OpenRaiser/Intern-Atlas huggingface dataset:
Mesh Represented Recycle Learning for 3D Hand Pose and Mesh Estimation
Venue: arXiv cs.CV 2023
Year: 2023

In general, hand pose estimation aims to improve the robustness of model performance in the real-world scenes. However, it is difficult to enhance the robustness since existing datasets are obtained in restricted environments to annotate 3D information. Although neural networks quantitatively achieve a high estimation accuracy, unsatisfied results can be observed in visual quality. This discrepancy between quantitative results and their visual qualities remains an open issue in the hand pose representation. To this end, we propose a mesh represented recycle learning strategy for 3D hand pose and mesh estimation which reinforces synthesized hand mesh representation in a training phase. To be specific, a hand pose and mesh estimation model first predicts parametric 3D hand annotations (i.e., 3D keypoint positions and vertices for hand mesh) with real-world hand images in the training phase. Second, synthetic hand images are generated with self-estimated hand mesh representations. After that, the synthetic hand images are fed into the same model again. Thus, the proposed learning strategy simultaneously improves quantitative results and visual qualities by reinforcing synthetic mesh representation. To encourage consistency between original model output and its recycled one, we propose self-correlation loss which maximizes the accuracy and reliability of our learning strategy. Consequently, the model effectively conducts self-refinement on hand pose estimation by learning mesh representation from its own output. To demonstrate the effectiveness of our learning strategy, we provide extensive experiments on FreiHAND dataset. Notably, our learning strategy improves the performance on hand pose and mesh estimation without any extra computational burden during the inference.
DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors
Venue: arXiv cs.CV 2023
Year: 2023

Animating a still image offers an engaging visual experience. Traditional image animation techniques mainly focus on animating natural scenes with stochastic dynamics (e.g. clouds and fluid) or domain-specific motions (e.g. human hair or body motions), and thus limits their applicability to more general visual content. To overcome this limitation, we explore the synthesis of dynamic content for open-domain images, converting them into animated videos. The key idea is to utilize the motion prior of text-to-video diffusion models by incorporating the image into the generative process as guidance. Given an image, we first project it into a text-aligned rich context representation space using a query transformer, which facilitates the video model to digest the image content in a compatible fashion. However, some visual details still struggle to be preserved in the resultant videos. To supplement with more precise image information, we further feed the full image to the diffusion model by concatenating it with the initial noises. Experimental results show that our proposed method can produce visually convincing and more logical & natural motions, as well as higher conformity to the input image. Comparative evaluation demonstrates the notable superiority of our approach over existing competitors.
Exploring Indoor Health: An In-depth Field Study on the Indoor Air Quality Dynamics
Venue: arXiv cs.HC 2023
Year: 2023

Indoor air pollution, a significant driver of respiratory and cardiovascular diseases, claims 3.2 million lives yearly, according to the World Health Organization, highlighting the pressing need to address this global crisis. In contrast to unconstrained outdoor environments, room structures, floor plans, ventilation systems, and occupant activities all impact the accumulation and spread of pollutants. Yet, comprehensive in-the-wild empirical studies exploring these unique indoor air pollution patterns and scope are lacking. To address this, we conducted a three-month-long field study involving over 28 indoor spaces to delve into the complexities of indoor air pollution. Our study was conducted using our custom-built DALTON air quality sensor and monitoring system, an innovative IoT air quality monitoring solution that considers cost, sensor type, accuracy, network connectivity, power, and usability. Our study also revealed that conventional measures, such as the Indoor Air Quality Index (IAQI), don't fully capture complex indoor air quality dynamics. Hence, we proposed the Healthy Home Index (HHI), a new metric considering the context and household activities, offering a more comprehensive understanding of indoor air quality. Our findings suggest that HHI provides a more accurate air quality assessment, underscoring the potential for wide-scale deployment of our indoor air quality monitoring platform.
Towards Simpler Sorting Networks and Monotone Circuits for Majority
Venue: arXiv cs.CC 2023
Year: 2023

