OpenRaiser/Intern-Atlas huggingface dataset:
Offline Deep Model Predictive Control (MPC) for Visual Navigation
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we propose a new visual navigation method based on a single RGB perspective camera. Using the Visual Teach & Repeat (VT&R) methodology, the robot acquires a visual trajectory consisting of multiple subgoal images in the teaching step. In the repeat step, we propose two network architectures, namely ViewNet and VelocityNet. The combination of the two networks allows the robot to follow the visual trajectory. ViewNet is trained to generate a future image based on the current view and the velocity command. The generated future image is combined with the subgoal image for training VelocityNet. We develop an offline Model Predictive Control (MPC) policy within VelocityNet with the dual goals of (1) reducing the difference between current and subgoal images and (2) ensuring smooth trajectories by mitigating velocity discontinuities. Offline training conserves computational resources, making it a more suitable option for scenarios with limited computational capabilities, such as embedded systems. We validate our experiments in a simulation environment, demonstrating that our model can effectively minimize the metric error between real and played trajectories.
Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses
Venue: arXiv cs.CL 2024
Year: 2024

Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. Open-ended survey responses serve as a valuable source of information. This paper proposes a machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch open-ended responses in employee satisfaction surveys. Our approach aims to overcome the inherent noise and variability in these responses, enabling a comprehensive analysis of sentiments that can support employee lifecycle management. Through response clustering we identify six key aspects (salary, schedule, contact, communication, personal attention, agreements), which we validate by domain experts. We compile a dataset of 1,458 Dutch survey responses, revealing label imbalance in aspects and sentiments. We propose few-shot approaches for ABSA based on Dutch BERT models, and compare them against bag-of-words and zero-shot baselines. Our work significantly contributes to the field of ABSA by demonstrating the first successful application of Dutch pre-trained language models to aspect-based sentiment analysis in the domain of human resources (HR).
Embedding Knowledge Graphs in Degenerate Clifford Algebras
Venue: arXiv cs.AI 2024
Year: 2024

Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form $Cl_{p,q}$ (i.e., algebras without nilpotent base vectors) have been studied in the context of knowledge graph embeddings. We propose to consider nilpotent base vectors with a nilpotency index of two. In these spaces, denoted $Cl_{p,q,r}$, allows generalizing over approaches based on dual numbers (which cannot be modelled using $Cl_{p,q}$) and capturing patterns that emanate from the absence of higher-order interactions between real and complex parts of entity embeddings. We design two new models for the discovery of the parameters $p$, $q$, and $r$. The first model uses a greedy search to optimize $p$, $q$, and $r$. The second predicts $(p, q,r)$ based on an embedding of the input knowledge graph computed using neural networks. The results of our evaluation on seven benchmark datasets suggest that nilpotent vectors can help capture embeddings better. Our comparison against the state of the art suggests that our approach generalizes better than other approaches on all datasets w.r.t. the MRR it achieves on validation data. We also show that a greedy search suffices to discover values of $p$, $q$ and $r$ that are close to optimal.
Marker+Codeword+Marker: A Coding Structure for Segmented Single-Insdel/-Edit Channels
Venue: arXiv cs.IT 2024
Year: 2024

An insdel refers to a deletion or an insertion, and an edit refers to an insdel or a substitution. In this paper, we consider the segmented single-insdel (resp. single-edit) channel, where the channel's input bit stream is partitioned into segments of length $n$ and each segment can suffer from at most a single insdel (resp. edit) error. The value of $n$ is known to the receiver but the boundaries of segments are not. We propose to encode each segment following a marker+codeword+marker structure, where the two markers are carefully selected and the codewords are chosen from Varshamov-Tenegolts (VT) codes. In this way, we are able to construct a new class of binary codes that can correct segmented single-insdel errors. Our codes have the lowest redundancy of $\log_2(n-6)+7$ bits and are the first one that has linear-time encoder/decoder in the literature. Moreover, by enhancing the VT codes and one of the markers, we are able to construct the first class of binary codes that can correct segmented single-edit errors. This class of codes has redundancy $\log_2(n-9)+10$ bits and has linear-time encoder/decoder.
Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding
Venue: arXiv cs.DC 2024
Year: 2024

