OpenRaiser/Intern-Atlas huggingface dataset:
DeBERTinha: A Multistep Approach to Adapt DebertaV3 XSmall for Brazilian Portuguese Natural Language Processing Task
Venue: arXiv cs.CL 2023
Year: 2023

This paper presents an approach for adapting the DebertaV3 XSmall model pre-trained in English for Brazilian Portuguese natural language processing (NLP) tasks. A key aspect of the methodology involves a multistep training process to ensure the model is effectively tuned for the Portuguese language. Initial datasets from Carolina and BrWac are preprocessed to address issues like emojis, HTML tags, and encodings. A Portuguese-specific vocabulary of 50,000 tokens is created using SentencePiece. Rather than training from scratch, the weights of the pre-trained English model are used to initialize most of the network, with random embeddings, recognizing the expensive cost of training from scratch. The model is fine-tuned using the replaced token detection task in the same format of DebertaV3 training. The adapted model, called DeBERTinha, demonstrates effectiveness on downstream tasks like named entity recognition, sentiment analysis, and determining sentence relatedness, outperforming BERTimbau-Large in two tasks despite having only 40M parameters.
Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks
Venue: arXiv cs.RO 2023
Year: 2023

Stowing, the task of placing objects in cluttered shelves or bins, is a common task in warehouse and manufacturing operations. However, this task is still predominantly carried out by human workers as stowing is challenging to automate due to the complex multi-object interactions and long-horizon nature of the task. Previous works typically involve extensive data collection and costly human labeling of semantic priors across diverse object categories. This paper presents a method to learn a generalizable robot stowing policy from predictive model of object interactions and a single demonstration with behavior primitives. We propose a novel framework that utilizes Graph Neural Networks to predict object interactions within the parameter space of behavioral primitives. We further employ primitive-augmented trajectory optimization to search the parameters of a predefined library of heterogeneous behavioral primitives to instantiate the control action. Our framework enables robots to proficiently execute long-horizon stowing tasks with a few keyframes (3-4) from a single demonstration. Despite being solely trained in a simulation, our framework demonstrates remarkable generalization capabilities. It efficiently adapts to a broad spectrum of real-world conditions, including various shelf widths, fluctuating quantities of objects, and objects with diverse attributes such as sizes and shapes.
On the Contractivity of Plug-and-Play Operators
Venue: arXiv cs.CV 2023
Year: 2023

In plug-and-play (PnP) regularization, the proximal operator in algorithms such as ISTA and ADMM is replaced by a powerful denoiser. This formal substitution works surprisingly well in practice. In fact, PnP has been shown to give state-of-the-art results for various imaging applications. The empirical success of PnP has motivated researchers to understand its theoretical underpinnings and, in particular, its convergence. It was shown in prior work that for kernel denoisers such as the nonlocal means, PnP-ISTA provably converges under some strong assumptions on the forward model. The present work is motivated by the following questions: Can we relax the assumptions on the forward model? Can the convergence analysis be extended to PnP-ADMM? Can we estimate the convergence rate? In this letter, we resolve these questions using the contraction mapping theorem: (i) for symmetric denoisers, we show that (under mild conditions) PnP-ISTA and PnP-ADMM exhibit linear convergence; and (ii) for kernel denoisers, we show that PnP-ISTA and PnP-ADMM converge linearly for image inpainting. We validate our theoretical findings using reconstruction experiments.
SIMD-ified R-tree Query Processing and Optimization
Venue: arXiv cs.DB 2023
Year: 2023

The introduction of Single Instruction Multiple Data (SIMD) instructions in mainstream CPUs has enabled modern database engines to leverage data parallelism by performing more computation with a single instruction, resulting in a reduced number of instructions required to execute a query as well as the elimination of conditional branches. Though SIMD in the context of traditional database engines has been studied extensively, it has been overlooked in the context of spatial databases. In this paper, we investigate how spatial database engines can benefit from SIMD vectorization in the context of an R-tree spatial index. We present vectorized versions of the spatial range select, and spatial join operations over a vectorized R-tree index. For each of the operations, we investigate two storage layouts for an R-tree node to leverage SIMD instructions. We design vectorized algorithms for each of the spatial operations given each of the two data layouts. We show that the introduction of SIMD can improve the latency of the spatial query operators up to 9x. We introduce several optimizations over the vectorized implementation of these query operators, and study their effectiveness in query performance and various hardware performance counters under different scenarios.
Lifting the Fog of Uncertainties: Dynamic Resource Orchestration for the Containerized Cloud
Venue: arXiv cs.DC 2023
Year: 2023

