OpenRaiser/Intern-Atlas huggingface dataset:
On the Potential of CLIP for Compositional Logical Reasoning
Venue: arXiv cs.AI 2023
Year: 2023

In this paper we explore the possibility of using OpenAI's CLIP to perform logically coherent grounded visual reasoning. To that end, we formalize our terms and give a geometric analysis of how embeddings in CLIP's latent space would need to be configured in order for the system to be logically coherent. Our main conclusion is that, as usually configured, CLIP cannot perform such reasoning.
Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem
Venue: arXiv cs.AI 2023
Year: 2023

Driven by expressiveness commonalities of Python and our Python-based embedded logic-based language Natlog, we design high-level interaction patterns between equivalent language constructs and data types on the two sides. By directly connecting generators and backtracking, nested tuples and terms, coroutines and first-class logic engines, reflection and meta-interpretation, we enable logic-based language constructs to access the full power of the Python ecosystem. We show the effectiveness of our design via Natlog apps working as orchestrators for JAX and Pytorch pipelines and as DCG-driven GPT3 and DALL.E prompt generators. Keyphrases: embedding of logic programming in the Python ecosystem, high-level inter-paradigm data exchanges, coroutining with logic engines, logic-based neuro-symbolic computing, logic grammars as prompt-generators for Large Language Models, logic-based neural network configuration and training.
A Logic Programming Approach to Global Logistics in a Co-Design Environment
Venue: arXiv cs.AI 2023
Year: 2023

In a co-design environment changes need to be integrated quickly and in an automated manner. This paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft within a co-design approach with respect to key performance indicators (like cost, time or resilience). The product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide. The goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system. The main motivation for approaching this challenge is to develop the industrial system in tandem with the product and making it more resilient against unforeseen events, reducing the risks of bottlenecks in the supply chain. This risk reduction ensures continued efficiency and operational success. To address this challenging and complex task we have chosen Answer Set Programming (ASP) as the modeling language, formalizing the relevant requirements of the investigated industrial system. The approach presented in this paper covers three main aspects: the extraction of the relevant information from a knowledge graph, the translation into logic programs and the computation of existing configurations guided by optimization criteria. Finally we visualize the results for an effortless evaluation of these models. Internal results seem promising and yielded several new research questions for future improvements of the discussed use case.
The Janus System: Multi-paradigm Programming in Prolog and Python
Venue: arXiv cs.PL 2023
Year: 2023

Python and Prolog express different programming paradigms, with different strengths. Python is wildly popular because it is well-structured, easy to use, and mixes well with thousands of scientific and machine learning programs written in C. Prolog's logic-based approach provides powerful reasoning capabilities, especially when combined with constraint evaluation, probabilistic reasoning, well-founded negation, and other advances. Both languages have commonalities as well: both are usually written in C, both are dynamically typed, and both use data structures based on a small number of recursive types. This paper describes the design and implementation of Janus, a system that tightly combines Prolog and Python into a single process. Janus bi-translates data structures and offers performance of many hundreds of thousands of round-trip inter-language calls per second. Although Janus is still new, it has been used in commercial applications including natural language processing, visual query answering and robotic automation. Janus was developed for XSB, but porting Janus code to a second Prolog has been straightforward, indicating that Janus is a tool that other Prologs may easily adopt.
An xAI Approach for Data-to-Text Processing with ASP
Venue: arXiv cs.AI 2023
Year: 2023

The generation of natural language text from data series gained renewed interest among AI research goals. Not surprisingly, the few proposals in the state of the art are based on training some system, in order to produce a text that describes and that is coherent to the data provided as input. Main challenges of such approaches are the proper identification of "what" to say (the key descriptive elements to be addressed in the data) and "how" to say: the correspondence and accuracy between data and text, the presence of contradictions/redundancy in the text, the control of the amount of synthesis. This paper presents a framework that is compliant with xAI requirements. In particular we model ASP/Python programs that enable an explicit control of accuracy errors and amount of synthesis, with proven optimal solutions. The text description is hierarchically organized, in a top-down structure where text is enriched with further details, according to logic rules. The generation of natural language descriptions' structure is also managed by logic rules.
Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques
Venue: arXiv cs.AI 2023
Year: 2023

Deep Learning (DL) models have become popular for solving complex problems, but they have limitations such as the need for high-quality training data, lack of transparency, and robustness issues. Neuro-Symbolic AI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning. Symbolic knowledge injection (SKI) techniques are a popular method to incorporate symbolic knowledge into sub-symbolic systems. This work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems (MAS).
What's going on at the back-end? Risks and benefits of smart toilets
Venue: arXiv cs.CY 2023
Year: 2023

