Artificial Intelligence (AI) repeatedly match or outperform radiologists in lab experiments. However, real-world implementations of radiological AI-based systems are found to provide little to no clinical value. This paper explores how to design AI for clinical usefulness in different contexts. We conducted 19 design sessions and design interventions with 13 radiologists from 7 clinical sites in Denmark and Kenya, based on three iterations of a functional AI-based prototype. Ten sociotechnical dependencies were identified as crucial for the design of AI in radiology. We conceptualised four technical dimensions that must be configured to the intended clinical context of use: AI functionality, AI medical focus, AI decision threshold, and AI Explainability. We present four design recommendations on how to address dependencies pertaining to the medical knowledge, clinic type, user expertise level, patient context, and user situation that condition the configuration of these technical dimensions.

The PARAFAC decomposition is conceptually simple and its The (efficient and parsimonious) decomposition of higher-order tensors representation complexity scales gracefully (the number of parameters is a fundamental problem with numerous applications in a variety grows linearly with the rank). The Tucker decomposition enjoys of fields. Several methods have been proposed in the literature additional degrees of freedom at the cost of greater complexity (exponential to that end, with the Tucker and PARAFAC decompositions being dependence of the number of parameters with respect to the most prominent ones. Inspired by the latter, in this work the rank). Hierarchical tensor decompositions, such as the Tensor we propose a multi-resolution low-rank tensor decomposition to describe Train (TT) decomposition [8] or a hierarchical Tucker (hTucker) decomposition (approximate) a tensor in a hierarchical fashion. The central [9], try to alleviate this problem. The former unwraps idea of the decomposition is to recast the tensor into multiple lowerdimensional the tensor into a chain of three-dimensional tensors, and the latter tensors to exploit the structure at different levels of resolution.

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations.

In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors.

In this article we present a novel system for natural language generation (NLG) of Spanish sentences from a minimum set of meaningful words (such as nouns, verbs and adjectives) which, unlike other state-of-the-art solutions, performs the NLG task in a fully automatic way, exploiting both knowledge-based and statistical approaches. Relying on its linguistic knowledge of vocabulary and grammar, the system is able to generate complete, coherent and correctly spelled sentences from the main word sets presented by the user. The system, which was designed to be integrable, portable and efficient, can be easily adapted to other languages by design and can feasibly be integrated in a wide range of digital devices. During its development we also created a supplementary lexicon for Spanish, aLexiS, with wide coverage and high precision, as well as syntactic trees from a freely available definite-clause grammar. The resulting NLG library has been evaluated both automatically and manually (annotation). The system can potentially be used in different application domains such as augmentative communication and automatic generation of administrative reports or news.

Such problems have attracted a lot of interest for multiple reasons. From a theoretical (and even from a purely algorithmic) point of view, they constitute a clear and well-defined domain in which computational complexity issues can be analyzed and search/optimization algorithms can be put to work in challenging conditions. From a more practical point of view, there are many real-world problems which can be formalized as SSPs. Such problems are notably found in the area of computational biology, in which technological advances and the numerous initiatives are producing an unprecedented flood of data (Reichhardt, 1999) very much requiring the use of powerful computational tools to overcome the associated challenges (Meneses et al., 2005). Among such problems of interest from the perspective of SSPs we can cite discovering potential drug targets, creating diagnostic probes, designing primers, locating binding sites, or identifying consensus sequences just to name a few (Festa, 2007; Lanctot et al., 2003; Meneses et al., 2005).

The current study presents a comprehensive review of the benefits and barriers associated with the adoption of Building Information Modeling (BIM) in infrastructure projects, focusing on the period from 2013 to 2023. The research explores the manifold advantages offered by BIM, spanning the entire project life cycle, including planning, design, construction, maintenance, and sustainability. Notably, BIM enhances collaboration, facilitates real-time data-driven decision-making, and leads to substantial cost and time savings. In parallel, a systematic literature review was conducted to identify and categorize the barriers hindering BIM adoption within the infrastructure industry. Eleven studies were selected for in-depth analysis, revealing a total of 74 obstacles. Through synthetic analysis and thematic clustering, seven primary impediments to BIM adoption were identified, encompassing challenges related to education/training, resistance to change, business value clarity, perceived cost, lack of standards and guidelines, lack of mandates, and lack of initiatives. This review explores the benefits and barriers in the industry that are facing BIM adoption in infrastructure projects, giving an important perspective toward improving effective BIM adoption strategies, policies, and standards. Future directions for research and industry development are outlined, including efforts to enhance education and training, promote standardization, advocate for policy and mandates, and integrate BIM with emerging technologies.

Large Language Models (LLMs) have demonstrated potential in assisting with Register Transfer Level (RTL) design tasks. Nevertheless, there remains to be a significant gap in benchmarks that accurately reflect the complexity of real-world RTL projects. To address this, this paper presents RTL-Repo, a benchmark specifically designed to evaluate LLMs on large-scale RTL design projects. RTL-Repo includes a comprehensive dataset of more than 4000 Verilog code samples extracted from public GitHub repositories, with each sample providing the full context of the corresponding repository. We evaluate several state-of-the-art models on the RTL-Repo benchmark, including GPT-4, GPT-3.5, Starcoder2, alongside Verilog-specific models like VeriGen and RTLCoder, and compare their performance in generating Verilog code for complex projects. The RTL-Repo benchmark provides a valuable resource for the hardware design community to assess and compare LLMs' performance in real-world RTL design scenarios and train LLMs specifically for Verilog code generation in complex, multi-file RTL projects. RTL-Repo is open-source and publicly available on Github.

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability.

Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, while others replace non-English inputs with an external model's outputs such as English translation text to circumvent the challenge of LLM understanding non-English. Unfortunately, these methods often underutilize the built-in skilled reasoning and useful language understanding capabilities of LLMs. In order to better utilize the minds of reasoning and language understanding in LLMs, we propose a new method, namely MindMerger, which merges LLMs with the external language understanding capabilities from multilingual models to boost the multilingual reasoning performance. Furthermore, a two-step training scheme is introduced to first train to embeded the external capabilities into LLMs and then train the collaborative utilization of the external capabilities and the built-in capabilities in LLMs. Experiments on three multilingual reasoning datasets and a language understanding dataset demonstrate that MindMerger consistently outperforms all baselines, especially in low-resource languages. Without updating the parameters of LLMs, the average accuracy improved by 6.7% and 8.0% across all languages and low-resource languages on the MGSM dataset, respectively.