OpenRaiser/Intern-Atlas huggingface dataset:
Sequential Amodal Segmentation via Cumulative Occlusion Learning
Venue: arXiv cs.CV 2024
Year: 2024

To fully understand the 3D context of a single image, a visual system must be able to segment both the visible and occluded regions of objects, while discerning their occlusion order. Ideally, the system should be able to handle any object and not be restricted to segmenting a limited set of object classes, especially in robotic applications. Addressing this need, we introduce a diffusion model with cumulative occlusion learning designed for sequential amodal segmentation of objects with uncertain categories. This model iteratively refines the prediction using the cumulative mask strategy during diffusion, effectively capturing the uncertainty of invisible regions and adeptly reproducing the complex distribution of shapes and occlusion orders of occluded objects. It is akin to the human capability for amodal perception, i.e., to decipher the spatial ordering among objects and accurately predict complete contours for occluded objects in densely layered visual scenes. Experimental results across three amodal datasets show that our method outperforms established baselines.
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
Venue: arXiv cs.DC 2024
Year: 2024

As an emerging artificial intelligence technology, graph neural networks (GNNs) have exhibited promising performance across a wide range of graph-related applications. However, information exchanges among neighbor nodes in GNN pose new challenges in the resource-constrained scenario, especially in wireless systems. In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation of GNNs. To improve the learning performance of GNNs, we aim to maximize the number of long-term average (LTA) communication links by the optimized power control under energy consumption constraints. Using the Lyapunov optimization method, we first transform the intractable long-term problem into a deterministic problem in each time slot by converting the long-term energy constraints into the objective function. In spite of this non-convex combinatorial optimization problem, we address this problem via equivalently solving a sequence of convex feasibility problems together with a greedy based solver. Simulation results demonstrate the superiority of our proposed scheme over the baselines.
Revitalising Stagecraft: NLP-Driven Sentiment Analysis for Traditional Theater Revival
Venue: arXiv cs.SE 2024
Year: 2024

This paper explores the application of FilmFrenzy, a python based ticket booking web application, in the revival of traditional Indian theatres. Additionally, this research paper explores how NLP can be implemented to improve user experience. Through clarifying audience views and pinpointing opportunities for development, FilmFrenzy aims to promote involvement and rejuvenation in India's conventional theatre scene. The platform seeks to maintain the relevance and vitality of conventional theatres by bridging the gap between audiences and them through the incorporation of contemporary technologies, especially NLP. This research envisions a future in which technology plays a crucial part in maintaining India's rich theatrical traditions, thereby contributing to the preservation and development of cultural heritage. With sentiment analysis and natural language processing (NLP) as essential instruments for improving stagecraft, the research envisions a period when traditional theatre will still be vibrant.
On the Secrecy Capacity of 1-2-1 Atomic Networks
Venue: arXiv cs.IT 2024
Year: 2024

We consider the problem of secure communication over a noiseless 1-2-1 network, an abstract model introduced to capture the directivity characteristic of mmWave communications. We focus on structured networks, which we refer to as 1-2-1 atomic networks. Broadly speaking, these are characterized by a source, a destination, and three layers of intermediate nodes with sparse connections. The goal is for the source to securely communicate to the destination in the presence of an eavesdropper with unbounded computation capabilities, but limited network presence. We derive novel upper and lower bounds on the secrecy capacity of 1-2-1 atomic networks. These bounds are shown to be tighter than existing bounds in some regimes. Moreover, in such regimes, the bounds match and hence, they characterize the secrecy capacity of 1-2-1 atomic networks.
Non-Binary Covering Codes for Low-Access Computations
Venue: arXiv cs.IT 2024
Year: 2024

Given a real dataset and a computation family, we wish to encode and store the dataset in a distributed system so that any computation from the family can be performed by accessing a small number of nodes. In this work, we focus on the families of linear computations where the coefficients are restricted to a finite set of real values. For two-valued computations, a recent work presented a scheme that gives good feasible points on the access-redundancy tradeoff. This scheme is based on binary covering codes having a certain closure property. In a follow-up work, this scheme was extended to all finite coefficient sets, using a new additive-combinatorics notion called coefficient complexity. In the present paper, we explore non-binary covering codes and develop schemes that outperform the state-of-the-art for some coefficient sets. We provide a more general coefficient complexity definition and show its applicability to the access-redundancy tradeoff.
Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera
Venue: arXiv cs.CV 2024
Year: 2024

