Miracle-dz/UniFAR-FTUs huggingface dataset:
Color-S4L: Self-supervised Semi-supervised Learning with Image Colorization

This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks. Different from widely-used consistency regularization within semi-supervised learning, we explored a novel self-supervised semi-supervised learning framework (Color-$S^{4}L$) especially with image colorization proxy task and deeply evaluate performances of various network architectures in such special pipeline. Also, we demonstrated its effectiveness and optimal performance on CIFAR-10, SVHN and CIFAR-100 datasets in comparison to previous supervised and semi-supervised optimal methods.
Classifying cow stall numbers using YOLO

This paper introduces the CowStallNumbers dataset, a collection of images extracted from videos focusing on cow teats, designed to advance the field of cow stall number detection. The dataset comprises 1042 training images and 261 test images, featuring stall numbers ranging from 0 to 60. To enhance the dataset, we performed fine-tuning on a YOLO model and applied data augmentation techniques, including random crop, center crop, and random rotation. The experimental outcomes demonstrate a notable 95.4\% accuracy in recognizing stall numbers.
Security and Privacy Issues in Cloud Storage

Even with the vast potential that cloud computing has, so far, it has not been adopted by the consumers with the enthusiasm and pace that it be worthy; this is a very reason statement why consumers still hesitated of using cloud computing for their sensitive data and the threats that prevent the consumers from shifting to use cloud computing in general and cloud storage in particular. The cloud computing inherits the traditional potential security and privacy threats besides its own issues due to its unique structures. Some threats related to cloud computing are the insider malicious attacks from the employees that even sometime the provider unconscious about, the lack of transparency of agreement between consumer and provider, data loss, traffic hijacking, shared technology and insecure application interface. Such threats need remedies to make the consumer use its features in secure way. In this review, we spot the light on the most security and privacy issues which can be attributed as gaps that sometimes the consumers or even the enterprises are not aware of. We also define the parties that involve in scenario of cloud computing that also may attack the entire cloud systems. We also show the consequences of these threats.
Examining Forgetting in Continual Pre-training of Aligned Large Language Models

Recent advances in Large Language Models (LLMs) have exhibited remarkable proficiency across various tasks. Given the potent applications of LLMs in numerous fields, there has been a surge in LLM development. In developing LLMs, a common practice involves continual pre-training on previously fine-tuned models. However, this can lead to catastrophic forgetting. In our work, we investigate the phenomenon of forgetting that occurs during continual pre-training on an existing fine-tuned LLM. We evaluate the impact of continuous pre-training on the fine-tuned LLM across various dimensions, including output format, knowledge, and reliability. Experiment results highlight the non-trivial challenge of addressing catastrophic forgetting during continual pre-training, especially the repetition issue.
Evolution of sex differences in cooperation: the role of trade-offs with dispersal

Explaining the evolution of sex differences in cooperation remains a major challenge. Comparative studies highlight that the more philopatric sex tends to be more cooperative than the more dispersive sex, but we do not understand why. Here we test the two leading evolutionary explanations for this association for the first time in a wild cooperative bird, using a novel automated radio-tracking study. Our findings provide no support for the widely invoked ‘philopatry hypothesis’, which proposes that the more philopatric sex helps more as it gains a greater direct fitness benefit from cooperation. Instead, our findings support the lesser known ‘dispersal trade-off hypothesis’, which proposes that the more dispersive sex helps less because investments in extra-territorial prospecting for dispersal opportunities trade-off against cooperation. This simple explanation for the evolution of sex differences in cooperation could apply widely across taxa as it does not require that cooperation yields a direct fitness benefit.
Steering spin fluctuations in lattice systems via two-tone Floquet engineering

We report on the controlled creation and destruction of antiferromagnetic dimers using two-tone Floquet engineering. We consider a one-dimensional spin-1/2 lattice with periodically modulated bonds using parametric resonances. The stroboscopic dynamics generated from distributed bond modulations lead to pair correlation between spins. Consequently, subharmonic response in local observables breaks discrete time translational symmetry and leads to the emergence of Floquet dynamical dimerisation. We present a protocol allowing the control of local spin-correlated pairs driven by one-period evolution operators, providing significant insight into new nonequilibrium states of matter that can be feasibly implemented in current quantum simulator platforms.
Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations

A genuine signer's signature is naturally unstable even at short time-intervals whereas, expert forgers always try to perfectly mimic a genuine signer's signature. This presents a challenge which puts a genuine signer at risk of being denied access, while a forge signer is granted access. The implication is a high false acceptance rate (FAR) which is the percentage of forge signature classified as belonging to a genuine class. Existing work have only scratched the surface of signature verification because the misclassification error remains high. In this paper, a consensus-threshold distance-based classifier criterion is proposed for offline writer-dependent signature verification. Using features extracted from SigNet and SigNet-F deep convolutional neural network models, the proposed classifier minimizes FAR. This is demonstrated via experiments on four datasets: GPDS-300, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-300, the consensus threshold classifier improves the state-of-the-art performance by achieving a 1.27% FAR compared to 8.73% and 17.31% recorded in literature. This performance is consistent across other datasets and guarantees that the risk of imposters gaining access to sensitive documents or transactions is minimal.
The discovery of bound star clusters 460 Myr after the Big Bang

Young galaxies, potentially responsible for the last major phase-transition of the Universe, appear brighter than expected and go through rapid bursty phases where copious amounts of ionizing radiation and feedback are produced. However, the stellar components of the majority of these reionization--era galaxies remain spatially unresolved. In this letter, we report the direct discovery of young massive star clusters in the strongly lensed galaxy SPT0615-JD1 (dubbed the Cosmic Gems arc) at redshift $z\sim10.2_{-0.2}^{+0.2}$ when the universe was $\sim 460$ Myr old. Recently observed with JWST/NIRCam imaging, the Cosmic Gems arc stretches over 5\arcsec\, (Bradley in prep.) revealing 5 individual massive young star clusters with lensing-corrected sizes of $\sim$1 pc, located in a region smaller than 70 pc. These Cosmic Gems produce $\sim60$ % of the FUV light of the host, and have very low dust attenuation (A$_V<$0.5 mag) and metallicity ($\sim$ 5% solar), intrinsic masses of $\sim10^6$ M$_{\odot}$, and ages younger than 35 Myr. Their stellar surface densities are around $10^5$~M$_{\odot}$/pc$^2$, three orders of magnitude higher than typical star clusters in the local universe. Despite the uncertainties inherent to the lensing model, their dynamical ages are consistent with being gravitationally bound stellar systems that could potentially evolve into globular clusters. They would be the earliest known proto-globular clusters, formed less than 500 Myr after the Big Bang. This discovery opens a new window into the physical processes that take place in reionization-era bursty galaxies, showing that star cluster formation and clustered stellar feedback might play an important role for reionization.
Characterizing Physical Memory Fragmentation

External fragmentation of physical memory occurs when adjacent differently sized regions of allocated physical memory are freed at different times, causing free memory to be physically discontiguous. It can significantly degrade system performance and efficiency, such as reducing the ability to use huge pages, a critical optimization on modern large-memory system. For decades system developers have sought to avoid and mitigate fragmentation, but few prior studies quantify and characterize it in production settings. Moreover, prior work often artificially fragments physical memory to create more realistic performance evaluations, but their fragmentation methodologies are ad hoc and unvalidated. Out of 13 papers, we found 11 different methodologies, some of which were subsequently found inadequate. The importance of addressing fragmentation necessitates a validated and principled methodology. Our work fills these gaps in knowledge and methodology. We conduct a study of memory fragmentation in production by observing 248 machines in the Computer Sciences Department at University of Wisconsin - Madison for a week. We identify six key memory usage patterns, and find that Linux's file cache and page reclamation systems are major contributors to fragmentation because they often obliviously break up contiguous memory. Finally, we create and\'uril, a tool to artificially fragment memory during experimental research evaluations. While and\'uril ultimately fails as a scientific tool, we discuss its design ideas, merits, and failings in hope that they may inspire future research.
RoboFusion: Towards Robust Multi-Modal 3D obiect Detection via SAM

Multi-modal 3D object detectors are dedicated to exploring secure and reliable perception systems for autonomous driving (AD). However, while achieving state-of-the-art (SOTA) performance on clean benchmark datasets, they tend to overlook the complexity and harsh conditions of real-world environments. Meanwhile, with the emergence of visual foundation models (VFMs), opportunities and challenges are presented for improving the robustness and generalization of multi-modal 3D object detection in autonomous driving. Therefore, we propose RoboFusion, a robust framework that leverages VFMs like SAM to tackle out-of-distribution (OOD) noise scenarios. We first adapt the original SAM for autonomous driving scenarios named SAM-AD. To align SAM or SAM-AD with multi-modal methods, we then introduce AD-FPN for upsampling the image features extracted by SAM. We employ wavelet decomposition to denoise the depth-guided images for further noise reduction and weather interference. Lastly, we employ self-attention mechanisms to adaptively reweight the fused features, enhancing informative features while suppressing excess noise. In summary, our RoboFusion gradually reduces noise by leveraging the generalization and robustness of VFMs, thereby enhancing the resilience of multi-modal 3D object detection. Consequently, our RoboFusion achieves state-of-the-art performance in noisy scenarios, as demonstrated by the KITTI-C and nuScenes-C benchmarks.
Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint

This paper analyzes hierarchical Bayesian inverse problems using techniques from high-dimensional statistics. Our analysis leverages a property of hierarchical Bayesian regularizers that we call approximate decomposability to obtain non-asymptotic bounds on the reconstruction error attained by maximum a posteriori estimators. The new theory explains how hierarchical Bayesian models that exploit sparsity, group sparsity, and sparse representations of the unknown parameter can achieve accurate reconstructions in high-dimensional settings.
Transferable Learned Image Compression-Resistant Adversarial Perturbations

Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are primarily applied to uncompressed images or compressed images by the traditional image compression method, i.e., JPEG, limited studies have investigated the robustness of models for image classification in the context of DNN-based image compression. With the rapid evolution of advanced image compression, DNN-based learned image compression has emerged as the promising approach for transmitting images in many security-critical applications, such as cloud-based face recognition and autonomous driving, due to its superior performance over traditional compression. Therefore, there is a pressing need to fully investigate the robustness of a classification system post-processed by learned image compression. To bridge this research gap, we explore the adversarial attack on a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules. Furthermore, to enhance the transferability of perturbations across various quality levels and architectures of learned image compression models, we introduce a saliency score-based sampling method to enable the fast generation of transferable perturbation. Extensive experiments with popular attack methods demonstrate the enhanced transferability of our proposed method when attacking images that have been post-processed with different learned image compression models.
The Optimal Rate for Linear KB-splines and LKB-splines Approximation of High Dimensional Continuous Functions and its Application

We propose a new approach for approximating functions in $C([0,1]^d)$ via Kolmogorov superposition theorem (KST) based on the linear spline approximation of the K-outer function in Kolmogorov superposition representation. We improve the results in \cite{LaiShenKST21} by showing that the optimal approximation rate based on our proposed approach is $\mathcal{O}(\frac{1}{n^2})$, with $n$ being the number of knots over $[0,1]$, and the approximation constant increases linearly in $d$. We show that there is a dense subclass in $C([0,1]^d)$ whose approximation can achieve such optimal rate, and the number of parameters needed in such approximation is at most $\mathcal{O}(nd)$. Moreover, for $d\geq 4$, we apply the tensor product spline denoising technique to smooth the KB-splines and get the corresponding LKB-splines. We use those LKB-splines as the basis to approximate functions for the cases when $d=4$ and $d=6$, which extends the results in \cite{LaiShenKST21} for $d=2$ and $d=3$. Based on the idea of pivotal data locations introduced in \cite{LaiShenKST21}, we validate via numerical experiments that fewer than $\mathcal{O}(nd)$ function values are enough to achieve the approximation rates such as $\mathcal{O}(\frac{1}{n})$ or $\mathcal{O}(\frac{1}{n^2})$ based on the smoothness of the K-outer function. Finally, we demonstrate that our approach can be applied to numerically solving partial differential equation such as the Poisson equation with accurate approximation results.
Role of H 2 O in Generating Subduction Zone Earthquakes

A dense nationwide seismic network and high seismic activity in Japan have provided a large volume of highquality data, enabling high-resolution imaging of the seismic structures defining the Japanese subduction zones. Here, the role of H2O in generating earthquakes in subduction zones is discussed based mainly on recent seismic studies in Japan using these high-quality data. Locations of intermediate-depth intraslab earthquakes and seismic velocity and attenuation structures within the subducted slab provide evidence that strongly supports intermediate-depth intraslab earthquakes, although the details leading to the earthquake rupture are still poorly understood. Coseismic rotations of the principal stress axes observed after great megathrust earthquakes demonstrate that the plate interface is very weak, which is probably caused by overpressured fluids. Detailed tomographic imaging of the seismic velocity structure in and around plate boundary zones suggests that interplate coupling is affected by local fluid overpressure. Seismic tomography studies also show the presence of inclined sheet-like seismic low-velocity, high-attenuation zones in the mantle wedge. These may correspond to the upwelling flow portion of subduction-induced secondary convection in the mantle wedge. The upwelling flows reach the arc Moho directly beneath the volcanic areas, suggesting a direct relationship. H2O originally liberated from the subducted slab is transported by this upwelling flow to the arc crust. The H2O that reaches the crust is overpressured above hydrostatic values, weakening the surrounding crustal rocks and decreasing the shear strength of faults, thereby inducing shallow inland earthquakes. These observations suggest that H2O expelled from the subducting slab plays an important role in generating subduction zone earthquakes both within the subduction zone itself and within the magmatic arc occupying its hanging wall.
Overview of Dialogue Robot Competition 2023

