Reactive navigation methods, which eliminate or minimize the use of memory, are considered, focusing on problems that still present a challenge to reactive strategies (box canyons, for example). It is shown that the addition of a local spatial memory that allows a robot to avoid areas that have already been visited offers a solution to the box canyon and other navigational problems. Such a strategy has been implemented using a spatial memory within a schema-based motor control model. Experiments have produced promising results in simulation and on mobile robots.< >
A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. These challenges are engendered from (1) finite context windows for large tables, (2) multi-faceted discrepancies amongst tokenization patterns against cell boundaries, and (3) various limitations stemming from data confidentiality in the process of using external models such as gpt-35-turbo. We propose a cooperative game dubbed “HiddenTables” as a potential resolution to this challenge. In essence, “HiddenTables” is played between the code-generating LLM “Solver” and the “Oracle” which evaluates the ability of the LLM agents to solve TableQA tasks. This game is based on natural language schemas and importantly, ensures the security of the underlying data. We provide evidential experiments on a diverse set of tables that demonstrate an LLM’s collective inability to generalize and perform on complex queries, handle compositional dependencies, and align natural language to programmatic commands when concrete table schemas are provided. Unlike encoder-based models, we have pushed the boundaries of “HiddenTables” to not be limited by the number of rows - therefore we exhibit improved efficiency in prompt and completion tokens. Our infrastructure has spawned a new dataset “PyQTax” that spans across 116,671 question-table-answer triplets and provides additional fine-grained breakdowns and labels for varying question taxonomies. Therefore, in tandem with our academic contributions regarding LLMs’ deficiency in TableQA tasks, “HiddenTables” is a tactile manifestation of how LLMs can interact with massive datasets while ensuring data security and minimizing generation costs.
We present and examine a technique for estimating the ego-motion of a mobile robot using memory-based learning and a monocular camera. Unlike other approaches that rely heavily on camera calibration and geometry to compute trajectory, our method learns a mapping from sparse optical flow to platform velocity and turn rate. We also demonstrate an efficient method of computing high-quality sparse optical flow, and techniques for using this sparse optical flow as input to a supervised learning method. We employ a voting scheme of many learners that use subsets of the sparse optical flow to cope with variable dimensionality and reduce the dimensionality of each learner. Finally, we perform experiments in which we examine the learned mapping for visual odometry, investigate the effects of varying the reduced dimensionality of the sparse optical flow state, and quantify the accuracy of two variations of our learner scheme. Our results indicate that our learning scheme estimates monocular visual odometry mainly from points on the ground plane, and reflect to a degree the minimum dimensionality imposed by the problem. In addition, we show that while this memory-based learning method cannot yet estimate ego-motion as accurately as recent geometric methods, it is possible to learn, with no explicit model of camera calibration or scene structure, complicated mappings that take advantage of properties of the camera and the environment.
We introduce a secure data-independent priority queue which supports polylogarithmic-time insertion operations and constant-time deletions and read-front (aka peek) operations as opposed to the originally introduced queue by Toft (PODC '11). Moreover, we minimize the number of comparisons required to perform different operations on Toft's priority queue. Data-independent data structures—first identified explicitly by Toft, and further elaborated by Mitchell and Zimmerman (STACS '14)—serve the purpose of computing on encrypted data without executing branching code which can be used to avoid prohibitively expensive operations in secure computation applications. Focusing on the costly sorting operations, we show significant asymptotic improvements over prior privacy preserving dark pool applications. Dark pools are securities-trading venues which attain ad-hoc order privacy, by matching orders outside of publicly visible exchanges via the so-called dark pool operators. In this paper, we describe an efficient and secure dark pool (implementing a full continuous double auction) based on our new priority queue. Our construction's security guarantees are cryptographic based on secure multiparty computation (MPC), and do not require that the dark pool operators are trusted. Our construction improves upon the asymptotic efficiency attained by previous efforts. Existing cryptographic dark pools process new orders in time which grows linearly in the size of the standing order book; ours does so in polylogarithmic time. We describe a concrete implementation of our MPC protocol with malicious security in the honest majority setting. We also report benchmarks of our implementation and compare them to prior works. Our protocol reduces the total running time by several orders of magnitude over prior secure dark pool solutions.
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that structural properties commonly exhibited by financial time series (e.g., the tendency to mean-revert) allow the use of Gaussian and Deep Gaussian Process models that further enable us to analytically evaluate optimal stopping value functions and policies. We additionally quantify uncertainty in the value function by propagating the price model through the optimal stopping analysis. We compare and contrast our proposed methods against a sampling-based method, as well as a deep learning based benchmark that is currently considered the state-of-the-art in the literature. We show that our family of algorithms outperforms benchmarks on three historical time series datasets that include intra-day and end-of-day equity stock prices as well as the daily US treasury yield curve rates.
In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach results in predicting distributions as opposed to pointwise values. To assess the robustness and quality of our approach, we examine various datasets and multiple evaluation metrics. Our experiments show that our forecasting tool is effective for cyclic data but somewhat less for irregular data such as stock prices. Importantly, when using image-based evaluation metrics, we find our method to outperform various baselines, including ARIMA, and a numerical variation of our deep learning approach.
Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based world agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism.
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