In this paper, we study the problem of computing the majority function by low-depth monotone circuits and a related problem of constructing low-depth sorting networks. We consider both the classical setting with elementary operations of arity $2$ and the generalized setting with operations of arity $k$, where $k$ is a parameter. For both problems and both settings, there are various constructions known, the minimal known depth being logarithmic. However, there is currently no known construction that simultaneously achieves sub-log-squared depth, effective constructability, simplicity, and has a potential to be used in practice. In this paper we make progress towards resolution of this problem. For computing majority by standard monotone circuits (gates of arity 2) we provide an explicit monotone circuit of depth $O(\log_2^{5/3} n)$. The construction is a combination of several known and not too complicated ideas. For arbitrary arity of gates $k$ we provide a new sorting network architecture inspired by representation of inputs as a high-dimensional cube. As a result we provide a simple construction that improves previous upper bound of $4 \log_k^2 n$ to $2 \log_k^2 n$. We prove the similar bound for the depth of the circuit computing majority of $n$ bits consisting of gates computing majority of $k$ bits. Note, that for both problems there is an explicit construction of depth $O(\log_k n)$ known, but the construction is complicated and the constant hidden in $O$-notation is huge.
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization
Venue: arXiv cs.LG 2023
Year: 2023

Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computational costs. The primary efficiency bottleneck in such optimizers is matrix inverse calculations in the preconditioning step, which are expensive to compute on GPUs. In this paper, we introduce Jorge, a second-order optimizer that promises the best of both worlds -- rapid convergence benefits of second-order methods, and high computational efficiency typical of first-order methods. We address the primary computational bottleneck of computing matrix inverses by completely eliminating them using an approximation of the preconditioner computation. This makes Jorge extremely efficient on GPUs in terms of wall-clock time. Further, we describe an approach to determine Jorge's hyperparameters directly from a well-tuned SGD baseline, thereby significantly minimizing tuning efforts. Our empirical evaluations demonstrate the distinct advantages of using Jorge, outperforming state-of-the-art optimizers such as SGD, AdamW, and Shampoo across multiple deep learning models, both in terms of sample efficiency and wall-clock time.
Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly
Venue: arXiv cs.CL 2023
Year: 2023

In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models. In this work, we adapt a recently proposed hardness metric, pointwise $\mathcal{V}$-usable information (PVI), to an in-context version (in-context PVI). Compared to the original PVI, in-context PVI is more efficient in that it requires only a few exemplars and does not require fine-tuning. We conducted a comprehensive empirical analysis to evaluate the reliability of in-context PVI. Our findings indicate that in-context PVI estimates exhibit similar characteristics to the original PVI. Specific to the in-context setting, we show that in-context PVI estimates remain consistent across different exemplar selections and numbers of shots. The variance of in-context PVI estimates across different exemplar selections is insignificant, which suggests that in-context PVI are stable. Furthermore, we demonstrate how in-context PVI can be employed to identify challenging instances. Our work highlights the potential of in-context PVI and provides new insights into the capabilities of ICL.
Quantum Computing for MIMO Beam Selection Problem: Model and Optical Experimental Solution
Venue: arXiv cs.NI 2023
Year: 2023

Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.
Performance Evaluation of Evolutionary Algorithms for Analog Integrated Circuit Design Optimisation
Venue: arXiv cs.NE 2023
Year: 2023

An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function that has resulted in faster convergence to the optimal solution. The algorithms are tuned and modified to converge to a better optimal solution with less standard deviation for multiple runs compared to standard versions. Modified versions of the artificial bee colony optimisation algorithm, genetic algorithm, grey wolf optimisation algorithm, and particle swarm optimisation algorithm are tested and compared for the optimal sizing of two operational amplifier topologies. An extensive performance evaluation of all the modified algorithms showed that the modifications have resulted in consistent performance with improved convergence for all the algorithms. The implementation of parallel computation in the algorithms has reduced run time. Among the considered algorithms, the modified artificial bee colony optimisation algorithm gave the most optimal solution with consistent results across multiple runs.
Large-Plaintext Functional Bootstrapping in FHE with Small Bootstrapping Keys
Venue: arXiv cs.CR 2023
Year: 2023

