OpenRaiser/Intern-Atlas huggingface dataset:
Towards Robust Prompts on Vision-Language Models
Venue: arXiv cs.CV 2023
Year: 2023

With the advent of vision-language models (VLMs) that can perform in-context and prompt-based learning, how can we design prompting approaches that robustly generalize to distribution shift and can be used on novel classes outside the support set of the prompts? In this work, we first define two types of robustness to distribution shift on VLMs, namely, robustness on base classes (the classes included in the support set of prompts) and robustness on novel classes. Then, we study the robustness of existing in-context learning and prompt learning approaches, where we find that prompt learning performs robustly on test images from base classes, while it does not generalize well on images from novel classes. We propose robust prompt learning by integrating multiple-scale image features into the prompt, which improves both types of robustness. Comprehensive experiments are conducted to study the defined robustness on six benchmarks and show the effectiveness of our proposal.
Visual Instruction Tuning
Venue: arXiv cs.CV 2023
Year: 2023

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.
STRAP: Structured Object Affordance Segmentation with Point Supervision
Venue: arXiv cs.CV 2023
Year: 2023

With significant annotation savings, point supervision has been proven effective for numerous 2D and 3D scene understanding problems. This success is primarily attributed to the structured output space; i.e., samples with high spatial affinity tend to share the same labels. Sharing this spirit, we study affordance segmentation with point supervision, wherein the setting inherits an unexplored dual affinity-spatial affinity and label affinity. By label affinity, we refer to affordance segmentation as a multi-label prediction problem: A plate can be both holdable and containable. By spatial affinity, we refer to a universal prior that nearby pixels with similar visual features should share the same point annotation. To tackle label affinity, we devise a dense prediction network that enhances label relations by effectively densifying labels in a new domain (i.e., label co-occurrence). To address spatial affinity, we exploit a Transformer backbone for global patch interaction and a regularization loss. In experiments, we benchmark our method on the challenging CAD120 dataset, showing significant performance gains over prior methods.
Smart Home Environment Modelled with a Multi-Agent System
Venue: arXiv cs.MA 2023
Year: 2023

A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five locations in a house. The context-aware agents behave based on predefined rules designed for daily activities. Our proposal aims to reduce operational cost of running devices. In the future, monitors of health aspects belonging to home residents will sustain their healthy life daily.
The XAISuite framework and the implications of explanatory system dissonance
Venue: arXiv cs.LG 2023
Year: 2023

Explanatory systems make machine learning models more transparent. However, they are often inconsistent. In order to quantify and isolate possible scenarios leading to this discrepancy, this paper compares two explanatory systems, SHAP and LIME, based on the correlation of their respective importance scores using 14 machine learning models (7 regression and 7 classification) and 4 tabular datasets (2 regression and 2 classification). We make two novel findings. Firstly, the magnitude of importance is not significant in explanation consistency. The correlations between SHAP and LIME importance scores for the most important features may or may not be more variable than the correlation between SHAP and LIME importance scores averaged across all features. Secondly, the similarity between SHAP and LIME importance scores cannot predict model accuracy. In the process of our research, we construct an open-source library, XAISuite, that unifies the process of training and explaining models. Finally, this paper contributes a generalized framework to better explain machine learning models and optimize their performance.
Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing
Venue: arXiv cs.ET 2023
Year: 2023

Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-time neurons for neuromorphic computing. Schottky-Barrier MOSFET is fabricated on a Silicon-on-insulator platform with polycrystalline Silicon as the channel and Nickel/Platinum as the source/drain. The Poly-Si and Nickel make the back-to-back Schottky junction enabling ultra-low ON current required for energy-efficient neurons.
Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awareness
Venue: arXiv cs.CY 2023
Year: 2023

Algorithmically optimizing the provision of limited resources is commonplace across domains from healthcare to lending. Optimization can lead to efficient resource allocation, but, if deployed without additional scrutiny, can also exacerbate inequality. Little is known about popular preferences regarding acceptable efficiency-equity trade-offs, making it difficult to design algorithms that are responsive to community needs and desires. Here we examine this trade-off and concomitant preferences in the context of GetCalFresh, an online service that streamlines the application process for California's Supplementary Nutrition Assistance Program (SNAP, formerly known as food stamps). GetCalFresh runs online advertisements to raise awareness of their multilingual SNAP application service. We first demonstrate that when ads are optimized to garner the most enrollments per dollar, a disproportionately small number of Spanish speakers enroll due to relatively higher costs of non-English language advertising. Embedding these results in a survey (N = 1,532) of a diverse set of Americans, we find broad popular support for valuing equity in addition to efficiency: respondents generally preferred reducing total enrollments to facilitate increased enrollment of Spanish speakers. These results buttress recent calls to reevaluate the efficiency-centric paradigm popular in algorithmic resource allocation.
Hybrid Materialization in a Disk-Based Column-Store
Venue: arXiv cs.DB 2023
Year: 2023