The advances in virtualization technologies have sparked a growing transition from virtual machine (VM)-based to container-based infrastructure for cloud computing. From the resource orchestration perspective, containers' lightweight and highly configurable nature not only enables opportunities for more optimized strategies, but also poses greater challenges due to additional uncertainties and a larger configuration parameter search space. Towards this end, we propose Drone, a resource orchestration framework that adaptively configures resource parameters to improve application performance and reduce operational cost in the presence of cloud uncertainties. Built on Contextual Bandit techniques, Drone is able to achieve a balance between performance and resource cost on public clouds, and optimize performance on private clouds where a hard resource constraint is present. We show that our algorithms can achieve sub-linear growth in cumulative regret, a theoretically sound convergence guarantee, and our extensive experiments show that Drone achieves an up to 45% performance improvement and a 20% resource footprint reduction across batch processing jobs and microservice workloads.
SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features
Venue: arXiv cs.CV 2023
Year: 2023

In comparison to conventional RGB cameras, the superior temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates for object tracking. Yet in practice, despite their theoretical advantages, the body of work on event-based multi-object tracking (MOT) remains in its infancy, especially in real-world settings where events from complex background and camera motion can easily obscure the true target motion. In this work, an event-based multi-object tracker, called SpikeMOT, is presented to address these challenges. SpikeMOT leverages spiking neural networks to extract sparse spatiotemporal features from event streams associated with objects. The resulting spike train representations are used to track the object movement at high frequency, while a simultaneous object detector provides updated spatial information of these objects at an equivalent frame rate. To evaluate the effectiveness of SpikeMOT, we introduce DSEC-MOT, the first large-scale event-based MOT benchmark incorporating fine-grained annotations for objects experiencing severe occlusions, frequent trajectory intersections, and long-term re-identification in real-world contexts. Extensive experiments employing DSEC-MOT and another event-based dataset, named FE240hz, demonstrate SpikeMOT's capability to achieve high tracking accuracy amidst challenging real-world scenarios, advancing the state-of-the-art in event-based multi-object tracking.
CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning
Venue: arXiv cs.RO 2023
Year: 2023

Human motion driven control (HMDC) is an effective approach for generating natural and compelling robot motions while preserving high-level semantics. However, establishing the correspondence between humans and robots with different body structures is not straightforward due to the mismatches in kinematics and dynamics properties, which causes intrinsic ambiguity to the problem. Many previous algorithms approach this motion retargeting problem with unsupervised learning, which requires the prerequisite skill sets. However, it will be extremely costly to learn all the skills without understanding the given human motions, particularly for high-dimensional robots. In this work, we introduce CrossLoco, a guided unsupervised reinforcement learning framework that simultaneously learns robot skills and their correspondence to human motions. Our key innovation is to introduce a cycle-consistency-based reward term designed to maximize the mutual information between human motions and robot states. We demonstrate that the proposed framework can generate compelling robot motions by translating diverse human motions, such as running, hopping, and dancing. We quantitatively compare our CrossLoco against the manually engineered and unsupervised baseline algorithms along with the ablated versions of our framework and demonstrate that our method translates human motions with better accuracy, diversity, and user preference. We also showcase its utility in other applications, such as synthesizing robot movements from language input and enabling interactive robot control.
On Uniform Scalar Quantization for Learned Image Compression
Venue: arXiv cs.CV 2023
Year: 2023

Learned image compression possesses a unique challenge when incorporating non-differentiable quantization into the gradient-based training of the networks. Several quantization surrogates have been proposed to fulfill the training, but they were not systematically justified from a theoretical perspective. We fill this gap by contrasting uniform scalar quantization, the most widely used category with rounding being its simplest case, and its training surrogates. In principle, we find two factors crucial: one is the discrepancy between the surrogate and rounding, leading to train-test mismatch; the other is gradient estimation risk due to the surrogate, which consists of bias and variance of the gradient estimation. Our analyses and simulations imply that there is a tradeoff between the train-test mismatch and the gradient estimation risk, and the tradeoff varies across different network structures. Motivated by these analyses, we present a method based on stochastic uniform annealing, which has an adjustable temperature coefficient to control the tradeoff. Moreover, our analyses enlighten us as to two subtle tricks: one is to set an appropriate lower bound for the variance parameter of the estimated quantized latent distribution, which effectively reduces the train-test mismatch; the other is to use zero-center quantization with partial stop-gradient, which reduces the gradient estimation variance and thus stabilize the training. Our method with the tricks is verified to outperform the existing practices of quantization surrogates on a variety of representative image compression networks.
From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication
Venue: arXiv cs.LG 2023
Year: 2023