Functional bootstrapping is a core technique in Fully Homomorphic Encryption (FHE). For large plaintext, to evaluate a general function homomorphically over a ciphertext, in the FHEW/TFHE approach, since the function in look-up table form is encoded in the coefficients of a test polynomial, the degree of the polynomial must be high enough to hold the entire table. This increases the bootstrapping time complexity and memory cost, as the size of bootstrapping keys and keyswitching keys need to be large accordingly. In this paper, we propose to encode the look-up table of any function in a polynomial vector, whose coefficients can hold more data. The corresponding representation of the additive group Zq used in the RGSW-based bootstrapping is the group of monic monomial permutation matrices, which integrates the permutation matrix representation used by Alperin-Sheriff and Peikert in 2014, and the monic monomial representation used in the FHEW/TFHE scheme. We make comprehensive investigation of the new representation, and propose a new bootstrapping algorithm based on it. The new algorithm has the prominent benefit of small bootstrapping key size and small key-switching key size, which leads to polynomial factor improvement in key size, in addition to constant factor improvement in run-time cost.
Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher
Venue: arXiv cs.IR 2023
Year: 2023

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources, due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem. Aiming to create a "PageRank" for the LLM era, we strive to transform LLM into a relevant, responsible, and trustworthy searcher. We propose a novel generative retrieval framework leveraging the knowledge of LLMs to foster a direct link between queries and online sources. This framework consists of three core modules: Generator, Validator, and Optimizer, each focusing on generating trustworthy online sources, verifying source reliability, and refining unreliable sources, respectively. Extensive experiments and evaluations highlight our method's superior relevance, responsibility, and trustfulness against various SOTA methods.
Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights
Venue: arXiv cs.LG 2023
Year: 2023

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top \in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by minimizing the loss function $L(X)$. This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for the Localized Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's design from a security and privacy perspective. This work underscores the importance of understanding and safeguarding the internal workings of transformers to ensure the confidentiality of processed data.
WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets
Venue: arXiv cs.CV 2023
Year: 2023

Image classification is a crucial task in modern weed management and crop intervention technologies. However, the limited size, diversity, and balance of existing weed datasets hinder the development of deep learning models for generalizable weed identification. In addition, the expensive labelling requirements of mainstream fully-supervised weed classifiers make them cost- and time-prohibitive to deploy widely, for new weed species, and in site-specific weed management. This paper proposes a novel method for Weed Contrastive Learning through visual Representations (WeedCLR), that uses class-optimized loss with Von Neumann Entropy of deep representation for weed classification in long-tailed datasets. WeedCLR leverages self-supervised learning to learn rich and robust visual features without any labels and applies a class-optimized loss function to address the class imbalance problem in long-tailed datasets. WeedCLR is evaluated on two public weed datasets: CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed species. WeedCLR achieves an average accuracy improvement of 4.3\% on CottonWeedID15 and 5.6\% on DeepWeeds over previous methods. It also demonstrates better generalization ability and robustness to different environmental conditions than existing methods without the need for expensive and time-consuming human annotations. These significant improvements make WeedCLR an effective tool for weed classification in long-tailed datasets and allows for more rapid and widespread deployment of site-specific weed management and crop intervention technologies.
The weight enumerator polynomials of the lifted codes of the projective Solomon-Stiffler codes
Venue: arXiv cs.IT 2023
Year: 2023

