Aerial robots play a vital role in various applications where the situational awareness of the robots concerning the environment is a fundamental demand. As one such use case, drones in GPS-denied environments require equipping with different sensors (e.g., vision sensors) that provide reliable sensing results while performing pose estimation and localization. In this paper, reconstructing the maps of indoor environments alongside generating 3D scene graphs for a high-level representation using a camera mounted on a drone is targeted. Accordingly, an aerial robot equipped with a companion computer and an RGB-D camera was built and employed to be appropriately integrated with a Visual Simultaneous Localization and Mapping (VSLAM) framework proposed by the authors. To enhance the situational awareness of the robot while reconstructing maps, various structural elements, including doors and walls, were labeled with printed fiducial markers, and a dictionary of the topological relations among them was fed to the system. The VSLAM system detects markers and reconstructs the map of the indoor areas enriched with higher-level semantic entities, including corridors and rooms. Another achievement is generating multi-layered vision-based situational graphs containing enhanced hierarchical representations of the indoor environment. In this regard, integrating VSLAM into the employed drone is the primary target of this paper to provide an end-to-end robot application for GPS-denied environments. To show the practicality of the system, various real-world condition experiments have been conducted in indoor scenarios with dissimilar structural layouts. Evaluations show the proposed drone application can perform adequately w.r.t. the ground-truth data and its baseline.

Recent capability increases in large language models (LLMs) open up applications in which teams of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard to detect. In this paper, we comprehensively formalise the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both the AI and security literature. We study incentives for the use of steganography, and propose a variety of mitigation measures. Our investigations result in a model evaluation framework that systematically tests capabilities required for various forms of secret collusion. We provide extensive empirical results across a range of contemporary LLMs. While the steganographic capabilities of current models remain limited, GPT-4 displays a capability jump suggesting the need for continuous monitoring of steganographic frontier model capabilities. We conclude by laying out a comprehensive research program to mitigate future risks of collusion between generative AI models.

Retrieval pipelines-an integral component of many machine learning systems-perform poorly in domains where documents are long (e.g., 10K tokens or more) and where identifying the relevant document requires synthesizing information across the entire text. Developing long-context retrieval encoders suitable for these domains raises three challenges: (1) how to evaluate long-context retrieval performance, (2) how to pretrain a base language model to represent both short contexts (corresponding to queries) and long contexts (corresponding to documents), and (3) how to fine-tune this model for retrieval under the batch size limitations imposed by GPU memory constraints. To address these challenges, we first introduce LoCoV1, a novel 12 task benchmark constructed to measure long-context retrieval where chunking is not possible or not effective. We next present the M2-BERT retrieval encoder, an 80M parameter state-space encoder model built from the Monarch Mixer architecture, capable of scaling to documents up to 32K tokens long. We describe a pretraining data mixture which allows this encoder to process both short and long context sequences, and a finetuning approach that adapts this base model to retrieval with only single-sample batches. Finally, we validate the M2-BERT retrieval encoder on LoCoV1, finding that it outperforms competitive baselines by up to 23.3 points, despite containing 5-90x fewer parameters.

We study how to learn the optimal tax design to maximize the efficiency in nonatomic congestion games. It is known that self-interested behavior among the players can damage the system's efficiency. Tax mechanisms is a common method to alleviate this issue and induce socially optimal behavior. In this work, we take the initial step for learning the optimal tax that can minimize the social cost with \emph{equilibrium feedback}, i.e., the tax designer can only observe the equilibrium state under the enforced tax. Existing algorithms are not applicable due to the exponentially large tax function space, nonexistence of the gradient, and nonconvexity of the objective. To tackle these challenges, our algorithm leverages several novel components: (1) piece-wise linear tax to approximate the optimal tax; (2) an extra linear term to guarantee a strongly convex potential function; (3) efficient subroutine to find the ``boundary'' tax. The algorithm can find an $\epsilon$-optimal tax with $O(\beta F^2/\epsilon)$ sample complexity, where $\beta$ is the smoothness of the cost function and $F$ is the number of facilities.

