OpenRaiser/Intern-Atlas huggingface dataset:
DeltaLCA: Comparative Life-Cycle Assessment for Electronics Design
Venue: arXiv cs.HC 2023
Year: 2023

Reducing the environmental footprint of electronics and computing devices requires new tools that empower designers to make informed decisions about sustainability during the design process itself. This is not possible with current tools for life cycle assessment (LCA) which require substantial domain expertise and time to evaluate the numerous chips and other components that make up a device. We observe first that informed decision-making does not require absolute metrics and can instead be done by comparing designs. Second, we can use domain-specific heuristics to perform these comparisons. We combine these insights to develop DeltaLCA, an open-source interactive design tool that addresses the dual challenges of automating life cycle inventory generation and data availability by performing comparative analyses of electronics designs. Users can upload standard design files from Electronic Design Automation (EDA) software and the tool will guide them through determining which one has greater carbon footprint. DeltaLCA leverages electronics-specific LCA datasets and heuristics and tries to automatically rank the two designs, prompting users to provide additional information only when necessary. We show through case studies DeltaLCA achieves the same result as evaluating full LCAs, and that it accelerates LCA comparisons from eight expert-hours to a single click for devices with ~30 components, and 15 minutes for more complex devices with ~100 components.
SynDiffix: More accurate synthetic structured data
Venue: arXiv cs.CR 2023
Year: 2023

This paper introduces SynDiffix, a mechanism for generating statistically accurate, anonymous synthetic data for structured data. Recent open source and commercial systems use Generative Adversarial Networks or Transformed Auto Encoders to synthesize data, and achieve anonymity through overfitting-avoidance. By contrast, SynDiffix exploits traditional mechanisms of aggregation, noise addition, and suppression among others. Compared to CTGAN, ML models generated from SynDiffix are twice as accurate, marginal and column pairs data quality is one to two orders of magnitude more accurate, and execution time is two orders of magnitude faster. Compared to the best commercial product we measured (MostlyAI), ML model accuracy is comparable, marginal and pairs accuracy is 5 to 10 times better, and execution time is an order of magnitude faster. Similar to the other approaches, SynDiffix anonymization is very strong. This paper describes SynDiffix and compares its performance with other popular open source and commercial systems.
Modelling daily mobility using mobile data traffic at fine spatiotemporal scale
Venue: arXiv cs.LG 2023
Year: 2023

We applied a data-driven approach that explores the usability of the NetMob 2023 dataset in modelling mobility patterns within an urban context. We combined the data with a highly suitable external source, the ENACT dataset, which provides a 1 km x 1km grid with estimates of the day and night population across Europe. We developed three sets of XGBoost models that predict the population in each 100m x 100m grid cell used in NetMob2023 based on the mobile data traffic of the 68 online services covered in the dataset, using the ENACT values as ground truth. The results suggest that the NetMob 2023 data can be useful for the estimation of the day and night population and grid cell level and can explain part of the dynamics of urban mobility.
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Venue: arXiv cs.CL 2023
Year: 2023

Social media platforms are rife with politically charged discussions. Therefore, accurately deciphering and predicting partisan biases using Large Language Models (LLMs) is increasingly critical. In this study, we address the challenge of understanding political bias in digitized discourse using LLMs. While traditional approaches often rely on finetuning separate models for each political faction, our work innovates by employing a singular, instruction-tuned LLM to reflect a spectrum of political ideologies. We present a comprehensive analytical framework, consisting of Partisan Bias Divergence Assessment and Partisan Class Tendency Prediction, to evaluate the model's alignment with real-world political ideologies in terms of stances, emotions, and moral foundations. Our findings reveal the model's effectiveness in capturing emotional and moral nuances, albeit with some challenges in stance detection, highlighting the intricacies and potential for refinement in NLP tools for politically sensitive contexts. This research contributes significantly to the field by demonstrating the feasibility and importance of nuanced political understanding in LLMs, particularly for applications requiring acute awareness of political bias.
Second-order Rate Analysis of a Two-user Gaussian Interference Channel with Heterogeneous Blocklength Constraints
Venue: arXiv cs.IT 2023
Year: 2023

