OpenRaiser/Intern-Atlas huggingface dataset:
CIKMar: A Dual-Encoder Approach to Prompt-Based Reranking in Educational Dialogue Systems
Venue: arXiv cs.CL 2024
Year: 2024

In this study, we introduce CIKMar, an efficient approach to educational dialogue systems powered by the Gemma Language model. By leveraging a Dual-Encoder ranking system that incorporates both BERT and SBERT model, we have designed CIKMar to deliver highly relevant and accurate responses, even with the constraints of a smaller language model size. Our evaluation reveals that CIKMar achieves a robust recall and F1-score of 0.70 using BERTScore metrics. However, we have identified a significant challenge: the Dual-Encoder tends to prioritize theoretical responses over practical ones. These findings underscore the potential of compact and efficient models like Gemma in democratizing access to advanced educational AI systems, ensuring effective and contextually appropriate responses.
System Identification For Constrained Robots
Venue: arXiv cs.RO 2024
Year: 2024

Identifying the parameters of robotic systems, such as motor inertia or joint friction, is critical to satisfactory controller synthesis, model analysis, and observer design. Conventional identification techniques are designed primarily for unconstrained systems, such as robotic manipulators. In contrast, the growing importance of legged robots that feature closed kinematic chains or other constraints, poses challenges to these traditional methods. This paper introduces a system identification approach for constrained systems that relies on iterative least squares to identify motor inertia and joint friction parameters from data. The proposed approach is validated in simulation and in the real-world on Digit, which is a 20 degree-of-freedom humanoid robot built by Agility Robotics. In these experiments, the parameters identified by the proposed method enable a model-based controller to achieve better tracking performance than when it uses the default parameters provided by the manufacturer. The implementation of the approach is available at https://github.com/roahmlab/ConstrainedSysID.
Hotspots and Trends in Magnetoencephalography Research (2013-2022): A Bibliometric Analysis
Venue: arXiv cs.DL 2024
Year: 2024

This study aimed to utilize bibliometric methods to analyze trends in international Magnetoencephalography (MEG) research from 2013 to 2022. Due to the limited volume of domestic literature on MEG, this analysis focuses solely on the global research landscape, providing insights from the past decade as a representative sample. This study utilized bibliometric methods to explore and analyze the progress, hotspots and developmental trends in international MEG research spanning from 1995 to 2022. The results indicated a dynamic and steady growth trend in the overall number of publications in MEG. Ryusuke Kakigi emerged as the most prolific author, while Neuroimage led as the most prolific journal. Current hotspots in MEG research encompass resting state, networks, functional connectivity, phase dynamics, oscillation, and more. Future trends in MEG research are poised to advance across three key aspects: disease treatment and practical applications, experimental foundations and technical advancements, and fundamental and advanced human cognition. In the future, there should be a focus on enhancing cross-integration and utilization of MEG with other instruments to diversify research methodologies in this field
A 1024 RV-Cores Shared-L1 Cluster with High Bandwidth Memory Link for Low-Latency 6G-SDR
Venue: arXiv cs.DC 2024
Year: 2024

We introduce an open-source architecture for next-generation Radio-Access Network baseband processing: 1024 latency-tolerant 32-bit RISC-V cores share 4 MiB of L1 memory via an ultra-low latency interconnect (7-11 cycles), a modular Direct Memory Access engine provides an efficient link to a high bandwidth memory, such as HBM2E (98% peak bandwidth at 910GBps). The system achieves leading-edge energy efficiency at sub-ms latency in key 6G baseband processing kernels: Fast Fourier Transform (93 GOPS/W), Beamforming (125 GOPS/W), Channel Estimation (96 GOPS/W), and Linear System Inversion (61 GOPS/W), with only 9% data movement overhead.
RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search
Venue: arXiv cs.IR 2024
Year: 2024

Approximate Nearest Neighbor Search (ANNS) is a fundamental and critical component in many applications, including recommendation systems and large language model-based applications. With the advancement of multimodal neural models, which transform data from different modalities into a shared high-dimensional space as feature vectors, cross-modal ANNS aims to use the data vector from one modality (e.g., texts) as the query to retrieve the most similar items from another (e.g., images or videos). However, there is an inherent distribution gap between embeddings from different modalities, and cross-modal queries become Out-of-Distribution (OOD) to the base data. Consequently, state-of-the-art ANNS approaches suffer poor performance for OOD workloads. In this paper, we quantitatively analyze the properties of the OOD workloads to gain an understanding of their ANNS efficiency. Unlike single-modal workloads, we reveal OOD queries spatially deviate from base data, and the k-nearest neighbors of an OOD query are distant from each other in the embedding space. The property breaks the assumptions of existing ANNS approaches and mismatches their design for efficient search. With insights from the OOD workloads, we propose pRojected bipartite Graph (RoarGraph), an efficient ANNS graph index built under the guidance of query distribution. Extensive experiments show that RoarGraph significantly outperforms state-of-the-art approaches on modern cross-modal datasets, achieving up to 3.56x faster search speed at a 90% recall rate for OOD queries.
Information-Theoretic Progress Measures reveal Grokking is an Emergent Phase Transition
Venue: arXiv cs.LG 2024
Year: 2024