We propose an approach for reconstructing free-moving object from a monocular RGB video. Most existing methods either assume scene prior, hand pose prior, object category pose prior, or rely on local optimization with multiple sequence segments. We propose a method that allows free interaction with the object in front of a moving camera without relying on any prior, and optimizes the sequence globally without any segments. We progressively optimize the object shape and pose simultaneously based on an implicit neural representation. A key aspect of our method is a virtual camera system that reduces the search space of the optimization significantly. We evaluate our method on the standard HO3D dataset and a collection of egocentric RGB sequences captured with a head-mounted device. We demonstrate that our approach outperforms most methods significantly, and is on par with recent techniques that assume prior information.
FlockGPT: Guiding UAV Flocking with Linguistic Orchestration
Venue: arXiv cs.RO 2024
Year: 2024

This article presents the world's first rapid drone flocking control using natural language through generative AI. The described approach enables the intuitive orchestration of a flock of any size to achieve the desired geometry. The key feature of the method is the development of a new interface based on Large Language Models to communicate with the user and to generate the target geometry descriptions. Users can interactively modify or provide comments during the construction of the flock geometry model. By combining flocking technology and defining the target surface using a signed distance function, smooth and adaptive movement of the drone swarm between target states is achieved. Our user study on FlockGPT confirmed a high level of intuitive control over drone flocking by users. Subjects who had never previously controlled a swarm of drones were able to construct complex figures in just a few iterations and were able to accurately distinguish the formed swarm drone figures. The results revealed a high recognition rate for six different geometric patterns generated through the LLM-based interface and performed by a simulated drone flock (mean of 80% with a maximum of 93\% for cube and tetrahedron patterns). Users commented on low temporal demand (19.2 score in NASA-TLX), high performance (26 score in NASA-TLX), attractiveness (1.94 UEQ score), and hedonic quality (1.81 UEQ score) of the developed system. The FlockGPT demo code repository can be found at: coming soon
Exploring Proactive Interventions toward Harmful Behavior in Embodied Virtual Spaces
Venue: arXiv cs.HC 2024
Year: 2024

Technological advancements have undoubtedly revolutionized various aspects of human life, altering the ways we perceive the world, engage with others, build relationships, and conduct our daily work routines. Among the recent advancements, the proliferation of virtual and mixed reality technologies stands out as a significant leap forward, promising to elevate our experiences and interactions to unprecedented levels. However, alongside the benefits, these emerging technologies also introduce novel avenues for harm and misuse, particularly in virtual and embodied spaces such as Zoom and virtual reality (VR) environments. The immersive nature of virtual reality environments raises unique challenges regarding psychological and emotional well-being. While VR can offer captivating and immersive experiences, prolonged exposure to virtual environments may lead to phenomena like cybersickness, disorientation, and even psychological distress in susceptible individuals. Additionally, the blurring of boundaries between virtual and real-world interactions in VR raises ethical concerns regarding consent, harassment, and the potential for virtual experiences to influence real-life behavior. Additionally, the increasing integration of artificial intelligence (AI) and machine learning algorithms in virtual spaces introduces risks related to algorithmic bias, discrimination, and manipulation. In VR environments, AI-driven systems may inadvertently perpetuate stereotypes, amplify inequalities, or manipulate user behavior through personalized content recommendations and targeted advertising, posing ethical dilemmas and societal risks.
Understanding and Mitigating Harmful Design in User-Generated Virtual Worlds
Venue: arXiv cs.HC 2024
Year: 2024