Due to the lack of a feedback channel in the C-V2X sidelink, finding a suitable modulation and coding scheme (MCS) is a difficult task. However, recent use cases for vehicle-to-everything (V2X) communication with higher demands on data rate necessitate choosing the MCS adaptively. In this paper, we propose a machine learning approach to predict suitable MCS levels. Additionally, we propose the use of quantile prediction and evaluate it in combination with different algorithms for the task of predicting the MCS level with the highest achievable data rate. Thereby, we show significant improvements over conventional methods of choosing the MCS level. Using a machine learning approach, however, requires larger real-world data sets than are currently publicly available for research. For this reason, this paper presents a data set that was acquired in extensive drive tests, and that we make publicly available.
HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field
Venue: arXiv cs.CV 2023
Year: 2023

The problem of modeling an animatable 3D human head avatar under light-weight setups is of significant importance but has not been well solved. Existing 3D representations either perform well in the realism of portrait images synthesis or the accuracy of expression control, but not both. To address the problem, we introduce a novel hybrid explicit-implicit 3D representation, Facial Model Conditioned Neural Radiance Field, which integrates the expressiveness of NeRF and the prior information from the parametric template. At the core of our representation, a synthetic-renderings-based condition method is proposed to fuse the prior information from the parametric model into the implicit field without constraining its topological flexibility. Besides, based on the hybrid representation, we properly overcome the inconsistent shape issue presented in existing methods and improve the animation stability. Moreover, by adopting an overall GAN-based architecture using an image-to-image translation network, we achieve high-resolution, realistic and view-consistent synthesis of dynamic head appearance. Experiments demonstrate that our method can achieve state-of-the-art performance for 3D head avatar animation compared with previous methods.
Compromise in Multilateral Negotiations and the Global Regulation of Artificial Intelligence
Venue: arXiv cs.CY 2023
Year: 2023

As artificial intelligence (AI) technologies spread worldwide, international discussions have increasingly focused on their consequences for democracy, human rights, fundamental freedoms, security, and economic and social development. In this context, UNESCO's Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, has emerged as the first global normative framework for AI development and deployment. The intense negotiations of every detail of the document brought forth numerous controversies among UNESCO member states. Drawing on a unique set of primary sources, including written positions and recorded deliberations, this paper explains the achievement of global compromise on AI regulation despite the multiplicity of UNESCO member-state positions representing a variety of liberal and sovereignist preferences. Building upon Boltanski's pragmatic sociology, it conceptualises the practice of multilateral negotiations and attributes the multilateral compromise to two embedded therein mechanisms: Structural normative hybridity and situated normative ambiguity allowed to accomplish a compromise by linking macro-normative structures with situated debates of multilateral negotiations.
Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display
Venue: arXiv cs.MM 2023
Year: 2023

ITM(inverse tone-mapping) converts SDR (standard dynamic range) footage to HDR/WCG (high dynamic range /wide color gamut) for media production. It happens not only when remastering legacy SDR footage in front-end content provider, but also adapting on-theair SDR service on user-end HDR display. The latter requires more efficiency, thus the pre-calculated LUT (look-up table) has become a popular solution. Yet, conventional fixed LUT lacks adaptability, so we learn from research community and combine it with AI. Meanwhile, higher-bit-depth HDR/WCG requires larger LUT than SDR, so we consult traditional ITM for an efficiency-performance trade-off: We use 3 smaller LUTs, each has a non-uniform packing (precision) respectively denser in dark, middle and bright luma range. In this case, their results will have less error only in their own range, so we use a contribution map to combine their best parts to final result. With the guidance of this map, the elements (content) of 3 LUTs will also be redistributed during training. We conduct ablation studies to verify method's effectiveness, and subjective and objective experiments to show its practicability. Code is available at: https://github.com/AndreGuo/ITMLUT.
An evaluation of GPT models for phenotype concept recognition
Venue: arXiv cs.CL 2023
Year: 2023

