OpenRaiser/Intern-Atlas huggingface dataset:
Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of $\geq$M1.0-class flares within 24 hours. We leveraged custom data augmentation and sample weighting to counter the inherent class-imbalance problem and used true skill statistic and Heidke skill score as evaluation metrics. Recent advancements in gradient-based attention methods allow us to interpret models by sending gradient signals to assign the burden of the decision on the input features. We interpret our model using three post hoc attention methods: (i) Guided Gradient-weighted Class Activation Mapping, (ii) Deep Shapley Additive Explanations, and (iii) Integrated Gradients. Our analysis shows that full-disk predictions of solar flares align with characteristics related to the active regions. The key findings of this study are: (1) We demonstrate that our full disk model can tangibly locate and predict near-limb solar flares, which is a critical feature for operational flare forecasting, (2) Our candidate model achieves an average TSS=0.51$\pm$0.05 and HSS=0.38$\pm$0.08, and (3) Our evaluation suggests that these models can learn conspicuous features corresponding to active regions from full-disk magnetograms.
Synthesizing Programmatic Policies with Actor-Critic Algorithms and ReLU Networks
Venue: arXiv cs.LG 2023
Year: 2023

Programmatically Interpretable Reinforcement Learning (PIRL) encodes policies in human-readable computer programs. Novel algorithms were recently introduced with the goal of handling the lack of gradient signal to guide the search in the space of programmatic policies. Most of such PIRL algorithms first train a neural policy that is used as an oracle to guide the search in the programmatic space. In this paper, we show that such PIRL-specific algorithms are not needed, depending on the language used to encode the programmatic policies. This is because one can use actor-critic algorithms to directly obtain a programmatic policy. We use a connection between ReLU neural networks and oblique decision trees to translate the policy learned with actor-critic algorithms into programmatic policies. This translation from ReLU networks allows us to synthesize policies encoded in programs with if-then-else structures, linear transformations of the input values, and PID operations. Empirical results on several control problems show that this translation approach is capable of learning short and effective policies. Moreover, the translated policies are at least competitive and often far superior to the policies PIRL algorithms synthesize.
Semi-supervised Contrastive Regression for Estimation of Eye Gaze
Venue: arXiv cs.CV 2023
Year: 2023

With the escalated demand of human-machine interfaces for intelligent systems, development of gaze controlled system have become a necessity. Gaze, being the non-intrusive form of human interaction, is one of the best suited approach. Appearance based deep learning models are the most widely used for gaze estimation. But the performance of these models is entirely influenced by the size of labeled gaze dataset and in effect affects generalization in performance. This paper aims to develop a semi-supervised contrastive learning framework for estimation of gaze direction. With a small labeled gaze dataset, the framework is able to find a generalized solution even for unseen face images. In this paper, we have proposed a new contrastive loss paradigm that maximizes the similarity agreement between similar images and at the same time reduces the redundancy in embedding representations. Our contrastive regression framework shows good performance in comparison to several state of the art contrastive learning techniques used for gaze estimation.
DiSPEL: Distributed Security Policy Enforcement for Bus-based SoC
Venue: arXiv cs.CR 2023
Year: 2023

The current zero trust model adopted in System-on-Chip (SoC) design is vulnerable to various malicious entities, and modern SoC designs must incorporate various security policies to protect sensitive assets from unauthorized access. These policies involve complex interactions between multiple IP blocks, which poses challenges for SoC designers and security experts when implementing these policies and for system validators when ensuring compliance. Difficulties arise when upgrading policies, reusing IPs for systems targeting different security requirements, and the subsequent increase in design time and time-to-market. This paper proposes a generic and flexible framework, called DiSPEL, for enforcing security policies defined by the user represented in a formal way for any bus-based SoC design. It employs a distributed deployment strategy while ensuring trusted bus operations despite the presence of untrusted IPs. It relies on incorporating a dedicated, centralized module capable of implementing diverse security policies involving bus-level interactions while generating the necessary logic and appending in the bus-level wrapper for IP-level policies. The proposed architecture is generic and independent of specific security policy types supporting both synthesizable and non-synthesizable solutions. The experimental results demonstrate its effectiveness and correctness in enforcing the security requirements and viability due to low overhead in terms of area, delay, and power consumption tested on open-source standard SoC benchmarks.
OBESEYE: Interpretable Diet Recommender for Obesity Management using Machine Learning and Explainable AI
Venue: arXiv cs.LG 2023
Year: 2023

