Miracle-dz/UniFAR-FTUs huggingface dataset:
COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving

LiDAR semantic segmentation for autonomous driving has been a growing field of interest in the past few years. Datasets and methods have appeared and expanded very quickly, but methods have not been updated to exploit this new availability of data and continue to rely on the same classical datasets. Different ways of performing LIDAR semantic segmentation training and inference can be divided into several subfields, which include the following: domain generalization, the ability to segment data coming from unseen domains ; source-to-source segmentation, the ability to segment data coming from the training domain; and pre-training, the ability to create re-usable geometric primitives. In this work, we aim to improve results in all of these subfields with the novel approach of multi-source training. Multi-source training relies on the availability of various datasets at training time and uses them together rather than relying on only one dataset. To overcome the common obstacles found for multi-source training, we introduce the coarse labels and call the newly created multi-source dataset COLA. We propose three applications of this new dataset that display systematic improvement over single-source strategies: COLA-DG for domain generalization (up to +10%), COLA-S2S for source-to-source segmentation (up to +5.3%), and COLA-PT for pre-training (up to +12%).
Distributed Matrix-Based Sampling for Graph Neural Network Training

The primary contribution of this paper is new methods for reducing communication in the sampling step for distributed GNN training. Here, we propose a matrix-based bulk sampling approach that expresses sampling as a sparse matrix multiplication (SpGEMM) and samples multiple minibatches at once. When the input graph topology does not fit on a single device, our method distributes the graph and use communication-avoiding SpGEMM algorithms to scale GNN minibatch sampling, enabling GNN training on much larger graphs than those that can fit into a single device memory. When the input graph topology (but not the embeddings) fits in the memory of one GPU, our approach (1) performs sampling without communication, (2) amortizes the overheads of sampling a minibatch, and (3) can represent multiple sampling algorithms by simply using different matrix constructions. In addition to new methods for sampling, we show that judiciously replicating feature data with a simple all-to-all exchange can outperform current methods for the feature extraction step in distributed GNN training. We provide experimental results on the largest Open Graph Benchmark (OGB) datasets on $128$ GPUs, and show that our pipeline is $2.5\times$ faster Quiver (a distributed extension to PyTorch-Geometric) on a $3$-layer GraphSAGE network. On datasets outside of OGB, we show a $8.46\times$ speedup on $128$ GPUs in-per epoch time. Finally, we show scaling when the graph is distributed across GPUs and scaling for both node-wise and layer-wise sampling algorithms
Truly Scale-Equivariant Deep Nets with Fourier Layers

In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance. Recent works have made progress in developing scale-equivariant convolutional neural networks, e.g., through weight-sharing and kernel resizing. However, these networks are not truly scale-equivariant in practice. Specifically, they do not consider anti-aliasing as they formulate the down-scaling operation in the continuous domain. To address this shortcoming, we directly formulate down-scaling in the discrete domain with consideration of anti-aliasing. We then propose a novel architecture based on Fourier layers to achieve truly scale-equivariant deep nets, i.e., absolute zero equivariance-error. Following prior works, we test this model on MNIST-scale and STL-10 datasets. Our proposed model achieves competitive classification performance while maintaining zero equivariance-error.
1D-convolutional transformer for Parkinson disease diagnosis from gait

This paper presents an efficient deep neural network model for diagnosing Parkinson’s disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The proposed architecture exploits the strengths of both convolutional neural networks and Transformers in a single end-to-end model, where the former is able to extract relevant local features from Vertical Ground Reaction Force (VGRF) signal, while the latter allows to capture long-term spatio-temporal dependencies in data. In this manner, our hybrid architecture achieves an improved performance compared to using either models individually. Our experimental results show that our approach is effective for detecting the different stages of Parkinson’s disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset. Moreover, our method can be generalized and adapted for other classification problems to jointly address the feature relevance and spatio-temporal dependency problems in 1D signals. Our source code and pre-trained models are publicly available at https://github.com/SafwenNaimi .
Animating NeRFs from Texture Space: A Framework for Pose-Dependent Rendering of Human Performances

