Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE DISPERSIVE WAVE DYNAMICS OF 2 × 2 RELAXATION SYSTEMS

We consider hyperbolic conservation laws with relaxation terms. By studying the dispersion relation of the solution of general linearized 2 × 2 hyperbolic relaxation systems, we investigate in detail the transition between the wave dynamics of the homogeneous relaxation system and that of the local equilibrium approximation. We establish that the wave velocities of the Fourier components of the solution to the relaxation system will be monotonic functions of a stiffness parameter φ = eξ, where e is the relaxation time and ξ is the wave number. This allows us to extend in a natural way the classical concept of the sub-characteristic condition into a more general transitional sub-characteristic condition. We further identify two parameters β and γ that characterize the behavior of such general 2 × 2 linear relaxation systems. In particular, these parameters define a natural transition point, representing a value of φ where the dynamics will change abruptly from being equilibrium-like to behaving more like the homogeneous relaxation system. Herein, the parameter γ determines the location of the transition point, whereas β measures the degree of smoothness of this transition.
Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective

Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) methods have been evolved almost independently for years. A neglected study in this field is the theoretical analysis of finding the optimum combination of SISR and MFSR. To fill this gap, we propose a novel theoretical analysis based on the iterative shrinkage and thresholding algorithm. We implement and compare several approaches for combining SISR and MFSR, and simulation results support the finding of our theoretical analysis, both quantitatively and qualitatively.
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning

The ideal form of Visual Question Answering requires understanding, grounding and reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most existing VQA benchmarks are limited to just picking the answer from a pre-defined set of options and lack attention to text. We present a new challenge with a dataset that contains 23,781 questions based on 10124 image-text pairs. Specifically, the task requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this challenge is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation.
Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science

The Vera C. Rubin Observatory is expected to start the Legacy Survey of Space and Time (LSST) in early to mid-2025. This multiband wide-field synoptic survey will transform our view of the solar system, with the discovery and monitoring of over five million small bodies. The final survey strategy chosen for LSST has direct implications on the discoverability and characterization of solar system minor planets and passing interstellar objects. Creating an inventory of the solar system is one of the four main LSST science drivers. The LSST observing cadence is a complex optimization problem that must balance the priorities and needs of all the key LSST science areas. To design the best LSST survey strategy, a series of operation simulations using the Rubin Observatory scheduler have been generated to explore the various options for tuning observing parameters and prioritizations. We explore the impact of the various simulated LSST observing strategies on studying the solar system’s small body reservoirs. We examine what are the best observing scenarios and review what are the important considerations for maximizing LSST solar system science. In general, most of the LSST cadence simulations produce ±5% or less variations in our chosen key metrics, but a subset of the simulations significantly hinder science returns with much larger losses in the discovery and light-curve metrics.
Detection of interstellar (C-13)N toward Zeta Ophiuchi

Observations of a diffuse interstellar cloud toward Zeta Oph, obtained with resolution 100,000-150,000 near the 3874.608-A R(0) line of (C-12)N using a coude echelle spectrograph on the 1.4-m telescope at ESO during 1984 and 1985, are reported. Data from 54 20-min runs were fitted to Gaussian line shapes using the line center, depth, and width of the R(0) and R(1) lines of (C-12)N and the line center and depth of the R(0) line of (C-13)N as fitting parameters. The (C-13)N R(0) line, with equivalent width 0.190 + or - 0.020 mA, was detected 173.7 + or - 0.8 mA to the red of (C-12)N R(0); the corresponding isotope abundance ratio, (C-12)N/(C-13)N = 47.3 + 5.5 or -4.4, is shown to be in good agreement with previous measurements for CH(+) (Hawkins et al., 1985). 13 references.
Self-Supervised Learning for Biologically-Inspired Place Representation Generalization across Appearance Changes