This paper presents a thematic analysis of an expert focus group considering smart toilets that record health data. The themes that arise indicate risks, many of which could be mitigated but currently are not, suggesting health benefits for the moment override other concerns only in specific application contexts.
Jaccard-constrained dense subgraph discovery
Venue: arXiv cs.DS 2023
Year: 2023

Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains. At the same time many real-world networks vary over time, that is, the dataset can be represented as a sequence of graph snapshots. Hence, it is natural to consider the question of finding dense subgraphs in a temporal network that are allowed to vary over time to a certain degree. In this paper, we search for dense subgraphs that have large pairwise Jaccard similarity coefficients. More formally, given a set of graph snapshots and a weight $λ$, we find a collection of dense subgraphs such that the sum of densities of the induced subgraphs plus the sum of Jaccard indices, weighted by $λ$, is maximized. We prove that this problem is NP-hard. To discover dense subgraphs with good objective value, we present an iterative algorithm which runs in $\mathcal{O}(n^2k^2 + m \log n + k^3 n)$ time per single iteration, and a greedy algorithm which runs in $\mathcal{O}(n^2k^2 + m \log n + k^3 n)$ time, where $k$ is the length of the graph sequence and $n$ and $m$ denote number of nodes and total number of edges respectively. We show experimentally that our algorithms are efficient, they can find ground truth in synthetic datasets and provide interpretable results from real-world datasets. Finally, we present a case study that shows the usefulness of our problem.
Benchmarking Multilabel Topic Classification in the Kyrgyz Language
Venue: arXiv cs.CL 2023
Year: 2023

Kyrgyz is a very underrepresented language in terms of modern natural language processing resources. In this work, we present a new public benchmark for topic classification in Kyrgyz, introducing a dataset based on collected and annotated data from the news site 24.KG and presenting several baseline models for news classification in the multilabel setting. We train and evaluate both classical statistical and neural models, reporting the scores, discussing the results, and proposing directions for future work.
Review of Parameter Tuning Methods for Nature-Inspired Algorithms
Venue: arXiv cs.AI 2023
Year: 2023

Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can largely influence the behaviour of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure the algorithm used for optimization may perform well and can be sufficiently robust for solving different types of optimization problems. This chapter reviews some of the main methods for parameter tuning and then highlights the important issues concerning the latest development in parameter tuning. A few open problems are also discussed with some recommendations for future research.
MerA: Merging Pretrained Adapters For Few-Shot Learning
Venue: arXiv cs.CL 2023
Year: 2023

Adapter tuning, which updates only a few parameters, has become a mainstream method for fine-tuning pretrained language models to downstream tasks. However, it often yields subpar results in few-shot learning. AdapterFusion, which assembles pretrained adapters using composition layers tailored to specific tasks, is a possible solution but significantly increases trainable parameters and deployment costs. Despite this, our preliminary study reveals that even single adapters can outperform Adapterfusion in few-shot learning, urging us to propose \textbf{\texttt{Merging Pretrained Adapters}} (MerA) that efficiently incorporates pretrained adapters to a single model through model fusion. Extensive experiments on two PLMs demonstrate that MerA achieves substantial improvements compared to both single adapters and AdapterFusion. To further enhance the capacity of MerA, we also introduce a simple yet effective technique, referred to as the "\textit{same-track}" setting, that merges adapters from the same track of pretraining tasks. With the implementation of the "\textit{same-track}" setting, we observe even more impressive gains, surpassing the performance of both full fine-tuning and adapter tuning by a substantial margin, e.g., 3.5\% in MRPC and 5.0\% in MNLI.
DiffuVolume: Diffusion Model for Volume based Stereo Matching
Venue: arXiv cs.CV 2023
Year: 2023