This paper studies emergent phenomena in neural networks by focusing on grokking where models suddenly generalize after delayed memorization. To understand this phase transition, we utilize higher-order mutual information to analyze the collective behavior (synergy) and shared properties (redundancy) between neurons during training. We identify distinct phases before grokking allowing us to anticipate when it occurs. We attribute grokking to an emergent phase transition caused by the synergistic interactions between neurons as a whole. We show that weight decay and weight initialization can enhance the emergent phase.
Differentiable Edge-based OPC
Venue: arXiv cs.AI 2024
Year: 2024

Optical proximity correction (OPC) is crucial for pushing the boundaries of semiconductor manufacturing and enabling the continued scaling of integrated circuits. While pixel-based OPC, termed as inverse lithography technology (ILT), has gained research interest due to its flexibility and precision. Its complexity and intricate features can lead to challenges in mask writing, increased defects, and higher costs, hence hindering widespread industrial adoption. In this paper, we propose DiffOPC, a differentiable OPC framework that enjoys the virtue of both edge-based OPC and ILT. By employing a mask rule-aware gradient-based optimization approach, DiffOPC efficiently guides mask edge segment movement during mask optimization, minimizing wafer error by propagating true gradients from the cost function back to the mask edges. Our approach achieves lower edge placement error while reducing manufacturing cost by half compared to state-of-the-art OPC techniques, bridging the gap between the high accuracy of pixel-based OPC and the practicality required for industrial adoption, thus offering a promising solution for advanced semiconductor manufacturing.
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques
Venue: arXiv cs.CV 2024
Year: 2024

Federated Learning (FL) has garnered significant attention in manufacturing for its robust model development and privacy-preserving capabilities. This paper contributes to research focused on the robustness of FL models in object detection, hereby presenting a comparative study with conventional techniques using a hybrid dataset for small object detection. Our findings demonstrate the superior performance of FL over centralized training models and different deep learning techniques when tested on test data recorded in a different environment with a variety of object viewpoints, lighting conditions, cluttered backgrounds, etc. These results highlight the potential of FL in achieving robust global models that perform efficiently even in unseen environments. The study provides valuable insights for deploying resilient object detection models in manufacturing environments.
Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
Venue: arXiv cs.AI 2024
Year: 2024

While image-to-text models have demonstrated significant advancements in various vision-language tasks, they remain susceptible to adversarial attacks. Existing white-box attacks on image-to-text models require access to the architecture, gradients, and parameters of the target model, resulting in low practicality. Although the recently proposed gray-box attacks have improved practicality, they suffer from semantic loss during the training process, which limits their targeted attack performance. To advance adversarial attacks of image-to-text models, this paper focuses on a challenging scenario: decision-based black-box targeted attacks where the attackers only have access to the final output text and aim to perform targeted attacks. Specifically, we formulate the decision-based black-box targeted attack as a large-scale optimization problem. To efficiently solve the optimization problem, a three-stage process \textit{Ask, Attend, Attack}, called \textit{AAA}, is proposed to coordinate with the solver. \textit{Ask} guides attackers to create target texts that satisfy the specific semantics. \textit{Attend} identifies the crucial regions of the image for attacking, thus reducing the search space for the subsequent \textit{Attack}. \textit{Attack} uses an evolutionary algorithm to attack the crucial regions, where the attacks are semantically related to the target texts of \textit{Ask}, thus achieving targeted attacks without semantic loss. Experimental results on transformer-based and CNN+RNN-based image-to-text models confirmed the effectiveness of our proposed \textit{AAA}.
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
Venue: arXiv cs.LG 2024
Year: 2024

Learning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model is perhaps the most standard and simplest Model-based Reinforcement Learning (RL) framework. In this work, we show that such a simple Model-based RL scheme, when equipped with optimistic and pessimistic planning procedures, achieves strong regret and sample complexity bounds in online and offline RL settings. Particularly, we demonstrate that under the conditions where the trajectory-wise reward is normalized between zero and one and the transition is time-homogenous, it achieves nearly horizon-free and second-order bounds. Nearly horizon-free means that our bounds have no polynomial dependence on the horizon of the Markov Decision Process. A second-order bound is a type of instance-dependent bound that scales with respect to the variances of the returns of the policies which can be small when the system is nearly deterministic and (or) the optimal policy has small values. We highlight that our algorithms are simple, fairly standard, and indeed have been extensively studied in the RL literature: they learn a model via MLE, build a version space around the MLE solution, and perform optimistic or pessimistic planning depending on whether operating in the online or offline mode. These algorithms do not rely on additional specialized algorithmic designs such as learning variances and performing variance-weighted learning and thus can easily leverage non-linear function approximations. The simplicity of the algorithms also implies that our horizon-free and second-order regret analysis is actually standard and mainly follows the general framework of optimism/pessimism in the face of uncertainty.
Classifier-Free Guidance is a Predictor-Corrector
Venue: arXiv cs.LG 2024
Year: 2024

