OpenRaiser/Intern-Atlas huggingface dataset:
Rapid Open-World Adaptation by Adaptation Principles Learning
Venue: arXiv cs.AI 2023
Year: 2023

Novelty adaptation is the ability of an intelligent agent to adjust its behavior in response to changes in its environment. This is an important characteristic of intelligent agents, as it allows them to continue to function effectively in novel or unexpected situations, but still stands as a critical challenge for deep reinforcement learning (DRL). To tackle this challenge, we propose a simple yet effective novel method, NAPPING (Novelty Adaptation Principles Learning), that allows trained DRL agents to respond to different classes of novelties in open worlds rapidly. With NAPPING, DRL agents can learn to adjust the trained policy only when necessary. They can quickly generalize to similar novel situations without affecting the part of the trained policy that still works. To demonstrate the efficiency and efficacy of NAPPING, we evaluate our method on four action domains that are different in reward structures and the type of task. The domains are CartPole and MountainCar (classic control), CrossRoad (path-finding), and AngryBirds (physical reasoning). We compare NAPPING with standard online and fine-tuning DRL methods in CartPole, MountainCar and CrossRoad, and state-of-the-art methods in the more complicated AngryBirds domain. Our evaluation results demonstrate that with our proposed method, DRL agents can rapidly and effectively adjust to a wide range of novel situations across all tested domains.
OsmLocator: locating overlapping scatter marks with a non-training generative perspective
Venue: arXiv cs.CV 2023
Year: 2023

Automated mark localization in scatter images, greatly helpful for discovering knowledge and understanding enormous document images and reasoning in visual question answering AI systems, is a highly challenging problem because of the ubiquity of overlapping marks. Locating overlapping marks faces many difficulties such as no texture, less contextual information, hallow shape and tiny size. Here, we formulate it as a combinatorial optimization problem on clustering-based re-visualization from a non-training generative perspective, to locate scatter marks by finding the status of multi-variables when an objective function reaches a minimum. The objective function is constructed on difference between binarized scatter images and corresponding generated re-visualization based on their clustering. Fundamentally, re-visualization tries to generate a new scatter graph only taking a rasterized scatter image as an input, and clustering is employed to provide the information for such re-visualization. This method could stably locate severely-overlapping, variable-size and variable-shape marks in scatter images without dependence of any training dataset or reference. Meanwhile, we propose an adaptive variant of simulated annealing which can works on various connected regions. In addition, we especially built a dataset named SML2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods. The results show that it can accurately locate most marks in scatter images with different overlapping severity and marker types, with about 0.3 absolute increase on an assignment-cost-based metric in comparison with state-of-the-art methods. This work is of value to data mining on massive web pages and literatures, and shedding new light on image measurement such as bubble counting.
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
Venue: arXiv cs.LG 2023
Year: 2023

Generative Adversarial Imitation Learning (GAIL) stands as a cornerstone approach in imitation learning. This paper investigates the gradient explosion in two types of GAIL: GAIL with deterministic policy (DE-GAIL) and GAIL with stochastic policy (ST-GAIL). We begin with the observation that the training can be highly unstable for DE-GAIL at the beginning of the training phase and end up divergence. Conversely, the ST-GAIL training trajectory remains consistent, reliably converging. To shed light on these disparities, we provide an explanation from a theoretical perspective. By establishing a probabilistic lower bound for GAIL, we demonstrate that gradient explosion is an inevitable outcome for DE-GAIL due to occasionally large expert-imitator policy disparity, whereas ST-GAIL does not have the issue with it. To substantiate our assertion, we illustrate how modifications in the reward function can mitigate the gradient explosion challenge. Finally, we propose CREDO, a simple yet effective strategy that clips the reward function during the training phase, allowing the GAIL to enjoy high data efficiency and stable trainability.
WiSegRT: Dataset for Site-Specific Indoor Radio Propagation Modeling with 3D Segmentation and Differentiable Ray-Tracing
Venue: arXiv cs.IT 2023
Year: 2023