Stereo matching is a significant part in many computer vision tasks and driving-based applications. Recently cost volume-based methods have achieved great success benefiting from the rich geometry information in paired images. However, the redundancy of cost volume also interferes with the model training and limits the performance. To construct a more precise cost volume, we pioneeringly apply the diffusion model to stereo matching. Our method, termed DiffuVolume, considers the diffusion model as a cost volume filter, which will recurrently remove the redundant information from the cost volume. Two main designs make our method not trivial. Firstly, to make the diffusion model more adaptive to stereo matching, we eschew the traditional manner of directly adding noise into the image but embed the diffusion model into a task-specific module. In this way, we outperform the traditional diffusion stereo matching method by 22% EPE improvement and 240 times inference acceleration. Secondly, DiffuVolume can be easily embedded into any volume-based stereo matching network with boost performance but slight parameters rise (only 2%). By adding the DiffuVolume into well-performed methods, we outperform all the published methods on Scene Flow, KITTI2012, KITTI2015 benchmarks and zero-shot generalization setting. It is worth mentioning that the proposed model ranks 1st on KITTI 2012 leader board, 2nd on KITTI 2015 leader board since 15, July 2023.
CALM: Contrastive Cross-modal Speaking Style Modeling for Expressive Text-to-Speech Synthesis
Venue: arXiv cs.SD 2023
Year: 2023

To further improve the speaking styles of synthesized speeches, current text-to-speech (TTS) synthesis systems commonly employ reference speeches to stylize their outputs instead of just the input texts. These reference speeches are obtained by manual selection which is resource-consuming, or selected by semantic features. However, semantic features contain not only style-related information, but also style irrelevant information. The information irrelevant to speaking style in the text could interfere the reference audio selection and result in improper speaking styles. To improve the reference selection, we propose Contrastive Acoustic-Linguistic Module (CALM) to extract the Style-related Text Feature (STF) from the text. CALM optimizes the correlation between the speaking style embedding and the extracted STF with contrastive learning. Thus, a certain number of the most appropriate reference speeches for the input text are selected by retrieving the speeches with the top STF similarities. Then the style embeddings are weighted summarized according to their STF similarities and used to stylize the synthesized speech of TTS. Experiment results demonstrate the effectiveness of our proposed approach, with both objective evaluations and subjective evaluations on the speaking styles of the synthesized speeches outperform a baseline approach with semantic-feature-based reference selection.
On the difficulty to beat the first linear programming bound for binary codes
Venue: arXiv cs.IT 2023
Year: 2023

The first linear programming bound of McEliece, Rodemich, Rumsey, and Welch is the best known asymptotic upper bound for binary codes, for a certain subrange of distances. Starting from the work of Friedman and Tillich, there are, by now, some arguably easier and more direct arguments for this bound. We show that this more recent line of argument runs into certain difficulties if one tries to go beyond this bound (say, towards the second linear programming bound of McEliece, Rodemich, Rumsey, and Welch).
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs
Venue: arXiv cs.LG 2023
Year: 2023

Multitask learning (MTL) leverages task-relatedness to enhance performance. With the emergence of multimodal data, tasks can now be referenced by multiple indices. In this paper, we employ high-order tensors, with each mode corresponding to a task index, to naturally represent tasks referenced by multiple indices and preserve their structural relations. Based on this representation, we propose a general framework of low-rank MTL methods with tensorized support vector machines (SVMs) and least square support vector machines (LSSVMs), where the CP factorization is deployed over the coefficient tensor. Our approach allows to model the task relation through a linear combination of shared factors weighted by task-specific factors and is generalized to both classification and regression problems. Through the alternating optimization scheme and the Lagrangian function, each subproblem is transformed into a convex problem, formulated as a quadratic programming or linear system in the dual form. In contrast to previous MTL frameworks, our decision function in the dual induces a weighted kernel function with a task-coupling term characterized by the similarities of the task-specific factors, better revealing the explicit relations across tasks in MTL. Experimental results validate the effectiveness and superiority of our proposed methods compared to existing state-of-the-art approaches in MTL. The code of implementation will be available at https://github.com/liujiani0216/TSVM-MTL.
Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction
Venue: arXiv cs.LG 2023
Year: 2023

Worldwide, many millions of people die suddenly and unexpectedly each year, either with or without a prior history of cardiovascular disease. Such events are sparse (once in a lifetime), many victims will not have had prior investigations for cardiac disease and many different definitions of sudden death exist. Accordingly, sudden death is hard to predict. This analysis used NHS Electronic Health Records (EHRs) for people aged $\geq$50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010 (n = 380,000) to try to overcome these challenges. We investigated whether medical history, blood tests, prescription of medicines, and hospitalisations might, in combination, predict a heightened risk of sudden death. We compared the performance of models trained to predict either sudden death or all-cause mortality. We built six models for each outcome of interest: three taken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three of our own creation. We trained these using two different data representations: a language-based representation, and a sparse temporal matrix. We used global interpretability to understand the most important features of each model, and compare how much agreement there was amongst models using Rank Biased Overlap. It is challenging to account for correlated variables without increasing the complexity of the interpretability technique. We overcame this by clustering features into groups and comparing the most important groups for each model. We found the agreement between models to be much higher when accounting for correlated variables. Our analysis emphasises the challenge of predicting sudden death and emphasises the need for better understanding and interpretation of machine learning models applied to healthcare applications.
P2M: A Fast Solver for Querying Distance from Point to Mesh Surface
Venue: arXiv cs.GR 2023
Year: 2023

