Field

We consider the square lattice $S$=1/2 quantum compass model (QCM) parameterized by $J_x, J_z$, under a field, $\mathbf{h}$, in the $x$-$z$ plane. At the special field value, $(h_x^\star,h_z^\star)$=$2S(J_x,J_z)$, we show that the QCM Hamiltonian may be written in a form such that two simple product states can be identified as exact ground-states, below a gap. Exact excited states can also be found. The exact product states are characterized by a staggered vector chirality, attaining a non-zero value in the surrounding phase. The resulting gapped phase, which we denote by $SVC$ occupies most of the in-plane field phase diagram. For some values of $h_x>h_z$ and $h_z>h_x$ at the edges of the phase diagram, we have found transitions between the $SVC$ phase and phases of weakly-coupled Ising-chain states, $Z$ and $X$. In zero field, the QCM is known to have an emergent sub-extensive ground-state degeneracy. As the field is increased from zero, we find that this degeneracy is partially lifted, resulting in bond-oriented spin-stripe states, $L$ and $R$, which are each separated from one another and the $SVC$ phase by first-order transitions. Our findings are important for understanding the field dependent phase diagram of materials with predominantly directionally-dependent Ising interactions.
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression

Existing statistical learning guarantees for general kernel regressors often yield loose bounds when used with finite-rank kernels. Yet, finite-rank kernels naturally appear in several machine learning problems, e.g.\ when fine-tuning a pre-trained deep neural network's last layer to adapt it to a novel task when performing transfer learning. We address this gap for finite-rank kernel ridge regression (KRR) by deriving sharp non-asymptotic upper and lower bounds for the KRR test error of any finite-rank KRR. Our bounds are tighter than previously derived bounds on finite-rank KRR, and unlike comparable results, they also remain valid for any regularization parameters.