The accurate modeling of indoor radio propagation is crucial for localization, monitoring, and device coordination, yet remains a formidable challenge, due to the complex nature of indoor environments where radio can propagate along hundreds of paths. These paths are resulted from the room layout, furniture, appliances and even small objects like a glass cup. They are also influenced by the object material and surface roughness. Advanced machine learning (ML) techniques have the potential to take such non-linear and hard-to-model factors into consideration. However, extensive and fine-grained datasets are urgently required. This paper presents WiSegRT, an open-source dataset for indoor radio propagation modeling. Generated by a differentiable ray tracer within the segmented 3-dimensional (3D) indoor environments, WiSegRT provides site-specific channel impulse responses for each grid point relative to the given transmitter location. We expect WiSegRT to support a wide-range of applications, such as ML-based channel prediction, accurate indoor localization, radio-based object detection, wireless digital twin, and more.
Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings
Venue: arXiv cs.CL 2023
Year: 2023

Sentence and word embeddings encode structural and semantic information in a distributed manner. Part of the information encoded -- particularly lexical information -- can be seen as continuous, whereas other -- like structural information -- is most often discrete. We explore whether we can compress transformer-based sentence embeddings into a representation that separates different linguistic signals -- in particular, information relevant to subject-verb agreement and verb alternations. We show that by compressing an input sequence that shares a targeted phenomenon into the latent layer of a variational autoencoder-like system, the targeted linguistic information becomes more explicit. A latent layer with both discrete and continuous components captures better the targeted phenomena than a latent layer with only discrete or only continuous components. These experiments are a step towards separating linguistic signals from distributed text embeddings and linking them to more symbolic representations.
Ensuring Cross-Device Portability of Electromagnetic Side-Channel Analysis
Venue: arXiv cs.CR 2023
Year: 2023

Investigation on smart devices has become an essential subdomain in digital forensics. The inherent diversity and complexity of smart devices pose a challenge to the extraction of evidence without physically tampering with it, which is often a strict requirement in law enforcement and legal proceedings. Recently, this has led to the application of non-intrusive Electromagnetic Side-Channel Analysis (EM-SCA) as an emerging approach to extract forensic insights from smart devices. EM-SCA for digital forensics is still in its infancy, and has only been tested on a small number of devices so far. Most importantly, the question still remains whether Machine Learning (ML) models in EM-SCA are portable across multiple devices to be useful in digital forensics, i.e., cross-device portability. This study experimentally explores this aspect of EM-SCA using a wide set of smart devices. The experiments using various iPhones and Nordic Semiconductor nRF52-DK devices indicate that the direct application of pre-trained ML models across multiple identical devices does not yield optimal outcomes (under 20% accuracy in most cases). Subsequent experiments included collecting distinct samples of EM traces from all the devices to train new ML models with mixed device data; this also fell short of expectations (still below 20% accuracy). This prompted the adoption of transfer learning techniques, which showed promise for cross-model implementations. In particular, for the iPhone 13 and nRF52-DK devices, applying transfer learning techniques resulted in achieving the highest accuracy, with accuracy scores of 98% and 96%, respectively. This result makes a significant advancement in the application of EM-SCA to digital forensics by enabling the use of pre-trained models across identical or similar devices.
On Computing Optimal Temporal Branchings and Spanning Subgraphs
Venue: arXiv cs.DS 2023
Year: 2023

In this work we extend the concept of out/in-branchings spanning the vertices of a digraph (also called directed spanning trees) to temporal graphs, which are digraphs where arcs are available only at prescribed times. While the literature has focused on minimum weight/earliest arrival time Temporal Out-Branchings (TOB), we solve the problem for other optimization criteria. In particular, we define five different types of TOBs based on the optimization of the travel duration (FT-TOB), of the departure time (LD-TOB), of the number of transfers (MT-TOB), of the total waiting time (MW-TOB), and of the travelling time (ST-TOB). For D$\in \{$LD,MT,ST$\}$, we provide necessary and sufficient conditions for the existence of a spanning D-TOB; when it does not exist, we characterize the maximum vertex set that a D-TOB can span. Moreover, we provide a log linear algorithm for computing such branchings. For D$\in \{$FT,MW$\}$, we prove that deciding the existence of a spanning D-TOB is NP-complete; we also show that the same results hold for optimal temporal in-branchings. Finally, we investigate the related problem of computing a spanning temporal subgraph with the minimum number of arcs and optimizing a chosen criterion D. This problem turns out to be NP-hard for any D. The hardness results are quite surprising, as computing optimal paths between nodes can always be done in polynomial time.
MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance
Venue: arXiv cs.CV 2023
Year: 2023

Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios has not been well-studied in the literature, despite its growing real-world demands. Existing mask-based inpainting methods fall short of retaining the underlying structure within the edit region. Meanwhile, mask-free attention-based methods often exhibit editing leakage and misalignment in more complex compositions. In this work, we develop MAG-Edit, a training-free, inference-stage optimization method, which enables localized image editing in complex scenarios. In particular, MAG-Edit optimizes the noise latent feature in diffusion models by maximizing two mask-based cross-attention constraints of the edit token, which in turn gradually enhances the local alignment with the desired prompt. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in achieving both text alignment and structure preservation for localized editing within complex scenarios.
Learning Temporal Properties is NP-hard
Venue: arXiv cs.LO 2023
Year: 2023

We investigate the complexity of LTL learning, which consists in deciding given a finite set of positive ultimately periodic words, a finite set of negative ultimately periodic words, and a bound B given in unary, if there is an LTL-formula of size less than or equal to B that all positive words satisfy and that all negative violate. We prove that this decision problem is NP-hard. We then use this result to show that CTL learning is also NP-hard. CTL learning is similar to LTL learning except that words are replaced by finite Kripke structures and we look for the existence of CTL formulae.
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

We introduce PolyDiff, the first diffusion-based approach capable of directly generating realistic and diverse 3D polygonal meshes. In contrast to methods that use alternate 3D shape representations (e.g. implicit representations), our approach is a discrete denoising diffusion probabilistic model that operates natively on the polygonal mesh data structure. This enables learning of both the geometric properties of vertices and the topological characteristics of faces. Specifically, we treat meshes as quantized triangle soups, progressively corrupted with categorical noise in the forward diffusion phase. In the reverse diffusion phase, a transformer-based denoising network is trained to revert the noising process, restoring the original mesh structure. At inference, new meshes can be generated by applying this denoising network iteratively, starting with a completely noisy triangle soup. Consequently, our model is capable of producing high-quality 3D polygonal meshes, ready for integration into downstream 3D workflows. Our extensive experimental analysis shows that PolyDiff achieves a significant advantage (avg. FID and JSD improvement of 18.2 and 5.8 respectively) over current state-of-the-art methods.
An In-depth Look at Gemini's Language Abilities
Venue: arXiv cs.CL 2023
Year: 2023

The recently released Google Gemini class of models are the first to comprehensively report results that rival the OpenAI GPT series across a wide variety of tasks. In this paper, we do an in-depth exploration of Gemini's language abilities, making two contributions. First, we provide a third-party, objective comparison of the abilities of the OpenAI GPT and Google Gemini models with reproducible code and fully transparent results. Second, we take a closer look at the results, identifying areas where one of the two model classes excels. We perform this analysis over 10 datasets testing a variety of language abilities, including reasoning, answering knowledge-based questions, solving math problems, translating between languages, generating code, and acting as instruction-following agents. From this analysis, we find that Gemini Pro achieves accuracy that is close but slightly inferior to the corresponding GPT 3.5 Turbo on all tasks that we benchmarked. We further provide explanations for some of this under-performance, including failures in mathematical reasoning with many digits, sensitivity to multiple-choice answer ordering, aggressive content filtering, and others. We also identify areas where Gemini demonstrates comparably high performance, including generation into non-English languages, and handling longer and more complex reasoning chains. Code and data for reproduction can be found at https://github.com/neulab/gemini-benchmark
Language-Assisted 3D Scene Understanding
Venue: arXiv cs.CV 2023
Year: 2023

The scale and quality of point cloud datasets constrain the advancement of point cloud learning. Recently, with the development of multi-modal learning, the incorporation of domain-agnostic prior knowledge from other modalities, such as images and text, to assist in point cloud feature learning has been considered a promising avenue. Existing methods have demonstrated the effectiveness of multi-modal contrastive training and feature distillation on point clouds. However, challenges remain, including the requirement for paired triplet data, redundancy and ambiguity in supervised features, and the disruption of the original priors. In this paper, we propose a language-assisted approach to point cloud feature learning (LAST-PCL), enriching semantic concepts through LLMs-based text enrichment. We achieve de-redundancy and feature dimensionality reduction without compromising textual priors by statistical-based and training-free significant feature selection. Furthermore, we also delve into an in-depth analysis of the impact of text contrastive training on the point cloud. Extensive experiments validate that the proposed method learns semantically meaningful point cloud features and achieves state-of-the-art or comparable performance in 3D semantic segmentation, 3D object detection, and 3D scene classification tasks.
Recommending Influencers to Merchants using Matching Game Algorithm
Venue: arXiv cs.SI 2023
Year: 2023