Obesity, the leading cause of many non-communicable diseases, occurs mainly for eating more than our body requirements and lack of proper activity. So, being healthy requires heathy diet plans, especially for patients with comorbidities. But it is difficult to figure out the exact quantity of each nutrient because nutrients requirement varies based on physical and disease conditions. In our study we proposed a novel machine learning based system to predict the amount of nutrients one individual requires for being healthy. We applied different machine learning algorithms: linear regression, support vector machine (SVM), decision tree, random forest, XGBoost, LightGBM on fluid and 3 other major micronutrients: carbohydrate, protein, fat consumption prediction. We achieved high accuracy with low root mean square error (RMSE) by using linear regression in fluid prediction, random forest in carbohydrate prediction and LightGBM in protein and fat prediction. We believe our diet recommender system, OBESEYE, is the only of its kind which recommends diet with the consideration of comorbidities and physical conditions and promote encouragement to get rid of obesity.
VoxelMap++: Mergeable Voxel Mapping Method for Online LiDAR(-inertial) Odometry
Venue: arXiv cs.RO 2023
Year: 2023

This paper presents VoxelMap++: a voxel mapping method with plane merging which can effectively improve the accuracy and efficiency of LiDAR(-inertial) based simultaneous localization and mapping (SLAM). This map is a collection of voxels that contains one plane feature with 3DOF representation and corresponding covariance estimation. Considering total map will contain a large number of coplanar features (kid planes), these kid planes' 3DOF estimation can be regarded as the measurements with covariance of a larger plane (father plane). Thus, we design a plane merging module based on union-find which can save resources and further improve the accuracy of plane fitting. This module can distinguish the kid planes in different voxels and merge these kid planes to estimate the father plane. After merging, the father plane 3DOF representation will be more accurate than the kids plane and the uncertainty will decrease significantly which can further improve the performance of LiDAR(-inertial) odometry. Experiments on challenging environments such as corridors and forests demonstrate the high accuracy and efficiency of our method compared to other state-of-the-art methods (see our attached video). By the way, our implementation VoxelMap++ is open-sourced on GitHub which is applicable for both non-repetitive scanning LiDARs and traditional scanning LiDAR.
Artificial Intelligence for Molecular Communication
Venue: arXiv cs.ET 2023
Year: 2023

Molecular communication is a novel approach for data transmission between miniaturized devices, especially in contexts where electrical signals are to be avoided. The communication is based on sending molecules (or other particles) at nano scale through channel instead sending electrons over a wire. Molecular communication devices have a large potential in medical applications as they offer an alternative to antenna-based transmission systems that may not be applicable due to size, temperature, or radiation constraints. The communication is achieved by transforming a digital signal into concentrations of molecules. These molecules are then detected at the other end of the communication channel and transformed back into a digital signal. Accurately modeling the transmission channel is often not possible which may be due to a lack of data or time-varying parameters of the channel (e. g., the movements of a person wearing a medical device). This makes demodulation of the signal very difficult. Many approaches for demodulation have been discussed with one particular approach having tremendous success: artificial neural networks. These networks imitate the decision process in the human brain and are capable of reliably classifying noisy input data. Training such a network relies on a large set of training data. As molecular communication as a technology is still in its early development phase, this data is not always readily available. We discuss neural network-based demodulation approaches relying on synthetic data based on theoretical channel models as well as works using actual measurements produced by a prototype test bed. In this work, we give a general overview over the field molecular communication, discuss the challenges in the demodulations process of transmitted signals, and present approaches to these challenges that are based on artificial neural networks.
Deep Image Harmonization in Dual Color Spaces
Venue: arXiv cs.CV 2023
Year: 2023

Image harmonization is an essential step in image composition that adjusts the appearance of composite foreground to address the inconsistency between foreground and background. Existing methods primarily operate in correlated $RGB$ color space, leading to entangled features and limited representation ability. In contrast, decorrelated color space (e.g., $Lab$) has decorrelated channels that provide disentangled color and illumination statistics. In this paper, we explore image harmonization in dual color spaces, which supplements entangled $RGB$ features with disentangled $L$, $a$, $b$ features to alleviate the workload in harmonization process. The network comprises a $RGB$ harmonization backbone, an $Lab$ encoding module, and an $Lab$ control module. The backbone is a U-Net network translating composite image to harmonized image. Three encoders in $Lab$ encoding module extract three control codes independently from $L$, $a$, $b$ channels, which are used to manipulate the decoder features in harmonization backbone via $Lab$ control module. Our code and model are available at \href{https://github.com/bcmi/DucoNet-Image-Harmonization}{https://github.com/bcmi/DucoNet-Image-Harmonization}.
A Comprehensive Analysis of Real-World Image Captioning and Scene Identification
Venue: arXiv cs.CV 2023
Year: 2023