The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion becomes mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24x compared with alternative approaches considering homogeneous QoS requests.
Learning from the Best: Active Learning for Wireless Communications
Venue: arXiv cs.NI 2024
Year: 2024

Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert involvement and domain knowledge, may involve private intellectual properties, and is often computationally and financially expensive. Active learning is an emerging area of research in machine learning that aims to reduce the labeling overhead without accuracy degradation. Active learning algorithms identify the most critical and informative samples in an unlabeled dataset and label only those samples, instead of the complete set. In this paper, we introduce active learning for deep learning applications in wireless communications, and present its different categories. We present a case study of deep learning-based mmWave beam selection, where labeling is performed by a compute-intensive algorithm based on exhaustive search. We evaluate the performance of different active learning algorithms on a publicly available multi-modal dataset with different modalities including image and LiDAR. Our results show that using an active learning algorithm for class-imbalanced datasets can reduce labeling overhead by up to 50% for this dataset while maintaining the same accuracy as classical training.
Hashing Beam Training for Integrated Ground-Air-Space Wireless Networks
Venue: arXiv cs.IT 2024
Year: 2024

In integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. These factors motivate our investigation into the IGAS beam training problem to achieve effective sensing services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme. Specifically, we first construct an IGAS single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed IGAS HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead.
Convergence of Approximate and Packet Routing Equilibria to Nash Flows Over Time
Venue: arXiv cs.GT 2024
Year: 2024

We consider a dynamic model of traffic that has received a lot of attention in the past few years. Infinitesimally small agents aim to travel from a source to a destination as quickly as possible. Flow patterns vary over time, and congestion effects are modeled via queues, which form based on the deterministic queueing model whenever the inflow into a link exceeds its capacity. Are equilibria in this model meaningful as a prediction of traffic behavior? For this to be the case, a certain notion of stability under ongoing perturbations is needed. Real traffic consists of discrete, atomic ''packets'', rather than being a continuous flow of non-atomic agents. Users may not choose an absolutely quickest route available, if there are multiple routes with very similar travel times. We would hope that in both these situations -- a discrete packet model, with packet size going to 0, and $ε$-equilibria, with $ε$ going to 0 -- equilibria converge to dynamic equilibria in the flow over time model. No such convergence results were known. We show that such a convergence result does hold in single-commodity instances for both of these settings, in a unified way. More precisely, we introduce a notion of ''strict'' $ε$-equilibria, and show that these must converge to the exact dynamic equilibrium in the limit as $ε\to 0$. We then show that results for the two settings mentioned can be deduced from this with only moderate further technical effort.
An approach to automated videogame beta testing
Venue: arXiv cs.AI 2024
Year: 2024

Videogames developed in the 1970s and 1980s were modest programs created in a couple of months by a single person, who played the roles of designer, artist and programmer. Since then, videogames have evolved to become a multi-million dollar industry. Today, AAA game development involves hundreds of people working together over several years. Management and engineering requirements have changed at the same pace. Although many of the processes have been adapted over time, this is not quite true for quality assurance tasks, which are still done mainly manually by human beta testers due to the specific peculiarities of videogames. This paper presents an approach to automate this beta testing.
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation
Venue: arXiv cs.CV 2024
Year: 2024

Deep neural networks have useful applications in many different tasks, however their performance can be severely affected by changes in the data distribution. For example, in the biomedical field, their performance can be affected by changes in the data (different machines, populations) between training and test datasets. To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks. It is implemented by recalculating batch normalization statistics on test batches. Prior work has focused on analysis with test data that has the same label distribution as the training data. However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure. This presents a risk in applying test time adaptation methods in deployment. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. Our selection scheme is based on two principles that we empirically motivate: (1) later layers of networks are more sensitive to label shift (2) individual features can be sensitive to specific classes. We apply the proposed technique to three classification tasks, including CIFAR10-C, Imagenet-C, and diagnosis of fatty liver, where we explore both covariate and label distribution shifts. We find that our method allows to bring the benefits of TTA while significantly reducing the risk of failure common in other methods, while being robust to choice in hyperparameters.
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
Venue: arXiv cs.CV 2024
Year: 2024

Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated/separate fine-tuned network for each target domain, that retain all the pre-trained model parameters, is prohibitively expensive. To address this limitation, we propose Convolutional Low-Rank Adaptation (ConvLoRA). ConvLoRA freezes pre-trained model weights, adds trainable low-rank decomposition matrices to convolutional layers, and backpropagates the gradient through these matrices thus greatly reducing the number of trainable parameters. To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA. Our method has fewer trainable parameters and performs better or on-par with large independent fine-tuned networks (with less than 0.9% trainable parameters of the total base model) when tested on the segmentation of Calgary-Campinas dataset containing brain MRI images. Our approach is simple, yet effective and can be applied to any deep learning-based architecture which uses convolutional and batch normalization layers. Code is available at: https://github.com/aleemsidra/ConvLoRA.
ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12
Venue: arXiv cs.HC 2024
Year: 2024

As Computational Thinking (CT) continues to permeate younger age groups in K-12 education, established CT platforms such as Scratch face challenges in catering to these younger learners, particularly those in the elementary school (ages 6-12). Through formative investigation with Scratch experts, we uncover three key obstacles to children's autonomous Scratch learning: artist's block in project planning, bounded creativity in asset creation, and inadequate coding guidance during implementation. To address these barriers, we introduce ChatScratch, an AI-augmented system to facilitate autonomous programming learning for young children. ChatScratch employs structured interactive storyboards and visual cues to overcome artist's block, integrates digital drawing and advanced image generation technologies to elevate creativity, and leverages Scratch-specialized Large Language Models (LLMs) for professional coding guidance. Our study shows that, compared to Scratch, ChatScratch efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children.
A Longitudinal Study of Italian and French Reddit Conversations Around the Russian Invasion of Ukraine
Venue: arXiv cs.SI 2024
Year: 2024

Global events like wars and pandemics can intensify online discussions, fostering information sharing and connection among individuals. However, the divisive nature of such events may lead to polarization within online communities, shaping the dynamics of online interactions. Our study delves into the conversations within the largest Italian and French Reddit communities, specifically examining how the Russian invasion of Ukraine affected online interactions. We use a dataset with over 3 million posts (i.e., comments and submissions) to (1) describe the patterns of moderation activity and (2) characterize war-related discussions in the subreddits. We found changes in moderators' behavior, who became more active during the first month of the war. Moreover, we identified a connection between the daily sentiment of comments and the prevalence of war-related discussions. These discussions were not only more negative and toxic compared to non-war-related ones but also did not involve a specific demographic group. Our research reveals that there is no tendency for users with similar characteristics to interact more. Overall, our study reveals how the war in Ukraine had a negative influence on daily conversations in the analyzed communities. This sheds light on how users responded to this significant event, providing insights into the dynamics of online discussions during events of global relevance.
Example-based Explanations for Random Forests using Machine Unlearning
Venue: arXiv cs.LG 2024
Year: 2024

Tree-based machine learning models, such as decision trees and random forests, have been hugely successful in classification tasks primarily because of their predictive power in supervised learning tasks and ease of interpretation. Despite their popularity and power, these models have been found to produce unexpected or discriminatory outcomes. Given their overwhelming success for most tasks, it is of interest to identify sources of their unexpected and discriminatory behavior. However, there has not been much work on understanding and debugging tree-based classifiers in the context of fairness. We introduce FairDebugger, a system that utilizes recent advances in machine unlearning research to identify training data subsets responsible for instances of fairness violations in the outcomes of a random forest classifier. FairDebugger generates top-$k$ explanations (in the form of coherent training data subsets) for model unfairness. Toward this goal, FairDebugger first utilizes machine unlearning to estimate the change in the tree structures of the random forest when parts of the underlying training data are removed, and then leverages the Apriori algorithm from frequent itemset mining to reduce the subset search space. We empirically evaluate our approach on three real-world datasets, and demonstrate that the explanations generated by FairDebugger are consistent with insights from prior studies on these datasets.
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss
Venue: arXiv cs.CV 2024
Year: 2024