Creating high-quality controllable 3D human models from multi-view RGB videos poses a significant challenge. Neural radiance fields (NeRFs) have demonstrated remarkable quality in reconstructing and free-viewpoint rendering of static as well as dynamic scenes. The extension to a controllable synthesis of dynamic human performances poses an exciting research question. In this paper, we introduce a novel NeRF-based framework for pose-dependent rendering of human performances. In our approach, the radiance field is warped around an SMPL body mesh, thereby creating a new surface-aligned representation. Our representation can be animated through skeletal joint parameters that are provided to the NeRF in addition to the viewpoint for pose dependent appearances. To achieve this, our representation includes the corresponding 2D UV coordinates on the mesh texture map and the distance between the query point and the mesh. To enable efficient learning despite mapping ambiguities and random visual variations, we introduce a novel remapping process that refines the mapped coordinates. Experiments demonstrate that our approach results in high-quality renderings for novel-view and novel-pose synthesis.
NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that capture the complex and stealthy scenarios of APT attacks by using provenance graphs to model system entities and their dependencies. Particularly, to accelerate attack detection and reduce financial losses, online provenance-based detection systems that detect and investigate APT attacks under the constraints of timeliness and limited resources are in dire need. Unfortunately, existing online systems usually sacrifice detection granularity to reduce computational complexity and produce provenance graphs with more than 100,000 nodes, posing challenges for security admins to interpret the detection results. In this paper, we design and implement NodLink, the first online detection system that maintains high detection accuracy without sacrificing detection granularity. Our insight is that the APT attack detection process in online provenance-based detection systems can be modeled as a Steiner Tree Problem (STP), which has efficient online approximation algorithms that recover concise attack-related provenance graphs with a theoretically bounded error. To utilize STP approximation algorithm frameworks for APT attack detection, we propose a novel design of in-memory cache, an efficient attack screening method, and a new STP approximation algorithm that is more efficient than the conventional one in APT attack detection while maintaining the same complexity. We evaluate NodLink in a production environment. The open-world experiment shows that NodLink outperforms two state-of-the-art (SOTA) online provenance analysis systems by achieving magnitudes higher detection and investigation accuracy while having the same or higher throughput.
African history in comparative perspective

General Background History is as inherently comparative as it is contemporary, thus identifying the discipline as the main cutting edge of the humanities into the social sciences. There is an undeniable connection between the subject of study, the human past, on one hand, and, on the other, the historian, alive and present. Even to this important extent of the inter-temporal link between the historian and his subject, there is an acknowledgement, though more implicit than explicit, of the comparative impulse. Where, as is the case with many historians, research and/or teaching engagements are about societies and cultures other than their own, the spatial or the cross-cultural are then consciously or unconsciously combined with the inter-temporal dimensions of the comparative disposition. However, while comparison is in the very nature of history, the prism worn by mainstream historians commits them to the exploration of the discipline as a dealing with the unique and the incurably specific. Even when working on localities within the same national state, often the main framework of their generally nationalistically predetermined preoccupations, historians as was once observed of those in Zambia (Rennie 1980) as also the United States (Woodward 1968 and Strauss 1979), behave like miners ‘too intent on their hole and too unaware of the researcher in the hole next door, also frantically digging’. Over time and space, over-concentration and over-specialisation in specific localities and regions make appreciation of other localities and regions unattractive, thus crippling the potentials for wider regional syntheses and vital contributions to world history projects.
EFFECT OF PORE FLUIDS ON THE ELASTIC PROPERTIES OF SANDSTONE

Bulk compressibility, Young’s modulus, and Poisson’s ratio were measured on three sandstones. Measurements were made on both dry and water saturated samples. Several runs were made on each sandstone to establish the statistical validity of the differences observed between the wet and dry samples. Bulk compressibility of wet sandstone was 10 to 30 percent greater than for dry. Young’s modulus was 8 to 20 percent less for wet sandstone, and Poisson’s ratio was 100 percent greater on one type of sandstone when wet and only slightly greater or about the same on wet samples of the others. A high clay content is believed to lead to a large effect of water on the elastic moduli of sandstone.
Nonmetricity and hypermomentum: on the possible violation of Lorentz invariance

We outline how the symmetry groups of spacetime are interpreted in a gauge-theoretic approach. Specifically, we focus on the hypermomentum concept and discuss the hyperfluid, that appropriately generalizes the perfect (Euler) fluid of general relativity to the case of continuous media with microstructure. We demonstrate that a possible violation of Lorentz invariance is most adequately understood by means of non-vanishing nonmetricity of a metric-affine geometry of spacetime.
Landau-Lifshitz-Navier-Stokes Equations: Large Deviations and Relationship to The Energy Equality

The dynamical large deviations principle for the three-dimensional incompressible Landau-Lifschitz-Navier-Stokes equations is shown, in the joint scaling regime of vanishing noise intensity and correlation length. This proves the consistency of the large deviations in lattice gas models \cite{QY}, with Landau-Lifschitz fluctuating hydrodynamics \cite{LL87}. Secondly, in the course of the proof, we unveil a novel relation between the validity of the deterministic energy equality for the deterministic forced Navier-Stokes equations and matching large deviations upper and lower bounds. In particular, we conclude that time-reversible uniqueness to the forced Navier-Stokes equations implies the validity of the energy equality, thus generalising the classical Lions-Ladyzhenskaya result. Thirdly, we prove that no non-trivial large deviations result can be true for local-in-time strong solutions.
The Morphologic Traits of Donkeys Raised in East and Southeast of Turkey