We consider a two-user Gaussian interference channel with heterogeneous blocklength constraints (HB-GIC), strong interference, and two private messages. We propose to apply the successive interference cancellation with early decoding, i.e., decoding a message with a number of received symbols less than the blocklength at the receiver. We determine the necessary number of received symbols to achieve successful decoding of the longer codeword that satisfies the input power constraints and target average error probability constraints. To attain the results, we investigate the dependence testing bound analysis over an independent and identically distributed (i.i.d.) Gaussian input. Besides, we derive the second-order achievable rate region of the considered HB-GIC. By numerical results based on the rate-profile approach, we compare the derived second-order rate region to the first-order one, which shows the rate back-off of the considered model due to the impact of finite blocklength.
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models (LLMs) interact with a SQL interpreter. The task necessitates LLMs to strategically generate multiple SQL queries to retrieve sufficient data from a database, to reason with the acquired context, and to synthesize them into a comprehensive analytical narrative. Our findings highlight that this task poses great challenges even for the state-of-the-art GPT-4 model. We propose and evaluate two interaction strategies, and provide a fine-grained analysis of the individual stages within the interaction. A key discovery is the identification of two primary bottlenecks hindering effective interaction: the capacity for planning and the ability to generate multiple SQL queries. To address the challenge of accurately assessing answer quality, we introduce a multi-agent evaluation framework that simulates the academic peer-review process, enhancing the precision and reliability of our evaluations. This framework allows for a more nuanced understanding of the strengths and limitations of current LLMs in complex retrieval and reasoning tasks.
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
Venue: arXiv cs.CV 2023
Year: 2023

Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that the patch-level annotations also contain abundant information corresponding to both changed and unchanged objects in bi-temporal images, an intuitive solution is to segment the changes with patch-level annotations. How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task. In this paper, we propose a memory-supported transformer (MS-Former), a novel framework consisting of a bi-directional attention block (BAB) and a patch-level supervision scheme (PSS) tailored for weakly supervised change detection with patch-level annotations. More specifically, the BAM captures contexts associated with the changed and unchanged regions from the temporal difference features to construct informative prototypes stored in the memory bank. On the other hand, the BAM extracts useful information from the prototypes as supplementary contexts to enhance the temporal difference features, thereby better distinguishing changed and unchanged regions. After that, the PSS guides the network learning valuable knowledge from the patch-level annotations, thus further elevating the performance. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method in the change detection task. The demo code for our work will be publicly available at \url{https://github.com/guanyuezhen/MS-Former}.
Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders
Venue: arXiv cs.IR 2023
Year: 2023

Prevailing research practice today often relies on training dense retrievers on existing large datasets such as MSMARCO and then experimenting with ways to improve zero-shot generalization capabilities to unseen domains. While prior work has tackled this challenge through resource-intensive steps such as data augmentation, architectural modifications, increasing model size, or even further base model pretraining, comparatively little investigation has examined whether the training procedures themselves can be improved to yield better generalization capabilities in the resulting models. In this work, we recommend a simple recipe for training dense encoders: Train on MSMARCO with parameter-efficient methods, such as LoRA, and opt for using in-batch negatives unless given well-constructed hard negatives. We validate these recommendations using the BEIR benchmark and find results are persistent across choice of dense encoder and base model size and are complementary to other resource-intensive strategies for out-of-domain generalization such as architectural modifications or additional pretraining. We hope that this thorough and impartial study around various training techniques, which augments other resource-intensive methods, offers practical insights for developing a dense retrieval model that effectively generalizes, even when trained on a single dataset.
To be or not to be? an exploration of continuously controllable prompt engineering
Venue: arXiv cs.CL 2023
Year: 2023

As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for customizing models and managing their outputs. However, the challenge of precisely controlling how prompts influence these models is an area ripe for further investigation. In response, we introduce ControlPE (Continuously Controllable Prompt Engineering). ControlPE enables finer adjustments to prompt effects, complementing existing prompt engineering, and effectively controls continuous targets. This approach harnesses the power of LoRA (Low-Rank Adaptation) to create an effect akin to prompt weighting, enabling fine-tuned adjustments to the impact of prompts. Our methodology involves generating specialized datasets for prompt distillation, incorporating these prompts into the LoRA model, and carefully adjusting LoRA merging weight to regulate the influence of prompts. This provides a dynamic and adaptable tool for prompt control. Through our experiments, we have validated the practicality and efficacy of ControlPE. It proves to be a promising solution for control a variety of prompts, ranging from generating short responses prompts, refusal prompts to chain-of-thought prompts.
Certified Control for Train Sign Classification
Venue: arXiv cs.CV 2023
Year: 2023