Virtual space offers innovative ways for individuals to engage with one another in a digital setting. Prominent virtual social platforms, such as Facebook Spaces, VR Chat, and AltspaceVR, facilitate social connections, allowing users to interact seamlessly. Additionally, certain video games, like Second Life and World of Warcraft, are set within these virtual spaces as well, providing immersive player experiences. As the popularity of virtual space grows, various companies have begun to democratize the process of creating these spaces, shifting the development from skilled professionals to hobbyist creators. Platforms like Minecraft, Roblox, and RecRoom enable users to create and publish their own virtual environments, hosting a wide range of interactions and narratives. This shift echoes the rise of user-generated content, where content creators create and publish content on platforms, such as social media platforms [6]. For example, YouTubers upload videos on YouTube and Reddit users post text-based content on Reddit. For a long time, user-generated content has predominantly contained text, videos, and images. However, with the emergence of virtual spaces, some platforms now allow creators to create and publish their own virtual spaces, leading to the emergence of user-generated virtual worlds.
New Harms Moderated by Immersive Experiences and Breaks in Them
Venue: arXiv cs.HC 2024
Year: 2024

This proposal highlights the potential real-world consequences of harmful experiences in immersive and embodied spaces due to presence, which moderates experiences, intensifying positive or negative content. While positive experiences enhance social interactions, negative content can lead to harm. Also, presence is not continuous and may break. Understanding the threshold between harmful experiences and breaks in presence is essential. This proposal underscores the need to explore whether the level of immersion or the nature of harm defines the boundary, emphasizing key discussion points for the workshop.
Understanding Emotional Hijacking in Metaverse
Venue: arXiv cs.HC 2024
Year: 2024

Emotions are an integral part of being human, and experiencing a range of emotions is what makes life rich and vibrant. From basic emotions like anger, fear, happiness, and sadness to more complex ones like excitement and grief, emotions help us express ourselves and connect with the world around us. In recent years, researchers have begun adopting virtual reality (VR) technology to evoke emotions as realistically as possible and quantify the strength of emotions from the electroencephalogram (EEG) signals measured from the brain to understand human emotions in realistic situations better. This is achieved by creating a sense of presence in the virtual environment, the feeling that the user is there. For instance, [6] studied the excitement of a rollercoaster ride in VR, and [5] studied the fear of navigating in a VR cave.
Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
Venue: arXiv cs.LG 2024
Year: 2024

Ensuring fair predictions across many distinct subpopulations in the training data can be prohibitive for large models. Recently, simple linear last layer retraining strategies, in combination with data augmentation methods such as upweighting, downsampling and mixup, have been shown to achieve state-of-the-art performance for worst-group accuracy, which quantifies accuracy for the least prevalent subpopulation. For linear last layer retraining and the abovementioned augmentations, we present the optimal worst-group accuracy when modeling the distribution of the latent representations (input to the last layer) as Gaussian for each subpopulation. We evaluate and verify our results for both synthetic and large publicly available datasets.
Probing Multimodal LLMs as World Models for Driving
Venue: arXiv cs.RO 2024
Year: 2024

We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynamic driving scenarios. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored. Our experimental study assesses various MLLMs as world models using in-car camera perspectives and reveals that while these models excel at interpreting individual images, they struggle to synthesize coherent narratives across frames, leading to considerable inaccuracies in understanding (i) ego vehicle dynamics, (ii) interactions with other road actors, (iii) trajectory planning, and (iv) open-set scene reasoning. We introduce the Eval-LLM-Drive dataset and DriveSim simulator to enhance our evaluation, highlighting gaps in current MLLM capabilities and the need for improved models in dynamic real-world environments.
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
Venue: arXiv cs.LG 2024
Year: 2024

This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) -- also known as Scientific Machine Learning -- with particular focus to those PEML strategies developed to tackle dynamical systems' challenges. The need to go beyond Machine Learning (ML) strategies is driven by: (i) limited volume of informative data, (ii) avoiding accurate-but-wrong predictions; (iii) dealing with uncertainties; (iv) providing Explainable and Interpretable inferences. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems, are presented.
Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients' rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. We collected data for 265 stroke patients from the University of Pittsburgh Medical Center. We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models-logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)-to predict outcomes in functional ability. Statistical analyses revealed significant associations between functional improvements and specific exercises. The RF model achieved the best performance in predicting functional outcomes. In this study, we identified three rehabilitation exercises that significantly contributed to patient post-stroke functional ability improvement in the first two months. Additionally, the successful application of a machine learning model to predict patient-specific functional outcomes underscores the potential for precision rehabilitation.
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds
Venue: arXiv cs.CL 2024
Year: 2024