We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM's lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT. Subsequently, we conduct end-to-end training on the SA-1B dataset. Benefiting from EfficientViT's efficiency and capacity, EfficientViT-SAM delivers 48.9x measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance. Our code and pre-trained models are released at https://github.com/mit-han-lab/efficientvit.
Information Theoretically Secure Encryption Key Generation over Wireless Networks by Exploiting Packet Errors
Venue: arXiv cs.IT 2024
Year: 2024

This article presents a novel method for establishing an information theoretically secure encryption key over wireless channels. It exploits the fact that data transmission over wireless links is accompanied by packet error, while noise terms, and thereby the error events observed by two separate receivers are independent of each other. A number of data packets, with random data, are transmitted from a first legitimate node, say Alice, to a second legitimate node, say Bob. Bob identifies all packets that are received error-free in the first transmission attempt and sends their indices to Alice over a public channel. Then, both Alice and Bob mix the contents of identified packets, e.g., using a hash function, and thereby derive an identical encryption key. Since error events from Alice to Bob is independent of error events from Alice to Eve, the chances that Eve has successfully received all packets used in key generation error-free diminishes as the number of packet increases. In many wireless standards, the first stage in error detection and Automatic Repeat Request (ARQ) is deployed at the PHY/MAC (Physical Layer/Medium Access Control) layer. In such setups, the first re-transmission is manged by the PHY/MAC layer without informing higher layers. This makes it impossible to directly access the information related to packet errors through high-level programming interfaces available to an end-user. A method is presented for determining packets received error-free in first transmission attempts through high-level programming. Examples are presented in conjunction with an LTE cellular network.
Strong convexity-guided hyper-parameter optimization for flatter losses
Venue: arXiv cs.LG 2024
Year: 2024

We propose a novel white-box approach to hyper-parameter optimization. Motivated by recent work establishing a relationship between flat minima and generalization, we first establish a relationship between the strong convexity of the loss and its flatness. Based on this, we seek to find hyper-parameter configurations that improve flatness by minimizing the strong convexity of the loss. By using the structure of the underlying neural network, we derive closed-form equations to approximate the strong convexity parameter, and attempt to find hyper-parameters that minimize it in a randomized fashion. Through experiments on 14 classification datasets, we show that our method achieves strong performance at a fraction of the runtime.
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training
Venue: arXiv cs.LG 2024
Year: 2024

Recent breakthroughs and successful deployment of large language and vision models in a constrained environment predominantly follow a two phase approach. First, large models are trained to achieve peak performance, followed by a model shrinking method to meet hardware constraints; Methods like distillation, compression or quantization help leverage the highly performant large models to induce smaller performant ones. Formally, this can be seen as the problem of identifying an optimal model of size $n$ from a larger model of size $k \cdot n$, where $k > 1$ is the overparameterization factor. This paper explores the hypothesis that a single training run can simultaneously train a larger model for performance and derive a smaller model for deployment. Our contribution is an effective architectural change, namely, {\it Majority Kernels} that is compatible with the main standard architectures such as multi-layer perceptrons (MLPs), Residual networks (ResNets), and Transformers. We demonstrate that applying our technique can modify the training dynamics resulting in performance gains across architectures and tasks while maintaining the inference performance consistent. Furthermore, our approach adds minimal overhead to the cost incurred (wall clock time) at training time. The proposed approach shows strong performance on a wide variety of datasets and models, even outperforming strong baselines such as distilled ensembles as well as combinatorial optimization methods based on submodular optimization.