The goal of this work was to apply the ``Gale-Shapley'' algorithm to a real-world problem. We analyzed the pairing of influencers with merchants, and after a detailed specification of the variables involved, we conducted experiments to observe the validity of the approach. We conducted an analysis of the problem of aligning the interests of merchants to have digital influencers promote their products and services. We propose applying the matching algorithm approach to address this issue. We demonstrate that it is possible to apply the algorithm and still achieve corporate objectives by translating performance indicators into the desired ranking of influencers and product campaigns to be advertised by merchants.
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making
Venue: arXiv cs.HC 2023
Year: 2023

The unprecedented performance of machine learning models in recent years, particularly Deep Learning and transformer models, has resulted in their application in various domains such as finance, healthcare, and education. However, the models are error-prone and cannot be used autonomously, especially in decision-making scenarios where, technically or ethically, the cost of error is high. Moreover, because of the black-box nature of these models, it is frequently difficult for the end user to comprehend the models' outcomes and underlying processes to trust and use the model outcome to make a decision. Explainable Artificial Intelligence (XAI) aids end-user understanding of the model by utilizing approaches, including visualization techniques, to explain and interpret the inner workings of the model and how it arrives at a result. Although numerous research studies have been conducted recently focusing on the performance of models and the XAI approaches, less work has been done on the impact of explanations on human-AI team performance. This paper surveyed the recent empirical studies on XAI's impact on human-AI decision-making, identified the challenges, and proposed future research directions.
User Modeling in the Era of Large Language Models: Current Research and Future Directions
Venue: arXiv cs.CL 2023
Year: 2023

User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions. The research of text and graph mining is developing rapidly, contributing many notable solutions in the past two decades. Recently, large language models (LLMs) have shown superior performance on generating, understanding, and even reasoning over text data. The approaches of user modeling have been equipped with LLMs and soon become outstanding. This article summarizes existing research about how and why LLMs are great tools of modeling and understanding UGC. Then it reviews a few categories of large language models for user modeling (LLM-UM) approaches that integrate the LLMs with text and graph-based methods in different ways. Then it introduces specific LLM-UM techniques for a variety of UM applications. Finally, it presents remaining challenges and future directions in the LLM-UM research. We maintain the reading list at: https://github.com/TamSiuhin/LLM-UM-Reading
Analysing user sentiment data for architectural interior spaces
Venue: arXiv cs.HC 2023
Year: 2023

This study aims to develop a data driven system to enhance the analysis and improvement of user experiences in interior spaces, acknowledging the significant impact of design on individuals health, productivity, and quality of life.
Implementing biosensing based user preference visualisation in architectural spaces
Venue: arXiv cs.HC 2023
Year: 2023

This study delves into the interplay between architectural spaces and human emotions, leveraging the emergent field of neuroarchitecture. It examines the functional and aesthetic influence of architectural design on individual users, with a focus on biosensing data such as brainwave and eye tracking information to understand user preferences.
FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial Expression Recognition
Venue: arXiv cs.CV 2023
Year: 2023

We present a soft benchmark for calibrating facial expression recognition (FER). While prior works have focused on identifying affective states, we find that FER models are uncalibrated. This is particularly true when out-of-distribution (OOD) shifts further exacerbate the ambiguity of facial expressions. While most OOD benchmarks provide hard labels, we argue that the ground-truth labels for evaluating FER models should be soft in order to better reflect the ambiguity behind facial behaviours. Our framework proposes soft labels that closely approximates the average information loss based on different types of OOD shifts. Finally, we show the benefits of calibration on five state-of-the-art FER algorithms tested on our benchmark.
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
Venue: arXiv cs.LG 2023
Year: 2023

In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy ranking (OPR) problem through value-based methods, such as Off-policy evaluation (OPE). However, they fail to analyze special cases performance (e.g., worst or best cases), due to the lack of holistic characterization of policies performance. It is even more difficult to estimate precise policy values when the reward is not fully accessible under sparse settings. In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking. POPR does not rely on value estimation, and the derived performance posterior can be used to distinguish candidates in worst, best, and average-cases. To estimate the posterior, we propose POPR-EABC, an Energy-based Approximate Bayesian Computation (ABC) method conducting likelihood-free inference. POPR-EABC reduces the heuristic nature of ABC by a smooth energy function, and improves the sampling efficiency by a pseudo-likelihood. We empirically demonstrate that POPR-EABC is adequate for evaluating policies in both discrete and continuous action spaces across various experiment environments, and facilitates probabilistic comparisons of candidate policies before deployment.