Determining the weight distribution of a code is an old and fundamental topic in coding theory that has been thoroughly studied. In 1977, Helleseth, Kløve, and Mykkeltveit presented a weight enumerator polynomial of the lifted code over $\mathbb{F}_{q^\ell}$ of a $q$-ary linear code with significant combinatorial properties, which can determine the support weight distribution of this linear code. The Solomon-Stiffler codes are a family of famous Griesmer codes, which were proposed by Solomon and Stiffler in 1965. In this paper, we determine the weight enumerator polynomials of the lifted codes of the projective Solomon-Stiffler codes using some combinatorial properties of subspaces. As a result, we determine the support weight distributions of the projective Solomon-Stiffler codes. In particular, we determine the weight hierarchies of the projective Solomon-Stiffler codes.
Cormas: The Software for Participatory Modelling and its Application for Managing Natural Resources in Senegal
Venue: arXiv cs.MA 2023
Year: 2023

Cormas is an agent-based simulation platform developed in the late 90s by the Green research at CIRAD unit to support the management of natural resources and understand the interactions between natural and social dynamics. This platform is well-suited for a participatory simulation approach that empowers local stakeholders by including them in all modelling and knowledge-sharing steps. In this short paper, we present the Cormas platform and discuss its unique features and their importance for the participatory simulation approach. We then present the early results of our ongoing study on managing pastoral resources in the Sahel region, identify the problems faced by local stakeholders, and discuss the potential use of Cormas at the next stage of our study to collectively model and understand the effective ways of managing the shared agro-sylvo-pastoral resources.
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
Venue: arXiv cs.LG 2023
Year: 2023

The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relationships, diagnose diseases, and develop biomarkers using various data sources like magnetic resonance imaging and electroencephalography. The primary objective of ML is to build models that can make accurate predictions on unseen data. Researchers aim to prove the existence of such generalizable models by evaluating performance using techniques such as cross-validation (CV), which uses systematic subsampling to estimate the generalization performance. Choosing a CV scheme and evaluating an ML pipeline can be challenging and, if used improperly, can lead to overestimated results and incorrect interpretations. We created julearn, an open-source Python library, that allow researchers to design and evaluate complex ML pipelines without encountering in common pitfalls. In this manuscript, we present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects that can be easily implemented using this novel library. Julearn aims to simplify the entry into the ML world by providing an easy-to-use environment with built in guards against some of the most common ML pitfalls. With its design, unique features and simple interface, it poses as a useful Python-based library for research projects.
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Venue: arXiv cs.CV 2023
Year: 2023

Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about the lack of diversity in the generated imagery. To tackle this, we introduce Diverse Diffusion, a method for boosting image diversity beyond gender and ethnicity, spanning into richer realms, including color diversity.Diverse Diffusion is a general unsupervised technique that can be applied to existing text-to-image models. Our approach focuses on finding vectors in the Stable Diffusion latent space that are distant from each other. We generate multiple vectors in the latent space until we find a set of vectors that meets the desired distance requirements and the required batch size.To evaluate the effectiveness of our diversity methods, we conduct experiments examining various characteristics, including color diversity, LPIPS metric, and ethnicity/gender representation in images featuring humans.The results of our experiments emphasize the significance of diversity in generating realistic and varied images, offering valuable insights for improving text-to-image models. Through the enhancement of image diversity, our approach contributes to the creation of more inclusive and representative AI-generated art.
Benchmarking Function Hook Latency in Cloud-Native Environments
Venue: arXiv cs.SE 2023
Year: 2023

Researchers and engineers are increasingly adopting cloud-native technologies for application development and performance evaluation. While this has improved the reproducibility of benchmarks in the cloud, the complexity of cloud-native environments makes it difficult to run benchmarks reliably. Cloud-native applications are often instrumented or altered at runtime, by dynamically patching or hooking them, which introduces a significant performance overhead. Our work discusses the benchmarking-related pitfalls of the dominant cloud-native technology, Kubernetes, and how they affect performance measurements of dynamically patched or hooked applications. We present recommendations to mitigate these risks and demonstrate how an improper experimental setup can negatively impact latency measurements.