Image captioning is a computer vision task that involves generating natural language descriptions for images. This method has numerous applications in various domains, including image retrieval systems, medicine, and various industries. However, while there has been significant research in image captioning, most studies have focused on high quality images or controlled environments, without exploring the challenges of real-world image captioning. Real-world image captioning involves complex and dynamic environments with numerous points of attention, with images which are often very poor in quality, making it a challenging task, even for humans. This paper evaluates the performance of various models that are built on top of different encoding mechanisms, language decoders and training procedures using a newly created real-world dataset that consists of over 800+ images of over 65 different scene classes, built using MIT Indoor scenes dataset. This dataset is captioned using the IC3 approach that generates more descriptive captions by summarizing the details that are covered by standard image captioning models from unique view-points of the image.
A logarithmic approximation algorithm for the activation edge multicover problem
Venue: arXiv cs.DS 2023
Year: 2023

In the Activation Edge-Multicover problem we are given a multigraph $G=(V,E)$ with activation costs $\{c_{e}^u,c_{e}^v\}$ for every edge $e=uv \in E$, and degree requirements $r=\{r_v:v \in V\}$. The goal is to find an edge subset $J \subseteq E$ of minimum activation cost $\sum_{v \in V}\max\{c_{uv}^v:uv \in J\}$,such that every $v \in V$ has at least $r_v$ neighbors in the graph $(V,J)$. Let $k= \max_{v \in V} r_v$ be the maximum requirement and let $θ=\max_{e=uv \in E} \frac{\max\{c_e^u,c_e^v\}}{\min\{c_e^u,c_e^v\}}$ be the maximum quotient between the two costs of an edge. For $θ=1$ the problem admits approximation ratio $O(\log k)$. For $k=1$ it generalizes the Set Cover problem (when $θ=\infty$), and admits a tight approximation ratio $O(\log n)$. This implies approximation ratio $O(k \log n)$ for general $k$ and $θ$, and no better approximation ratio was known. We obtain the first logarithmic approximation ratio $O(\log k +\log\min\{θ,n\})$, that bridges between the two known ratios -- $O(\log k)$ for $θ=1$ and $O(\log n)$ for $k=1$. This implies approximation ratio $O\left(\log k +\log\min\{θ,n\}\right) +β\cdot (θ+1)$ for the Activation $k$-Connected Subgraph problem, where $β$ is the best known approximation ratio for the ordinary min-cost version of the problem.
Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Domain transfer is a prevalent challenge in modern neural Information Retrieval (IR). To overcome this problem, previous research has utilized domain-specific manual annotations and synthetic data produced by consistency filtering to finetune a general ranker and produce a domain-specific ranker. However, training such consistency filters are computationally expensive, which significantly reduces the model efficiency. In addition, consistency filtering often struggles to identify retrieval intentions and recognize query and corpus distributions in a target domain. In this study, we evaluate a more efficient solution: replacing the consistency filter with either direct pseudo-labeling, pseudo-relevance feedback, or unsupervised keyword generation methods for achieving consistent filtering-free unsupervised dense retrieval. Our extensive experimental evaluations demonstrate that, on average, TextRank-based pseudo relevance feedback outperforms other methods. Furthermore, we analyzed the training and inference efficiency of the proposed paradigm. The results indicate that filtering-free unsupervised learning can continuously improve training and inference efficiency while maintaining retrieval performance. In some cases, it can even improve performance based on particular datasets.
RV-CURE: A RISC-V Capability Architecture for Full Memory Safety
Venue: arXiv cs.AR 2023
Year: 2023

Despite decades of efforts to resolve, memory safety violations are still persistent and problematic in modern systems. Various defense mechanisms have been proposed, but their deployment in real systems remains challenging because of performance, security, or compatibility concerns. In this paper, we propose RV-CURE, a RISC-V capability architecture that implements full-system support for full memory safety. For capability enforcement, we first propose a compiler technique, data-pointer tagging (DPT), applicable to protecting all memory types. It inserts a pointer tag in a pointer address and associates that tag with the pointer's capability metadata. DPT enforces a capability check for every memory access by a tagged pointer and thereby prevents illegitimate memory accesses. Furthermore, we investigate and present lightweight hardware extensions for DPT based on the open-source RISC-V BOOM processor. We observe that a capability-execution pipeline can be implemented in parallel with the existing memory-execution pipeline without intrusive modifications. With our seamless hardware integration, we achieve low-cost capability checks transparently performed in hardware. Altogether, we prototype RV-CURE as a synthesized RTL processor and conduct full-system evaluations on FPGAs running Linux OS. Our evaluations show that RV-CURE achieves strong memory safety at a 10.8% slowdown across the SPEC 2017 C/C++ workloads.
Spanish Pre-trained BERT Model and Evaluation Data
Venue: arXiv cs.CL 2023
Year: 2023