Open-ended worlds are those in which there are no pre-specified goals or environmental reward signal. As a consequence, an agent must know how to perform a multitude of tasks. However, when a new task is presented to an agent, we expect it to be able to reuse some of what it knows from previous tasks to rapidly learn that new task. We introduce a novel technique whereby policies for different a priori known tasks are combined into a Mixture-of-Experts model with an attention mechanism across a mix of frozen and unfrozen experts. The model learns when to attend to frozen task-specific experts when appropriate and learns new experts to handle novel situations. We work in an open-ended text-based environment in which the agent is tasked with behaving like different types of character roles and must rapidly learn behaviors associated with new character role types. We show that our agent both obtains more rewards in the zero-shot setting, and discovers these rewards with greater sample efficiency in the few-shot learning settings.
A trustless society? A political look at the blockchain vision
Venue: arXiv cs.CY 2024
Year: 2024

A lot of business and research effort currently deals with the so called decentralised ledger technology blockchain. Putting it to use carries the tempting promise to make the intermediaries of social interactions superfluous and furthermore keep secure track of all interactions. Currently intermediaries such as banks and notaries are necessary and must be trusted, which creates great dependencies, as the financial crisis of 2008 painfully demonstrated. Especially banks and notaries are said to become dispensable as a result of using the blockchain. But in real-world applications of the blockchain, the power of central actors does not dissolve, it only shifts to new, democratically illegitimate, uncontrolled or even uncontrollable power centers. As interesting as the blockchain technically is, it doesn't efficiently solve any real-world problem and is no substitute for traditional political processes or democratic regulation of power. Research efforts investigating the blockchain should be halted.
Enhanced Multimodal Content Moderation of Children's Videos using Audiovisual Fusion
Venue: arXiv cs.CV 2024
Year: 2024

Due to the rise in video content creation targeted towards children, there is a need for robust content moderation schemes for video hosting platforms. A video that is visually benign may include audio content that is inappropriate for young children while being impossible to detect with a unimodal content moderation system. Popular video hosting platforms for children such as YouTube Kids still publish videos which contain audio content that is not conducive to a child's healthy behavioral and physical development. A robust classification of malicious videos requires audio representations in addition to video features. However, recent content moderation approaches rarely employ multimodal architectures that explicitly consider non-speech audio cues. To address this, we present an efficient adaptation of CLIP (Contrastive Language-Image Pre-training) that can leverage contextual audio cues for enhanced content moderation. We incorporate 1) the audio modality and 2) prompt learning, while keeping the backbone modules of each modality frozen. We conduct our experiments on a multimodal version of the MOB (Malicious or Benign) dataset in supervised and few-shot settings.
Creating Geospatial Trajectories from Human Trafficking Text Corpora
Venue: arXiv cs.IR 2024
Year: 2024

Human trafficking is a crime that affects the lives of millions of people across the globe. Traffickers exploit the victims through forced labor, involuntary sex, or organ harvesting. Migrant smuggling could also be seen as a form of human trafficking when the migrant fails to pay the smuggler and is forced into coerced activities. Several news agencies and anti-trafficking organizations have reported trafficking survivor stories that include the names of locations visited along the trafficking route. Identifying such routes can provide knowledge that is essential to preventing such heinous crimes. In this paper we propose a Narrative to Trajectory (N2T) information extraction system that analyzes reported narratives, extracts relevant information through the use of Natural Language Processing (NLP) techniques, and applies geospatial augmentation in order to automatically plot trajectories of human trafficking routes. We evaluate N2T on human trafficking text corpora and demonstrate that our approach of utilizing data preprocessing and augmenting database techniques with NLP libraries outperforms existing geolocation detection methods.