We investigate the theoretical foundations of classifier-free guidance (CFG). CFG is the dominant method of conditional sampling for text-to-image diffusion models, yet unlike other aspects of diffusion, it remains on shaky theoretical footing. In this paper, we disprove common misconceptions, by showing that CFG interacts differently with DDPM (Ho et al., 2020) and DDIM (Song et al., 2021), and neither sampler with CFG generates the gamma-powered distribution $p(x|c)^γp(x)^{1-γ}$. Then, we clarify the behavior of CFG by showing that it is a kind of predictor-corrector method (Song et al., 2020) that alternates between denoising and sharpening, which we call predictor-corrector guidance (PCG). We prove that in the SDE limit, CFG is actually equivalent to combining a DDIM predictor for the conditional distribution together with a Langevin dynamics corrector for a gamma-powered distribution (with a carefully chosen gamma). Our work thus provides a lens to theoretically understand CFG by embedding it in a broader design space of principled sampling methods.
An optimal pairwise merge algorithm improves the quality and consistency of nonnegative matrix factorization
Venue: arXiv cs.LG 2024
Year: 2024

Non-negative matrix factorization (NMF) is a key technique for feature extraction and widely used in source separation. However, existing algorithms may converge to poor local minima, or to one of several minima with similar objective value but differing feature parametrizations. Here we show that some of these weaknesses may be mitigated by performing NMF in a higher-dimensional feature space and then iteratively combining components with an analytically-solvable pairwise merge strategy. Experimental results demonstrate our method helps non-ideal NMF solutions escape to better local optima and achieve greater consistency of the solutions. Despite these extra steps, our approach exhibits similar computational performance to established methods by reducing the occurrence of "plateau phenomenon" near saddle points. Moreover, the results also illustrate that our method is compatible with different NMF algorithms. Thus, this can be recommended as a preferred approach for most applications of NMF.
Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models
Venue: arXiv cs.CV 2024
Year: 2024

Most evaluations of vision models use indirect methods to assess latent space quality. These methods often involve adding extra layers to project the latent space into a new one. This projection makes it difficult to analyze and compare the original latent space. This article uses the k* Distribution, a local neighborhood analysis method, to examine the learned latent space at the level of individual concepts, which can be extended to examine the entire latent space. We introduce skewness-based true and approximate metrics for interpreting individual concepts to assess the overall quality of vision models' latent space. Our findings indicate that current vision models frequently fracture the distributions of individual concepts within the latent space. Nevertheless, as these models improve in generalization across multiple datasets, the degree of fracturing diminishes. A similar trend is observed in robust vision models, where increased robustness correlates with reduced fracturing. Ultimately, this approach enables a direct interpretation and comparison of the latent spaces of different vision models and reveals a relationship between a model's generalizability and robustness. Results show that as a model becomes more general and robust, it tends to learn features that result in better clustering of concepts. Project Website is available online at https://shashankkotyan.github.io/k-Distribution/
Improving Rare Word Translation With Dictionaries and Attention Masking
Venue: arXiv cs.CL 2024
Year: 2024

In machine translation, rare words continue to be a problem for the dominant encoder-decoder architecture, especially in low-resource and out-of-domain translation settings. Human translators solve this problem with monolingual or bilingual dictionaries. In this paper, we propose appending definitions from a bilingual dictionary to source sentences and using attention masking to link together rare words with their definitions. We find that including definitions for rare words improves performance by up to 1.0 BLEU and 1.6 MacroF1.
Improved Q-learning based Multi-hop Routing for UAV-Assisted Communication
Venue: arXiv cs.NI 2024
Year: 2024

Designing effective Unmanned Aerial Vehicle(UAV)-assisted routing protocols is challenging due to changing topology, limited battery capacity, and the dynamic nature of communication environments. Current protocols prioritize optimizing individual network parameters, overlooking the necessity for a nuanced approach in scenarios with intermittent connectivity, fluctuating signal strength, and varying network densities, ultimately failing to address aerial network requirements comprehensively. This paper proposes a novel, Improved Q-learning-based Multi-hop Routing (IQMR) algorithm for optimal UAV-assisted communication systems. Using Q(λ) learning for routing decisions, IQMR substantially enhances energy efficiency and network data throughput. IQMR improves system resilience by prioritizing reliable connectivity and inter-UAV collision avoidance while integrating real-time network status information, all in the absence of predefined UAV path planning, thus ensuring dynamic adaptability to evolving network conditions. The results validate IQMR's adaptability to changing system conditions and superiority over the current techniques. IQMR showcases 36.35\% and 32.05\% improvements in energy efficiency and data throughput over the existing methods.
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
Venue: arXiv cs.LG 2024
Year: 2024