We have held dialogue robot competitions in 2020 and 2022 to compare the performances of interactive robots using an android that closely resembles a human. In 2023, the third competition DRC2023 was held. The task of DRC2023 was designed to be more challenging than the previous travel agent dialogue tasks. Since anyone can now develop a dialogue system using LLMs, the participating teams are required to develop a system that effectively uses information about the situation on the spot (real-time information), which is not handled by ChatGPT and other systems. DRC2023 has two rounds, a preliminary round and the final round as well as the previous competitions. The preliminary round has held on Oct.27 -- Nov.20, 2023 at real travel agency stores. The final round will be held on December 23, 2023. This paper provides an overview of the task settings and evaluation method of DRC2023 and the preliminary round results.
We Need to Talk About Classification Evaluation Metrics in NLP

In Natural Language Processing (NLP) classification tasks such as topic categorisation and sentiment analysis, model generalizability is generally measured with standard metrics such as Accuracy, F-Measure, or AUC-ROC. The diversity of metrics, and the arbitrariness of their application suggest that there is no agreement within NLP on a single best metric to use. This lack suggests there has not been sufficient examination of the underlying heuristics which each metric encodes. To address this we compare several standard classification metrics with more 'exotic' metrics and demonstrate that a random-guess normalised Informedness metric is a parsimonious baseline for task performance. To show how important the choice of metric is, we perform extensive experiments on a wide range of NLP tasks including a synthetic scenario, natural language understanding, question answering and machine translation. Across these tasks we use a superset of metrics to rank models and find that Informedness best captures the ideal model characteristics. Finally, we release a Python implementation of Informedness following the SciKitLearn classifier format.
Simulations with unified mosfet model for distortions analysis

The challenges of the integrated circuits and nanotechnology need very accurate models for active devices. From this point of view the design of linear analog circuits lacks models for state-of-the-art MOS transistors to accurately describe distortion effects. This is produced by the inaccurate modelling of the second order effects induced by high vertical gate field such as mobility degradation and short channel series resistance and second order effects induced by parallel drain field like velocity saturation in the ohmic region, channel length modulation in the saturation region, static feedback and weak avalanche. After a rigorous description of transistor transconductance and channel conductance in ohmic and saturation region we included these effects in the MOS transistor model, using a compact expression of drain current for computation reasons. The simulations using the new drain current expression were in good agreement with experimental data, proves scalability and large voltage range functionality.
Observational analysis of influence of sea-land breeze on diurnal characteristics of urban heat island in Tianjin during summer

Based on the hourly observations of 14automatic weather stations in Tianjin,the Beijing radiosounding data,and the 6-hourly surface conventional observation data in Tianjin during summer 2008,this paper studies the relationship between the sea-land breeze and the diurnal characteristics of urban heat island in Tianjin.Results show that,under the atmospheric stable stratification condition,the diurnal variation of urban heat island intensity in Tianjin increases on the sealand breeze days compared to the non sea-land breeze days.The sea breeze can cool the city,reduce the urban heat island intensity,and delay the appearance of the urban heat island in the nighttime,while the land breeze can significantly enhance the urban heat island intensity at night.We also find that the urban heat island intensity is closely related to the distance of sea breeze propagating.On the sea breeze days,when the synoptic scale geostrophic winds are in the same direction with the offshore wind,the sea breeze can only spread short distance;when the synoptic scale geostrophic winds are in the same direction with the onshore wind,the sea breeze can propagate farther;when the sea breeze can only reach Jinnan,Dongli or Ninghe stations,the urban heat island intensity in Tianjin has the maximum increase;with the increasing of sea breeze propagating distance,the overall increase rate of urban heat island intensity decreases.
A Primer on Temporal Graph Learning

This document aims to familiarize readers with temporal graph learning (TGL) through a concept-first approach. We have systematically presented vital concepts essential for understanding the workings of a TGL framework. In addition to qualitative explanations, we have incorporated mathematical formulations where applicable, enhancing the clarity of the text. Since TGL involves temporal and spatial learning, we introduce relevant learning architectures ranging from recurrent and convolutional neural networks to transformers and graph neural networks. We also discuss classical time series forecasting methods to inspire interpretable learning solutions for TGL.
Hawking radiation inside a rotating black hole

In semiclassical gravity, the vacuum expectation value ${\langle\hat{N}\rangle}$ of the particle number operator for a quantum field gives rise to the perception of thermal radiation in the vicinity of a black hole. This Hawking effect has been examined only for observers asymptotically far from a Kerr black hole; here we generalize the analysis to various classes of freely falling observers both outside and inside the Kerr event horizon. Of note, we find that the effective temperature of the ${\langle\hat{N}\rangle}$ distribution remains regular for observers at the event horizon but becomes negative and divergent for observers reaching the inner Cauchy horizon. Furthermore, the perception of Hawking radiation varies greatly for different classes of observers, though the spectrum is generally a graybody that decreases in intensity with black hole spin and increases in temperature when looking toward the edges of the black hole shadow.