Visual place recognition is a key to unlocking spatial navigation for animals, humans and robots. From a biological perspective, research in neuroscience has shown that place recognition ability presupposes abstracting mental representations of places so that they can generalize to unseen conditions. Inspired by that, we investigate learning features that are insensitive to appearance mod-iﬁcations while sensitive to geometric transformations in a self-supervised manner. That dual-purpose training is made possible by combining the two self-supervision main paradigms, i.e. contrastive and predictive learning. Our re-sults on standard benchmarks reveal that jointly learning such appearance-invariant and geometry-equivariant image descriptors leads to competitive visual place recognition results across adverse seasonal and illumination conditions, without requiring any human-annotated labels 1
Hybrid Y-Net Architecture for Singing Voice Separation

This research paper presents a novel deep learning-based neural network architecture, named Y-Net, for achieving music source separation. The proposed architecture performs end-to-end hybrid source separation by extracting features from both spectrogram and waveform domains. Inspired by the U-Net architecture, Y-Net predicts a spectrogram mask to separate vocal sources from a mixture signal. Our results demonstrate the effectiveness of the proposed architecture for music source separation with fewer parameters. Overall, our work presents a promising approach for improving the accuracy and efficiency of music source separation.
NxPlain: A Web-based Tool for Discovery of Latent Concepts

The proliferation of deep neural networks in various domains has seen an increased need for the interpretability of these models, especially in scenarios where fairness and trust are as important as model performance. A lot of independent work is being carried out to: i) analyze what linguistic and non-linguistic knowledge is learned within these models, and ii) highlight the salient parts of the input. We present NxPlain, a web-app that provides an explanation of a model’s prediction using latent concepts. NxPlain discovers latent concepts learned in a deep NLP model, provides an interpretation of the knowledge learned in the model, and explains its predictions based on the used concepts. The application allows users to browse through the latent concepts in an intuitive order, letting them efficiently scan through the most salient concepts with a global corpus-level view and a local sentence-level view. Our tool is useful for debugging, unraveling model bias, and for highlighting spurious correlations in a model. A hosted demo is available here: https://nxplain.qcri. org
A Topological Distance Measure between Multi-Fields for Classification and Analysis of Shapes and Data

Distance measures play an important role in shape classification and data analysis problems. Topological distances based on Reeb graphs and persistence diagrams have been employed to obtain effective algorithms in shape matching and scalar data analysis. In the current paper, we propose an improved distance measure between two multi-fields by computing a multi-dimensional Reeb graph (MDRG) each of which captures the topology of a multi-field through a hierarchy of Reeb graphs in different dimensions. A hierarchy of persistence diagrams is then constructed by computing a persistence diagram corresponding to each Reeb graph of the MDRG. Based on this representation, we propose a novel distance measure between two MDRGs by extending the bottleneck distance between two Reeb graphs. We show that the proposed measure satisfies the pseudo-metric and stability properties. We examine the effectiveness of the proposed multi-field topology-based measure on two different applications: (1) shape classification and (2) detection of topological features in a time-varying multi-field data. In the shape classification problem, the performance of the proposed measure is compared with the well-known topology-based measures in shape matching. In the second application, we consider a time-varying volumetric multi-field data from the field of computational chemistry where the goal is to detect the site of stable bond formation between Pt and CO molecules. We demonstrate the ability of the proposed distance in classifying each of the sites as occurring before and after the bond stabilization.
A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification

Deep neural networks based on layer-stacking architectures have historically suffered from poor inherent interpretability. Meanwhile, symbolic probabilistic models function with clear interpretability, but how to combine them with neural networks to enhance their performance remains to be explored. In this paper, we try to marry these two systems for text classification via a structured language model. We propose a Symbolic-Neural model that can learn to explicitly predict class labels of text spans from a constituency tree without requiring any access to span-level gold labels. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self-interpretable classification model. Our experiments demonstrate that our approach could achieve good prediction accuracy in downstream tasks. Meanwhile, the predicted span labels are consistent with human rationales to a certain degree.
A proposal for a hierarchical MRF model based on conditional probability