In column-oriented query processing, a materialization strategy determines when lightweight positions (row IDs) are translated into tuples. It is an important part of column-store architecture, since it defines the class of supported query plans, and, therefore, impacts the overall system performance. In this paper we continue investigating materialization strategies for a distributed disk-based column-store. We start with demonstrating cases when existing approaches impose fundamental limitations on the resulting system performance. Then, in order to address them, we propose a new hybrid materialization model. The main feature of hybrid materialization is the ability to manipulate both positions and values at the same time. This way, query engine can flexibly combine advantages of all the existing strategies and support a new class of query plans. Moreover, hybrid materialization allows the query engine to flexibly customize the materialization policy of individual attributes. We describe our vision of how hybrid materialization can be implemented in a columnar system. As an example, we use PosDB~ -- a distributed, disk-based column-store. We present necessary data structures, the internals of a hybrid operator, and describe the algebra of such operators. Based on this implementation, we evaluate performance of late, ultra-late, and hybrid materialization strategies in several scenarios based on TPC-H queries. Our experiments demonstrate that hybrid materialization is almost two times faster than its counterparts, while providing a more flexible query model.
Generative Disco: Text-to-Video Generation for Music Visualization
Venue: arXiv cs.HC 2023
Year: 2023

Visuals can enhance our experience of music, owing to the way they can amplify the emotions and messages conveyed within it. However, creating music visualization is a complex, time-consuming, and resource-intensive process. We introduce Generative Disco, a generative AI system that helps generate music visualizations with large language models and text-to-video generation. The system helps users visualize music in intervals by finding prompts to describe the images that intervals start and end on and interpolating between them to the beat of the music. We introduce design patterns for improving these generated videos: transitions, which express shifts in color, time, subject, or style, and holds, which help focus the video on subjects. A study with professionals showed that transitions and holds were a highly expressive framework that enabled them to build coherent visual narratives. We conclude on the generalizability of these patterns and the potential of generated video for creative professionals.
Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functions
Venue: arXiv cs.RO 2023
Year: 2023

Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, collision detection using differentiable optimization has provided a way to compute the minimum uniform scaling factor that results in an intersection between two convex shapes and to also compute the Jacobian of the scaling factor. In this letter, we propose a framework that uses this scaling factor, with an offset, to systematically define a CBF for obstacle avoidance tasks. We provide theoretical analyses of the continuity and continuous differentiability of the proposed CBF. We empirically evaluate the proposed CBF's behavior and show that the resulting optimal control problem is computationally efficient, which makes it applicable for real-time robotic control. We validate our approach, first using a 2D mobile robot example, then on the Franka-Emika Research 3 (FR3) robot manipulator both in simulation and experiment.
PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds
Venue: arXiv cs.CV 2023
Year: 2023

3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to deep learning. However, deep learning algorithms are data-driven and require large amounts of annotated point cloud data for training and evaluation. Unlike 2D image labels, annotating point cloud data is difficult due to the limitations of sparsity, irregularity, and low resolution, which requires more manual work, and the annotation efficiency is much lower than 2D image.Therefore, we propose an annotation algorithm for point cloud data, which is pre-annotation and camera-LiDAR late fusion algorithm to easily and accurately annotate. The contributions of this study are as follows. We propose (1) a pre-annotation algorithm that employs 3D object detection and auto fitting for the easy annotation of point clouds, (2) a camera-LiDAR late fusion algorithm using 2D and 3D results for easily error checking, which helps annotators easily identify missing objects, and (3) a point cloud annotation evaluation pipeline to evaluate our experiments. The experimental results show that the proposed algorithm improves the annotating speed by 6.5 times and the annotation quality in terms of the 3D Intersection over Union and precision by 8.2 points and 5.6 points, respectively; additionally, the miss rate is reduced by 31.9 points.
Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering
Venue: arXiv cs.CR 2023
Year: 2023

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by $3.11\times$ and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding.
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems
Venue: arXiv cs.LG 2023
Year: 2023

Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered. However, for a given application, finding the right AI/ML model is a complex and costly process, that involves the generation, training, and evaluation of multiple interlinked steps (called pipelines), such as data pre-processing, feature engineering, selection, and model tuning. These pipelines are complex (in structure) and costly (both in compute resource and time) to execute end-to-end, with a hyper-parameter associated with each step. AutoML systems automate the search of these hyper-parameters but are slow, as they rely on optimizing the pipeline's end output. We propose the eTOP Framework which works on top of any AutoML system and decides whether or not to execute the pipeline to the end or terminate at an intermediate step. Experimental evaluation on 26 benchmark datasets and integration of eTOPwith MLBox4 reduces the training time of the AutoML system upto 40x than baseline MLBox.
Revisiting Block-Diagonal SDP Relaxations for the Clique Number of the Paley Graphs
Venue: arXiv cs.DS 2023
Year: 2023