Most of the existing point-to-mesh distance query solvers, such as Proximity Query Package (PQP), Embree and Fast Closest Point Query (FCPW), are based on bounding volume hierarchy (BVH). The hierarchical organizational structure enables one to eliminate the vast majority of triangles that do not help find the closest point. In this paper, we develop a totally different algorithmic paradigm, named P2M, to speed up point-to-mesh distance queries. Our original intention is to precompute a KD tree (KDT) of mesh vertices to approximately encode the geometry of a mesh surface containing vertices, edges and faces. However, it is very likely that the closest primitive to the query point is an edge e (resp., a face f), but the KDT reports a mesh vertex \u{psion} instead. We call \u{psion} an interceptor of e (resp., f). The main contribution of this paper is to invent a simple yet effective interception inspection rule and an efficient flooding interception inspection algorithm for quickly finding out all the interception pairs. Once the KDT and the interception table are precomputed, the query stage proceeds by first searching the KDT and then looking up the interception table to retrieve the closest geometric primitive. Statistics show that our query algorithm runs many times faster than the state-of-the-art solvers.
State Estimation over Broadcast and Multi-Access Channels in an Unreliable Regime
Venue: arXiv cs.IT 2023
Year: 2023

This article examines the problem of state estimation over multi-terminal channels in an unreliable regime. More specifically, we consider two canonical settings. In the first setting, measurements of a common stochastic source need to be transmitted to two distinct remote monitors over a packet-erasure broadcast channel. In the second setting, measurements of two distinct stochastic sources need to be transmitted to a common remote monitor over a packet-erasure multi-access channel. For these networked systems, we uncover the fundamental performance limits in the sense of a causal tradeoff between the estimation error and the communication cost by identifying optimal encoding and decoding strategies. In the course of our analysis, we introduce two novel semantic metrics that play essential roles in state estimation over broadcast and multi-access channels. The first metric arising in the context of broadcast channels is the dissemination value of information, which quantifies the valuation of provisioning a piece of information to multiple receivers simultaneously. The second metric arising in the context of multi-access channels is the prioritization value of information, which quantifies the valuation of provisioning a piece of information chosen from one out of multiple transmitters. Our findings certify that the optimal encoding and decoding strategies hinge on these semantic metrics.
Intergroup Bias in Attitudes Toward Restrictions on Uncivil Political Expression and Its Underlying Mechanisms
Venue: arXiv cs.SI 2023
Year: 2023

There appears to be a dilemma between the freedom of expression and protection from the adverse effects of uncivil political expression online. While previous studies have revealed various factors that affect attitudes toward freedom of expression and speech restrictions, it is less clear whether people have intergroup biases when forming these attitudes. To address this gap, the present study conducted a pre-registered online survey experiment and investigated people's attitudes toward uncivil political expression by randomizing its in-group and out-group affiliations. The results revealed that people tend to perceive uncivil political expression directed from an out-group toward an in-group as more uncivil, compared to the expression originating from an in-group toward an out-group. This difference subsequently influences their inclination to endorse speech restrictions when faced with uncivil political comments: stronger support for restrictions on expressions from the out-group toward the in-group as opposed to those from the in-group toward the out-group. These findings should serve as a wake-up call to public opinion that advocates for restrictions on uncivil political expression.
Deep Inductive Logic Programming meets Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data efficient and capable of learning first-order logic rules that can entail data behaviour. A differentiable extension to ILP, so-called differentiable Neural Logic (dNL) networks, are able to learn Boolean functions as their neural architecture includes symbolic reasoning. We propose an application of dNL in the field of Relational Reinforcement Learning (RRL) to address dynamic continuous environments. This represents an extension of previous work in applying dNL-based ILP in RRL settings, as our proposed model updates the architecture to enable it to solve problems in continuous RL environments. The goal of this research is to improve upon current ILP methods for use in RRL by incorporating non-linear continuous predicates, allowing RRL agents to reason and make decisions in dynamic and continuous environments.