The standard regularization theory extended to problems where generic constraints or knowledge are expressed within the framework of a Markov random field (MRF) model. This extended theory is applied to image restoration in which a desired state in the line process is given as a constraint. The forward process in transformation between two kinds of visual information, from information of pixel intensity to information of edge configuration, is modeled with a renormalization group technique rather than with the usual optics. Perfect restorations were obtained for some simple pictures. <<ETX>>
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks. However, existing methods typically learn soft prompt vectors from scratch, and it has not been clear how to exploit the rich cross-task knowledge with prompt vectors in a multitask learning setting. We propose multitask prompt tuning (MPT), which first learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task. Extensive experiments on 23 NLP datasets demonstrate that our proposed approach outperforms the state-of-the-art methods, including the full finetuning baseline in some cases, despite only tuning 0.035% as many task-specific parameters.
Hierarchical Training of Deep Neural Networks Using Early Exiting

Deep neural networks provide state-of-the-art accuracy for vision tasks but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication cost, runtime and privacy concerns. In this study, a novel hierarchical training method for deep neural networks is proposed that uses early exits in a divided architecture between edge and cloud workers to reduce the communication cost, training runtime and privacy concerns. The method proposes a brand-new use case for early exits to separate the backward pass of neural networks between the edge and the cloud during the training phase. We address the issues of most available methods that due to the sequential nature of the training phase, cannot train the levels of hierarchy simultaneously or they do it with the cost of compromising privacy. In contrast, our method can use both edge and cloud workers simultaneously, does not share the raw input data with the cloud and does not require communication during the backward pass. Several simulations and on-device experiments for different neural network architectures demonstrate the effectiveness of this method. It is shown that the proposed method reduces the training runtime by 29% and 61% in CIFAR-10 classification experiment for VGG-16 and ResNet-18 when the communication with the cloud is done at a low bit rate channel. This gain in the runtime is achieved whilst the accuracy drop is negligible. This method is advantageous for online learning of high-accuracy deep neural networks on low-resource devices such as mobile phones or robots as a part of an edge-cloud system, making them more flexible in facing new tasks and classes of data.
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable of generating fine-grained masks for semantic segmentation. To guide CAM to find more non-discriminating object patterns, this paper turns to an interesting working mechanism in agent learning named Complementary Learning System (CLS). CLS holds that the neocortex builds a sensation of general knowledge, while the hippocampus specially learns specific details, completing the learned patterns. Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask. Specifically, GSLM develops a General Learning Module (GLM) and a Specific Learning Module (SLM). The GLM is trained with image-level supervision to extract coarse and general localization representations from CAM. Based on the general knowledge in the GLM, the SLM progressively exploits the specific spatial knowledge from the localization representations, expanding the CAM in an explicit way. To this end, we propose the Seed Reactivation to help SLM reactivate non-discriminating regions by setting a boundary for activation values, which successively identifies more regions of CAM. Without extra refinement processes, our method is able to achieve breakthrough improvements for CAM of over 20.0% mIoU on PASCAL VOC 2012 and 10.0% mIoU on MS COCO 2014 datasets, representing a new state-of-the-art among existing WSSS methods.
The AI Ghostwriter Effect: Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30&n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation models. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. The degree of personalization did not impact the AI Ghostwriter Effect, and control over the model increased participants' sense of ownership. We also found that the discrepancy between the sense of ownership and the authorship declaration is stronger in interactions with a human ghostwriter and that people use similar rationalizations for authorship in AI ghostwriters and human ghostwriters. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.
Intrusion Response Systems: Past, Present and Future

The rapid expansion of the Internet of Things and the emergence of edge computing-based applications has led to a new wave of cyber-attacks, with intensity and complexity that has never been seen before. Historically most research has focused on Intrusion Detection Systems (IDS), however due to the volume and speed of this new generation of cyber-attacks it is no longer sufficient to solely detect attacks and leave the response to security analysts. Consequently, research into Intrusion Response Systems (IRS) is accelerating rapidly. As such, new intrusion response approaches, methods and systems have been investigated, prototyped, and deployed. This paper is intended to provide a comprehensive review of the state of the art of IRSs. Specifically, a taxonomy to characterize the lifecycle of IRSs ranging from response selection to response deployment and response implementation is presented. A 10-phase structure to organize the core technical constituents of IRSs is also presented. Following this, an extensive review and analysis of the literature on IRSs published during the past decade is provided, and further classifies them into corresponding phases based on the proposed taxonomy and phase structure. This study provides a new way of classifying IRS research, thus offering in-depth insights into the latest discoveries and findings. In addition, through critical analysis and comparison, expert views, guidance and best practices on intrusion response approaches, system development and standardization are presented, upon which future research challenges and directions are postulated.
Resource-aware Cyber Deception in Cloud-Native Environments