Persistent homology, a fundamental technique within Topological Data Analysis (TDA), captures structural and shape characteristics of graphs, yet encounters computational difficulties when applied to dynamic directed graphs. This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs. Our approach creatively uses line graph transformations to produce both source and sink line graphs, highlighting the shared neighbor structures that Dowker complexes focus on. The DNDN incorporates a Source-Sink Line Graph Neural Network (SSLGNN) layer to effectively capture the neighborhood relationships among dynamic edges. Additionally, we introduce an innovative duality edge fusion mechanism, ensuring that the results for both the sink and source line graphs adhere to the duality principle intrinsic to Dowker complexes. Our approach is validated through comprehensive experiments on real-world datasets, demonstrating DNDN's capability not only to effectively approximate dynamic Dowker filtration results but also to perform exceptionally in dynamic graph classification tasks.
Thin-Plate Spline-based Interpolation for Animation Line Inbetweening
Venue: arXiv cs.CV 2024
Year: 2024

Animation line inbetweening is a crucial step in animation production aimed at enhancing animation fluidity by predicting intermediate line arts between two key frames. However, existing methods face challenges in effectively addressing sparse pixels and significant motion in line art key frames. In literature, Chamfer Distance (CD) is commonly adopted for evaluating inbetweening performance. Despite achieving favorable CD values, existing methods often generate interpolated frames with line disconnections, especially for scenarios involving large motion. Motivated by this observation, we propose a simple yet effective interpolation method for animation line inbetweening that adopts thin-plate spline-based transformation to estimate coarse motion more accurately by modeling the keypoint correspondence between two key frames, particularly for large motion scenarios. Building upon the coarse estimation, a motion refine module is employed to further enhance motion details before final frame interpolation using a simple UNet model. Furthermore, to more accurately assess the performance of animation line inbetweening, we refine the CD metric and introduce a novel metric termed Weighted Chamfer Distance, which demonstrates a higher consistency with visual perception quality. Additionally, we incorporate Earth Mover's Distance and conduct user study to provide a more comprehensive evaluation. Our method outperforms existing approaches by delivering high-quality interpolation results with enhanced fluidity. The code is available at \url{https://github.com/Tian-one/tps-inbetween}.
Automatic Metrics in Natural Language Generation: A Survey of Current Evaluation Practices
Venue: arXiv cs.CL 2024
Year: 2024

Automatic metrics are extensively used to evaluate natural language processing systems. However, there has been increasing focus on how they are used and reported by practitioners within the field. In this paper, we have conducted a survey on the use of automatic metrics, focusing particularly on natural language generation (NLG) tasks. We inspect which metrics are used as well as why they are chosen and how their use is reported. Our findings from this survey reveal significant shortcomings, including inappropriate metric usage, lack of implementation details and missing correlations with human judgements. We conclude with recommendations that we believe authors should follow to enable more rigour within the field.
On the Reliability of Radio Frequency Fingerprinting
Venue: arXiv cs.CR 2024
Year: 2024

Radio Frequency Fingerprinting (RFF) offers a unique method for identifying devices at the physical (PHY) layer based on their RF emissions due to intrinsic hardware differences. Nevertheless, RFF techniques depend on the ability to extract information from the PHY layer of the radio spectrum by resorting to Software Defined Radios (SDR). Previous works have highlighted the so-called ``Day-After-Tomorrow'' effect, i.e., an intrinsic issue of SDRs leading to a fingerprint mutation following a radio power cycle. In this work, we extend such a study by demonstrating that fingerprint mutations appear every time a new FPGA image is reloaded, i.e., when the SDR initiates a new communication. In this context, we provide an in-depth analysis of the reliability of RFF over multiple FPGA image reloading operations, highlighting its ephemeral and mutational nature. We introduce a methodology for abstracting fingerprint mutations into a graph and provide a theoretical framework for assessing fingerprint reliability. Our results show that the common assumption of considering the RF fingerprint as unique and always persistent is incorrect. By combining real-world measurements, high-performance SDRs, and state-of-the-art deep learning techniques, we experimentally demonstrate that radio devices feature multiple fingerprints that can be clustered according to shared features. Moreover, we show that the RF fingerprint is a time-independent probabilistic phenomenon, which requires the collection of multiple samples to achieve the necessary reliability.