There is considerable industrial interest in integrating AI techniques into railway systems, notably for fully autonomous train systems. The KI-LOK research project is involved in developing new methods for certifying such AI-based systems. Here we explore the utility of a certified control architecture for a runtime monitor that prevents false positive detection of traffic signs in an AI-based perception system. The monitor uses classical computer vision algorithms to check if the signs -- detected by an AI object detection model -- fit predefined specifications. We provide such specifications for some critical signs and integrate a Python prototype of the monitor with a popular object detection model to measure relevant performance metrics on generated data. Our initial results are promising, achieving considerable precision gains with only minor recall reduction; however, further investigation into generalization possibilities will be necessary.
Online Reachability Analysis and Space Convexification for Autonomous Racing
Venue: arXiv cs.MA 2023
Year: 2023

This paper presents an optimisation-based approach for an obstacle avoidance problem within an autonomous vehicle racing context. Our control regime leverages online reachability analysis and sensor data to compute the maximal safe traversable region that an agent can traverse within the environment. The idea is to first compute a non-convex safe region, which then can be convexified via a novel coupled separating hyperplane algorithm. This derived safe area is then used to formulate a nonlinear model-predictive control problem that seeks to find an optimal and safe driving trajectory. We evaluate the proposed approach through a series of diverse experiments and assess the runtime requirements of our proposed approach through an analysis of the effects of a set of varying optimisation objectives for generating these coupled hyperplanes.
Diffusion-Augmented Neural Processes
Venue: arXiv cs.LG 2023
Year: 2023

Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable. However, the current state of the art in the field (AR CNPs; Bruinsma et al., 2023) presents a few issues that prevent its widespread deployment. This work proposes an alternative, diffusion-based approach to NPs which, through conditioning on noised datasets, addresses many of these limitations, whilst also exceeding SOTA performance.
Contribution Evaluation in Federated Learning: Examining Current Approaches
Venue: arXiv cs.LG 2023
Year: 2023

Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, clients with private and potentially heterogeneous data and compute resources come together to train a common model without raw data ever leaving their locale. Instead, the participants contribute by sharing local model updates, which, naturally, differ in quality. Quantitatively evaluating the worth of these contributions is termed the Contribution Evaluation (CE) problem. We review current CE approaches from the underlying mathematical framework to efficiently calculate a fair value for each client. Furthermore, we benchmark some of the most promising state-of-the-art approaches, along with a new one we introduce, on MNIST and CIFAR-10, to showcase their differences. Designing a fair and efficient CE method, while a small part of the overall FL system design, is tantamount to the mainstream adoption of FL.
Parallel and Sequential Resources Networks
Venue: arXiv cs.MA 2023
Year: 2023

A large number of real and abstract systems involve the transformation of some basic resource into respective products under the action of multiple processing agents, which can be understood as multiple-agent production systems (MAP). At each discrete time instant, for each agent, a fraction of the resources is assumed to be kept, forwarded to other agents, or converted into work with some efficiency. The present work describes a systematic study of nine basic MAP architectures subdivided into two main groups, namely parallel and sequential distribution of resources from a single respective source. Several types of interconnections among the involved processing agents are also considered. The resulting MAP architectures are studied in terms of the total amount of work, the dispersion of the resources (states) among the agents, and the transition times from the start of operation until the respective steady state. Several interesting results are obtained and discussed, including the observation that some of the parallel designs were able to yield maximum work and minimum state dispersion, achieved at the expense of the transition time and use of several interconnections between the source and the agents. The results obtained for the sequential designs indicate that relatively high performance can be obtained for some specific cases.
Cross-Layer Optimization for Statistical QoS Provision in C-RAN with Finite-Length Coding
Venue: arXiv cs.IT 2023
Year: 2023