This study was carried out to determine the morphological traits, distributions of body coat colour and body measurements of donkeys raised in the East and Southeast of Turkey by comparing with other donkey breeds of the world. For this purpose a total of 124 male and 70 female donkeys were measured. Descriptive statistics results yielded the following means: for withers height was 102.3 cm, height at rump 104.3 cm, body length 105.2 cm, heart girth circumference 113.5 cm, chest depth 45.7 cm, chest width 29.5 cm, cannon circumference 13.6 cm, head length 48.7 cm and ear length 21.9 cm respectively. In this study the body coat color frequencies among the donkeys were: mouse gray 31.4%, white 24.7%, black 23.7%, and brown 20.2%.
Temperature and Moisture Controls of Soil Respiration in a Dry Dipterocarp Forest, Ratchaburi Province

To quantify soil respiration and to investigate its diurnal and seasonal variations, soil respiration was studied in a dry dipterocarp forest located in Chombung District, Ratchaburi Province (13° 35’ 13.3” N, 99° 30’ 3.9” E). Soil respiration was measured hourly during February to July 2008 using a closed-automatic chamber method. The results showed that soil respiration varied significantly both spatially and seasonally. Among three replicates of measurements and within each hour of measurement, the coefficient of variations could be as high as 80%. On a daily scale, a weak relationship between soil respiration and soil temperature was observed. On a seasonal scale, a negative relationship between soil respiration and temperature was observed. However, a strong positive relationship between soil respiration and soil moisture over the moisture range of 17-19%vol was found. Soil respiration decreased beyond this moisture level. The total CO2 emissions during the six-month period in dry dipterocarp forest were 1.81 kgCO2/m 2 , or 4.9 tonne C/ha.
Sharp Lp-Lq estimates for a class of dissipative wave equations

In this paper we derive $L^p-L^q$ estimates, with $1\leq p\leq q\leq\infty$ (including endpoint estimates as $L^1-L^1$ and $L^1-L^\infty$), for the solution to \[ \begin{cases} u_{tt}-\Delta u + Au_t =0, \quad t\in\mathbb{R}_+,\, x\in \mathbb{R}^n, \\ u(0,x)=0, \\ u_t(0,x)=u_1(x), \end{cases} \] for a general class of dissipation terms, where $Af=\mathscr{F}^{-1}(a(\xi)\,\mathscr{F}f(\xi))$, with $a\in\mathcal C^{n+1}(\mathbb{R}^n\setminus\{0\})$, and $a(\xi)>0$ verifies conditions of Mikhlin-H\"ormander type for $M_p^q$ multipliers which may be different at low frequencies and at high frequencies; in particular $a(\xi)$ may also be inhomogeneous and anisotropic. We prove that the obtained estimates are sharp.
Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis

Meta learning is a promising technique for solving few-shot fault prediction problems, which have attracted the attention of many researchers in recent years. Existing meta-learning methods for time series prediction, which predominantly rely on random and similarity matching-based task partitioning, face three major limitations: (1) feature exploitation inefficiency; (2) suboptimal task data allocation; and (3) limited robustness with small samples. To overcome these limitations, we introduce a novel 'pseudo meta-task' partitioning scheme that treats a continuous time period of a time series as a meta-task, composed of multiple successive short time periods. Employing continuous time series as pseudo meta-tasks allows our method to extract more comprehensive features and relationships from the data, resulting in more accurate predictions. Moreover, we introduce a differential algorithm to enhance the robustness of our method across different datasets. Through extensive experiments on several fault and time series prediction datasets, we demonstrate that our approach substantially enhances prediction performance and generalization capability under both few-shot and general conditions.
Higher homotopy and lattice gauge fields

We present a general formalism for higher dimensional versions of lattice gauge fields based on higher strict homotopy groupoids. First, using the language of nonabelian Algebraic Topology, we define local lattice higher gauge fields. Then, we provide local-to-global principles for lattice higher gauge fields based on the HHSvK theorem. We prove that, under the correct assumptions, lattice higher gauge field as presented here, subsumes both the notion of extended lattice gauge field and other notions of higher lattice gauge field present in the literature.
Architecting Intermediate Layers for Efficient Composition of Data Management and Machine Learning Systems