Objective: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation. Materials and Methods: The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations. Results: Our results show that, with an appropriate setup, these models can achieve state of the art performance. The best run, using few-shot learning, achieved 0.58 macro F1 score on publication abstracts and 0.75 macro F1 score on clinical observations, the former being comparable with the state of the art, while the latter surpassing the current best in class tool. Conclusion: While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing raw data samples. Although in many practical scenarios the derivatives of the objective function are not available, only few works have considered the federated zeroth-order setting, in which functions can only be accessed through a budgeted number of point evaluations. In this work we focus on convex optimization and design the first federated zeroth-order algorithm to estimate the curvature of the global objective, with the purpose of achieving superlinear convergence. We take an incremental Hessian estimator whose error norm converges linearly, and we adapt it to the federated zeroth-order setting, sampling the random search directions from the Stiefel manifold for improved performance. In particular, both the gradient and Hessian estimators are built at the central server in a communication-efficient and privacy-preserving way by leveraging synchronized pseudo-random number generators. We provide a theoretical analysis of our algorithm, named FedZeN, proving local quadratic convergence with high probability and global linear convergence up to zeroth-order precision. Numerical simulations confirm the superlinear convergence rate and show that our algorithm outperforms the federated zeroth-order methods available in the literature.
Grand Perspective: Load Shedding in Distributed CEP Applications
Venue: arXiv cs.DC 2023
Year: 2023

In distributed Complex Event Processing (CEP) applications with high load but limited resources, bottleneck operators in the operator graph can significantly slow down processing of event streams, thus compelling the need to shed load. A high-quality load shedding strategy that resolves the bottleneck with high output quality evaluates each event's importance with regards to the application's final output and drops less important events from the event stream for the benefit of important ones. So far, no solution has been proposed that is able to permit good load shedding in distributed, multi-operator CEP applications. On one hand, shedding strategies have been proposed for single-operator CEP applications that can measure an event's importance immediately at the bottleneck operator, only, and thereby ignore the effect of other streams in the application on an event's importance. On the other hand, shedding strategies have been proposed for applications with multiple operators from the area of stream processing that provide a fixed selectivity which is not given in the conditional CEP operators. We, therefore, propose a load-shedding solution for distributed CEP applications that maximizes the application's final output and ensures timely processing of important events by using a set of CEP-tailored selectivity functions and a linear program, which is an abstraction of the CEP application. Moreover, our solution ensures a quality optimal shedder configuration even in the presence of dynamically changing conditions. With the help of extensive evaluations on both synthetic and real data, we show that our solution successfully resolves overload at bottleneck operators and at the same time maximizes the quality of the application's output.
Meta Reinforcement Learning for Fast Spectrum Sharing in Vehicular Networks
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we investigate the problem of fast spectrum sharing in vehicle-to-everything communication. In order to improve the spectrum efficiency of the whole system, the spectrum of vehicle-to-infrastructure links is reused by vehicle-to-vehicle links. To this end, we model it as a problem of deep reinforcement learning and tackle it with proximal policy optimization. A considerable number of interactions are often required for training an agent with good performance, so simulation-based training is commonly used in communication networks. Nevertheless, severe performance degradation may occur when the agent is directly deployed in the real world, even though it can perform well on the simulator, due to the reality gap between the simulation and the real environments. To address this issue, we make preliminary efforts by proposing an algorithm based on meta reinforcement learning. This algorithm enables the agent to rapidly adapt to a new task with the knowledge extracted from similar tasks, leading to fewer interactions and less training time. Numerical results show that our method achieves near-optimal performance and exhibits rapid convergence.
Effect of structure-based training on 3D localization precision and quality
Venue: arXiv cs.CV 2023
Year: 2023

This study introduces a structural-based training approach for CNN-based algorithms in single-molecule localization microscopy (SMLM) and 3D object reconstruction. We compare this approach with the traditional random-based training method, utilizing the LUENN package as our AI pipeline. The quantitative evaluation demonstrates significant improvements in detection rate and localization precision with the structural-based training approach, particularly in varying signal-to-noise ratios (SNRs). Moreover, the method effectively removes checkerboard artifacts, ensuring more accurate 3D reconstructions. Our findings highlight the potential of the structural-based training approach to advance super-resolution microscopy and deepen our understanding of complex biological systems at the nanoscale.