The Spanish language is one of the top 5 spoken languages in the world. Nevertheless, finding resources to train or evaluate Spanish language models is not an easy task. In this paper we help bridge this gap by presenting a BERT-based language model pre-trained exclusively on Spanish data. As a second contribution, we also compiled several tasks specifically for the Spanish language in a single repository much in the spirit of the GLUE benchmark. By fine-tuning our pre-trained Spanish model, we obtain better results compared to other BERT-based models pre-trained on multilingual corpora for most of the tasks, even achieving a new state-of-the-art on some of them. We have publicly released our model, the pre-training data, and the compilation of the Spanish benchmarks.
Learning Multiple Gaits within Latent Space for Quadruped Robots
Venue: arXiv cs.RO 2023
Year: 2023

Learning multiple gaits is non-trivial for legged robots, especially when encountering different terrains and velocity commands. In this work, we present an end-to-end training framework for learning multiple gaits for quadruped robots, tailored to the needs of robust locomotion, agile locomotion, and user's commands. A latent space is constructed concurrently by a gait encoder and a gait generator, which helps the agent to reuse multiple gait skills to achieve adaptive gait behaviors. To learn natural behaviors for multiple gaits, we design gait-dependent rewards that are constructed explicitly from gait parameters and implicitly from conditional adversarial motion priors (CAMP). We demonstrate such multiple gaits control on a quadruped robot Go1 with only proprioceptive sensors.
Customizing Textile and Tactile Skins for Interactive Industrial Robots
Venue: arXiv cs.RO 2023
Year: 2023

Tactile skins made from textiles enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments, and can provide unique opportunities in robot control and learning, making them a promising technology for enhancing robot perception and interaction.
Improving Domain-Specific Retrieval by NLI Fine-Tuning
Venue: arXiv cs.CL 2023
Year: 2023

The aim of this article is to investigate the fine-tuning potential of natural language inference (NLI) data to improve information retrieval and ranking. We demonstrate this for both English and Polish languages, using data from one of the largest Polish e-commerce sites and selected open-domain datasets. We employ both monolingual and multilingual sentence encoders fine-tuned by a supervised method utilizing contrastive loss and NLI data. Our results point to the fact that NLI fine-tuning increases the performance of the models in both tasks and both languages, with the potential to improve mono- and multilingual models. Finally, we investigate uniformity and alignment of the embeddings to explain the effect of NLI-based fine-tuning for an out-of-domain use-case.
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we investigate the vulnerability of MDE to adversarial patches. We propose a novel \underline{S}tealthy \underline{A}dversarial \underline{A}ttacks on \underline{M}DE (SAAM) that compromises MDE by either corrupting the estimated distance or causing an object to seamlessly blend into its surroundings. Our experiments, demonstrate that the designed stealthy patch successfully causes a DNN-based MDE to misestimate the depth of objects. In fact, our proposed adversarial patch achieves a significant 60\% depth error with 99\% ratio of the affected region. Importantly, despite its adversarial nature, the patch maintains a naturalistic appearance, making it inconspicuous to human observers. We believe that this work sheds light on the threat of adversarial attacks in the context of MDE on edge devices. We hope it raises awareness within the community about the potential real-life harm of such attacks and encourages further research into developing more robust and adaptive defense mechanisms.
Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing
Venue: arXiv cs.MS 2023
Year: 2023

This report provides an introduction to the Bandicoot C++ library for linear algebra and scientific computing on GPUs, overviewing its user interface and performance characteristics, as well as the technical details of its internal design. Bandicoot is the GPU-enabled counterpart to the well-known Armadillo C++ linear algebra library, aiming to allow users to take advantage of GPU-accelerated computation for their existing codebases without significant changes. Adapting the same internal template meta-programming techniques that Armadillo uses, Bandicoot is able to provide compile-time optimisation of mathematical expressions within user code. The library is ready for production use and is available at https://coot.sourceforge.io. Bandicoot is distributed under the Apache 2.0 License.
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework
Venue: arXiv cs.CV 2023
Year: 2023

We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://github.com/tongyuantongyu/vs-NNVISR.