Modern data analytics workloads combine relational data processing with machine learning (ML). Most DBMS handle these workloads by offloading these ML operations to external specialized ML systems. While both DBMS and ML systems go to great lengths to optimize performance for their specific workloads, significant performance is lost when used in combination, due to data movement across system boundaries, conversions between incompatible internal data formats, and the lack of cross system optimizations. A key idea to remove these bottlenecks is to integrate existing data manipulation systems with ML systems by building a common intermediate layer (IR). Although this idea has been explored before (Weld, Delite), previous such attempts require significant re-engineering of prior systems and still fall short in achieving best-of-breed performance for individual tasks (e.g., SQL, Deep Learning). Specifically, they rely on re-implementing existing systems using a generic set of operators and fail to match best-of-breed individual performance due to the inability to recover high-level optimizations from this generic IR through compiler analysis. We present Flern, the first intermediate-layer integration between DB and ML systems that are best-of-breed individually, competitive with the best compiled query engines such as HyPer on comprehensive relational benchmarks (TPC-H) and competitive with TensorFlow and PyTorch in state-of-the-art ML models (e.g., DeepSpeech, SqueezeNet, Transformers) and also represents a new state-of-the-art for integration. A key realization is to architect intermediate layers based on generative programming capabilities, which preserves high-level contextual information for cross optimizations and enables the construction of a variety of complex structures and cross system optimizations with minimal effort.
You Only Forward Once: Prediction and Rationalization in A Single Forward Pass

Unsupervised rationale extraction aims to extract concise and contiguous text snippets to support model predictions without any annotated rationale. Previous studies have used a two-phase framework known as the Rationalizing Neural Prediction (RNP) framework, which follows a generate-then-predict paradigm. They assumed that the extracted explanation, called rationale, should be sufficient to predict the golden label. However, the assumption above deviates from the original definition and is too strict to perform well. Furthermore, these two-phase models suffer from the interlocking problem and spurious correlations. To solve the above problems, we propose a novel single-phase framework called You Only Forward Once (YOFO), derived from a relaxed version of rationale where rationales aim to support model predictions rather than make predictions. In our framework, A pre-trained language model like BERT is deployed to simultaneously perform prediction and rationalization with less impact from interlocking or spurious correlations. Directly choosing the important tokens in an unsupervised manner is intractable. Instead of directly choosing the important tokens, YOFO gradually removes unimportant tokens during forward propagation. Through experiments on the BeerAdvocate and Hotel Review datasets, we demonstrate that our model is able to extract rationales and make predictions more accurately compared to RNP-based models. We observe an improvement of up to 18.4\% in token-level F1 compared to previous state-of-the-art methods. We also conducted analyses and experiments to explore the extracted rationales and token decay strategies. The results show that YOFO can extract precise and important rationales while removing unimportant tokens in the middle part of the model.
Magnetic properties of perovskites Pr$_{0.9}$Sr$_{0.1}$Mn$_{0.9}^{3+}$Mn$_{0.1}^{4+}$O$_{3}$: Monte Carlo simulations and experiments

This work presents the remarkable experimental magnetocaloric properties of the perovskites Pr$_{0.9}$Sr$_{0.1}$Mn$_{0.9}^{3+}$Mn$_{0.1}^{4+}$O$_{3}$, including the magnetic entropy change $|\Delta S_m|$ and the Relative Cooling Power (RCP). To understand these striking properties, we elaborate in this paper a model and use Monte Carlo (MC) simulations to study it for comparison. For the model, we take into account nearest-neighbor (NN) interactions between magnetic ions Mn$^{3+}$($S=2$) and Mn$^{4+}$($S=3/2$) and the interactions between these Mn ions with the magnetic Pr ions. The crystal is a body-centered tetragonal lattice where the corner sites are occupied by Mn ions and the center sites by Pr and Sr ions in their respective concentrations given in the compound formula. We use an Ising-like spin model. We show that pairwise interactions between ions cannot reproduce the large plateau of the magnetization experimentally observed below the phase-transition temperature. By introducing for the first time a many-spin interaction between Mn ions, we obtain an excellent agreement with experiments. Fitting the experimental Curie temperature $T_C$ with the MC transition temperature, we estimate the value of the effective exchange interaction in the system. From this value, we estimate various exchange interactions between ions: the dominant one is that between Mn$^{3+}$ and Mn$^{4+}$ which is at the origin of the ferromagnetic ordering below $T_C$. We also studied the applied-field effect on the magnetization in the region below and above $T_C$. The obtained MC results for $|\Delta S_m|$ are in agreement with experiments performed for applied fields from 1 to 5 Tesla. MC results of RCP are also shown and compared to experimental ones.