Cyber deception can be a valuable addition to traditional cyber defense mechanisms, especially for modern cloud-native environments with a fading security perimeter. However, pre-built decoys used in classical computer networks are not effective in detecting and mitigating malicious actors due to their inability to blend with the variety of applications in such environments. On the other hand, decoys cloning the deployed microservices of an application can offer a high-fidelity deception mechanism to intercept ongoing attacks within production environments. However, to fully benefit from this approach, it is essential to use a limited amount of decoy resources and devise a suitable cloning strategy to minimize the impact on legitimate services performance. Following this observation, we formulate a non-linear integer optimization problem that maximizes the number of attack paths intercepted by the allocated decoys within a fixed resource budget. Attack paths represent the attacker's movements within the infrastructure as a sequence of violated microservices. We also design a heuristic decoy placement algorithm to approximate the optimal solution and overcome the computational complexity of the proposed formulation. We evaluate the performance of the optimal and heuristic solutions against other schemes that use local vulnerability metrics to select which microservices to clone as decoys. Our results show that the proposed allocation strategy achieves a higher number of intercepted attack paths compared to these schemes while requiring approximately the same number of decoys.
DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training

Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zero-shot transfer capability in many discriminative tasks. Their adaptation to zero-shot image-conditioned text generation tasks has drawn increasing interest. Prior arts approach to zero-shot captioning by either utilizing the existing large language models (e.g., GPT-2) or pre-training the encoder-decoder network in an end-to-end manner. In this work, we propose a simple framework, named DeCap, for zero-shot captioning. We introduce a lightweight visual-aware language decoder. This decoder is both data-efficient and computation-efficient: 1) it only requires the text data for training, easing the burden on the collection of paired data. 2) it does not require end-to-end training. When trained with text-only data, the decoder takes the text embedding extracted from the off-the-shelf CLIP encoder as a prefix embedding. The challenge is that the decoder is trained on the text corpus but at the inference stage, it needs to generate captions based on visual inputs. The modality gap issue is widely observed in multi-modal contrastive models that prevents us from directly taking the visual embedding as the prefix embedding. We propose a training-free mechanism to reduce the modality gap. We project the visual embedding into the CLIP text embedding space, while the projected embedding retains the information of the visual input. Taking the projected embedding as the prefix embedding, the decoder generates high-quality descriptions that match the visual input. The experiments show that DeCap outperforms other zero-shot captioning methods and unpaired captioning methods on the typical image captioning benchmarks, i.e., MSCOCO and NoCaps.
Virtual Sparse Convolution for Multimodal 3D Object Detection

Recently, virtuall pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inaccurate depth completion significantly degrade detection precision. This paper proposes a fast yet effective backbone, termed Vir-ConvNet, based on a new operator VirConv (Virtual Sparse Convolution), for virtual-point-based 3D object detection. VirConv consists of two key designs: (1) StVD (Stochastic Voxel Discard) and (2) NRConv (Noise-Resistant Sub-manifold Convolution). StVD alleviates the computation problem by discarding large amounts of nearby redundant voxels. NRConv tackles the noise problem by encoding voxel features in both 2D image and 3D LiDAR space. By integrating VirConv, we first develop an efficient pipeline VirConv-L based on an early fusion design. Then, we build a high-precision pipeline Vir Conv-T based on a transformed refinement scheme. Finally, we develop a semi-supervised pipeline VirConv-S based on a pseudo-label framework. On the KITTI car 3D detection test leader-board, our VirConv-L achieves 85% AP with a fast running speed of 56ms. Our VirConv-T and VirConv-S attains a high-precision of 86.3% and 87.2% AP, and currently rank 2nd and 1st11On the date of CVPR deadline, i. e. , Nov.11, 2022, respectively. The code is available at https://github.com/hailanyi/VirConv.