This work addresses the block-diagonal semidefinite program (SDP) relaxations for the clique number of the Paley graphs. The size of the maximal clique (clique number) of a graph is a classic NP-complete problem; a Paley graph is a deterministic graph where two vertices are connected if their difference is a quadratic residue (square) in a finite field with the number of elements given by certain primes and prime powers. Improving the upper bound for the Paley graph clique number for prime powers that are non-squares is an open problem in combinatorics. Moreover, since quadratic residues exhibit pseudorandom properties, Paley graphs are related to the construction of deterministic restricted isometries, an open problem in compressed sensing. Recent work provides numerical evidence that the current upper bounds can be improved by the sum-of-squares (SOS) relaxations. In particular, the bounds given by the SOS relaxations of degree 4 (SOS-4) have been empirically observed to be growing at an order smaller than square root of the prime. However, computations of SOS-4 appear to be intractable with respect to large graphs. Gvozdenovic et al. introduced a more computationally efficient block-diagonal hierarchy of SDPs and computed the values of these SDPs of degrees 2 (L2) for the Paley graph clique numbers associated with primes p less or equal to 809, which bound from above the corresponding SOS-4 relaxations. We compute the values of the L2 relaxations for p's between 821 and 997. Our results provide some numerical evidence that these relaxations, and therefore also the SOS-4 relaxations, may be scaling at an order smaller than the square root of p. However, due to the size of the SDPs, we have not been able to compute L2 relaxations for p's greater than 997. Therefore, our scaling estimate is not conclusive and presents an interesting open problem for further study.
Continuous Versatile Jumping Using Learned Action Residuals
Venue: arXiv cs.RO 2023
Year: 2023

Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement learning to learn continuous jumping motions for quadrupedal robots. The core of our framework is a stance controller, which combines a manually designed acceleration controller with a learned residual policy. As the acceleration controller warm starts policy for efficient training, the trained policy overcomes the limitation of the acceleration controller and improves the jumping stability. In addition, a low-level whole-body controller converts the body pose command from the stance controller to motor commands. After training in simulation, our framework can be deployed directly to the real robot, and perform versatile, continuous jumping motions, including omni-directional jumps at up to 50cm high, 60cm forward, and jump-turning at up to 90 degrees. Please visit our website for more results: https://sites.google.com/view/learning-to-jump.
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
Venue: arXiv cs.LG 2023
Year: 2023

Given two probability densities on related data spaces, we seek a map pushing one density to the other while satisfying application-dependent constraints. For maps to have utility in a broad application space (including domain translation, domain adaptation, and generative modeling), the map must be available to apply on out-of-sample data points and should correspond to a probabilistic model over the two spaces. Unfortunately, existing approaches, which are primarily based on optimal transport, do not address these needs. In this paper, we introduce a novel pushforward map learning algorithm that utilizes normalizing flows to parameterize the map. We first re-formulate the classical optimal transport problem to be map-focused and propose a learning algorithm to select from all possible maps under the constraint that the map minimizes a probability distance and application-specific regularizers; thus, our method can be seen as solving a modified optimal transport problem. Once the map is learned, it can be used to map samples from a source domain to a target domain. In addition, because the map is parameterized as a composition of normalizing flows, it models the empirical distributions over the two data spaces and allows both sampling and likelihood evaluation for both data sets. We compare our method (parOT) to related optimal transport approaches in the context of domain adaptation and domain translation on benchmark data sets. Finally, to illustrate the impact of our work on applied problems, we apply parOT to a real scientific application: spectral calibration for high-dimensional measurements from two vastly different environments
Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications
Venue: arXiv cs.NI 2023
Year: 2023

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study the performance of non-ideal wireless PBFT and RAFT networks with THz and mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this paper, we study and compare the performance of THz and mmWave signals in non-ideal wireless PBFT and RAFT networks, considering Rayleigh Fading (RF) and close-in Free Space (FS) reference distance path loss. Performance is evaluated by five metrics: consensus success rate, latency, throughput, reliability gain, and energy consumption. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make CMs inevitably successful, and it is named the active distance of CMs. The research results analyze the performance of non-ideal wireless PBFT and RAFT networks, and provide important references for the future transmission of THz and mmWave signals in PBFT and RAFT networks.