The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task processing. In this paper, we propose a cross-layer optimization framework based on a practical finite-length coding communication system in C-RAN, aiming at maximizing bandwidth efficiency while providing statistical quality of service (QoS) for individual services. Based on the theoretical results from effective capacity and finite-length coding, we formulate a joint optimization problem involving modulation and coding schemes (MCS), retransmission count, initial bandwidth allocation and AP selection, which reflects the coordinated decision of parameters across the physical layer, data link layer and transport layer. To tackle such a mixed-integer nonlinear programming (MINLP) problem, we firstly decompose it into a transmission parameter decision (TPD) sub-problem and a user association (UA) sub-problem, which can be solved by a binary search-based algorithm and an auction-based algorithm respectively. Simulation results demonstrate that the proposed model can accurately capture the impact of QoS requirements and channel quality on the optimal transmission parameters. Furthermore, compared with fixed transmission parameter setting, the proposed algorithms achieve the bandwidth efficiency gain up to 27.87% under various traffic and channel scenarios.
Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images
Venue: arXiv cs.CV 2023
Year: 2023

Whole slide images (WSIs) are massive digital pathology files illustrating intricate tissue structures. Selecting a small, representative subset of patches from each WSI is essential yet challenging. Therefore, following the "Divide & Conquer" approach becomes essential to facilitate WSI analysis including the classification and the WSI matching in computational pathology. To this end, we propose a novel method termed "Selection of Distinct Morphologies" (SDM) to choose a subset of WSI patches. The aim is to encompass all inherent morphological variations within a given WSI while simultaneously minimizing the number of selected patches to represent these variations, ensuring a compact yet comprehensive set of patches. This systematically curated patch set forms what we term a "montage". We assess the representativeness of the SDM montage across various public and private histopathology datasets. This is conducted by using the leave-one-out WSI search and matching evaluation method, comparing it with the state-of-the-art Yottixel's mosaic. SDM demonstrates remarkable efficacy across all datasets during its evaluation. Furthermore, SDM eliminates the necessity for empirical parameterization, a crucial aspect of Yottixel's mosaic, by inherently optimizing the selection process to capture the distinct morphological features within the WSI.
Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs
Venue: arXiv cs.HC 2023
Year: 2023

In the rapidly evolving domain of artificial intelligence, chatbots have emerged as a potent tool for various applications ranging from e-commerce to healthcare. This research delves into the intricacies of chatbot technology, from its foundational concepts to advanced generative models like ChatGPT. We present a comprehensive taxonomy of existing chatbot approaches, distinguishing between rule-based, retrieval-based, generative, and hybrid models. A specific emphasis is placed on ChatGPT, elucidating its merits for frequently asked questions (FAQs)-based chatbots, coupled with an exploration of associated Natural Language Processing (NLP) techniques such as named entity recognition, intent classification, and sentiment analysis. The paper further delves into the customization and fine-tuning of ChatGPT, its integration with knowledge bases, and the consequent challenges and ethical considerations that arise. Through real-world applications in domains such as online shopping, healthcare, and education, we underscore the transformative potential of chatbots. However, we also spotlight open challenges and suggest future research directions, emphasizing the need for optimizing conversational flow, advancing dialogue mechanics, improving domain adaptability, and enhancing ethical considerations. The research culminates in a call for further exploration in ensuring transparent, ethical, and user-centric chatbot systems.
Xputer: Bridging Data Gaps with NMF, XGBoost, and a Streamlined GUI Experience
Venue: arXiv cs.LG 2023
Year: 2023

The rapid proliferation of data across diverse fields has accentuated the importance of accurate imputation for missing values. This task is crucial for ensuring data integrity and deriving meaningful insights. In response to this challenge, we present Xputer, a novel imputation tool that adeptly integrates Non-negative Matrix Factorization (NMF) with the predictive strengths of XGBoost. One of Xputer's standout features is its versatility: it supports zero imputation, enables hyperparameter optimization through Optuna, and allows users to define the number of iterations. For enhanced user experience and accessibility, we have equipped Xputer with an intuitive Graphical User Interface (GUI) ensuring ease of handling, even for those less familiar with computational tools. In performance benchmarks, Xputer not only rivals the computational speed of established tools such as IterativeImputer but also often outperforms them in terms of imputation accuracy. Furthermore, Xputer autonomously handles a diverse spectrum of data types, including categorical, continuous, and Boolean, eliminating the need for prior preprocessing. Given its blend of performance, flexibility, and user-friendly design, Xputer emerges as a state-of-the-art solution in the realm of data imputation.