In this study, we examine the fluctuation in the value of the Great Britain Pound (GBP). We focus particularly on its relationship with the United States Dollar (USD) and the Euro (EUR) currency pairs. Utilizing data from June 15, 2018, to June 15, 2023, we apply various mathematical models to assess their effectiveness in predicting the 20-day variation in the pairs' daily returns. Our analysis involves the implementation of Exponentially Weighted Moving Average (EWMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and Implied Volatility (IV) models. To evaluate their performance, we compare the accuracy of their predictions using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. We delve into the intricacies of GARCH models, examining their statistical characteristics when applied to the provided dataset. Our findings suggest the existence of asymmetric returns in the EUR/GBP pair, while such evidence is inconclusive for the GBP/USD pair. Additionally, we observe that GARCH-type models better fit the data when assuming residuals follow a standard t-distribution rather than a standard normal distribution. Furthermore, we investigate the efficacy of different forecasting techniques within GARCH-type models. Comparing rolling window forecasts to expanding window forecasts, we find no definitive superiority in either approach across the tested scenarios. Our experiments reveal that for the GBP/USD pair, the most accurate volatility forecasts stem from the utilization of GARCH models employing a rolling window methodology. Conversely, for the EUR/GBP pair, optimal forecasts are derived from GARCH models and Ordinary Least Squares (OLS) models incorporating the annualized implied volatility of the exchange rate as an independent variable. Keywords: Euro, Exponentially Weighted Moving Average, Generalized Autoregressive Conditional Heteroskedasticity, Great Britain Pound, Implied Volatility, United States Dollar 1. Introduction As defined by the [1], a volatility of a financial asset is simply how much its price changes over time.

The US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while being highly scalable and offering the flexibility to incorporate additional domain-specific constraints.

Developmental AI is a bootstrapping approach where embodied AIs start with innate competences and learn by interacting with the world. They develop abilities in small steps along a bio-inspired trajectory. However, developmental AIs have not yet reached the abilities of young children. In contrast, mainstream approaches for creating AIs have led to valuable AI systems and impressive feats. These approaches include deep learning and generative approaches (e.g., large language models) and manually constructed symbolic approaches. Manually constructed AIs are brittle even in circumscribed domains. Generative AIs are helpful on average, but they can make strange mistakes and not notice them. They sometimes lack common sense and social alignment. This position paper lays out prospects, gaps, and challenges for augmenting AI mainstream approaches with developmental AI. The ambition is to create data-rich experientially based foundation models and human-compatible, resilient, and trustworthy AIs. This research aims to produce AIs that learn to communicate, establish common ground, read critically, consider the provenance of information, test hypotheses, and collaborate. A virtuous multidisciplinary research cycle has led to developmental AIs with capabilities for multimodal perception, object recognition, and manipulation. Computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to an embodied learning approach. They need to bridge competence gaps involving nonverbal communication, speech, reading, and writing. Aspirationally, developmental AIs would learn, share what they learn, and collaborate to achieve high standards. The approach would make the creation of AIs more democratic, enabling more people to train, test, build on, and replicate AIs.

A Waymo car went up in flames in San Francisco's Chinatown after a crowd surrounded it, scrawled graffiti, smashed windows and then threw a firework inside the driverless vehicle in the middle of a crowded street Saturday night. Nobody was in the car and no injuries were reported, police and company officials said. The San Francisco Fire Department was called to the scene about 9 p.m. in the 700 block of Jackson Street, authorities said. Chinatown was bustling as people celebrated Lunar New Year. Videos posted on social media by software engineer Michael Vandi show someone using a skateboard to crush the white Jaguar's windows as the car's rooftop sensors continued to turn.

Breaking up with people has become much more complex in the digital age, with shared logins and apps meaning your ex could be watching you long after you have split. If you have shared devices, it's perfectly possible that an ex could access your email - and from there, your bank accounts, by resetting passwords. If your partner is abusive, they can also use tech to spy on you or even track you, or use your images to make AI porn, warned cybersecurity and privacy expert, Laura Kankaala at F-Secure, speaking to DailyMail.com Even smart light bulbs can pose a problem, she explained. Cyberstalking affects millions of Americans, with three million people stalked using technology in 2019, according to the U.S. Department of Justice.