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In this paper, we explore the behavior of orthogonal involutions in the context of totally positive field extensions. Let $K/F$ be a totally positive extension of formally real fields. By Becher's result, if a quadratic form $q$ over $F$ becomes isotropic over $K$, then $q$ is weakly isotropic over $F$. We present an example in which, despite $K/F$ being totally positive, a central simple algebra $(A,\sigma)$ over $F$ with an orthogonal involution becomes isotropic over $K$, while remaining strongly isotropic over $F$. However, when $K/F$ is assumed to be a Galois totally positive $2$-extension of formally real fields, we show that an analogue of Becher's result for quadratic forms holds for orthogonal involutions. Furthermore, for a totally positive Galois field extension $K/F$, we verify Becher's conjecture for central division algebras of index $2^n$ and exponent $2$ containing a subfield of $F_{py}$ of degree $2^{n-2}$ over $F$.

We show the analogue of the Serre-Swan theorem in a context of supergeometry. This theorem gives an equivalence of the category of locally free supersheaves of bounded rank over locally ringed superspace with the category of finitely generated super projective modules over its coordinate superring, under the assumptions that every locally free supersheaf is generated by global sections and it is acyclic.

Despite extensive research, the precise role of acoustic and semantic cues in complex speech perception tasks remains unclear. In this study, we propose a paradigm to understand the encoding of these cues in electroencephalogram (EEG) data, using match-mismatch (MM) classification task. The MM task involves determining whether the stimulus and response correspond to each other or not. We design a multi-modal sequence model, based on long short term memory (LSTM) architecture, to perform the MM task. The model is input with acoustic stimulus (derived from the speech envelope), semantic stimulus (derived from textual representations of the speech content), and neural response (derived from the EEG data). Our experiments are performed on two separate conditions, i) natural passive listening condition and, ii) an auditory attention based dichotic listening condition. Using the MM task as the analysis framework, we observe that - a) speech perception is fragmented based on word boundaries, b) acoustic and semantic cues offer similar levels of MM task performance in natural listening conditions, and c) semantic cues offer significantly improved MM classification over acoustic cues in dichotic listening task. Further, the study provides evidence of right ear advantage in dichotic listening conditions., Comment: 9 Pages, 4 Figures

Rare diseases present unique challenges in healthcare, often suffering from delayed diagnosis and fragmented information landscapes. The scarcity of reliable knowledge in these conditions poses a distinct challenge for Large Language Models (LLMs) in supporting clinical management and delivering precise patient information underscoring the need for focused training on these 'zebra' cases. We present Zebra-Llama, a specialized context-aware language model with high precision Retrieval Augmented Generation (RAG) capability, focusing on Ehlers-Danlos Syndrome (EDS) as our case study. EDS, affecting 1 in 5,000 individuals, exemplifies the complexities of rare diseases with its diverse symptoms, multiple subtypes, and evolving diagnostic criteria. By implementing a novel context-aware fine-tuning methodology trained on questions derived from medical literature, patient experiences, and clinical resources, along with expertly curated responses, Zebra-Llama demonstrates unprecedented capabilities in handling EDS-related queries. On a test set of real-world questions collected from EDS patients and clinicians, medical experts evaluated the responses generated by both models, revealing Zebra-Llama's substantial improvements over base model (Llama 3.1-8B-Instruct) in thoroughness (77.5% vs. 70.1%), accuracy (83.0% vs. 78.8%), clarity (74.7% vs. 72.0%) and citation reliability (70.6% vs. 52.3%). Released as an open-source resource, Zebra-Llama not only provides more accessible and reliable EDS information but also establishes a framework for developing specialized AI solutions for other rare conditions. This work represents a crucial step towards democratizing expert-level knowledge in rare disease management, potentially transforming how healthcare providers and patients navigate the complex landscape of rare diseases., Comment: 26 pages, 4 figures, 1 supplementary figure

With the ubiquitous use of document corpora for question answering, one important aspect which is especially relevant for technical documents is the ability to extract information from tables which are interspersed with text. The major challenge in this is that unlike free-flow text or isolated set of tables, the representation of a table in terms of what is a relevant chunk is not obvious. We conduct a series of experiments examining various representations of tabular data interspersed with text to understand the relative benefits of different representations. We choose a corpus of $3^{rd}$ Generation Partnership Project (3GPP) documents since they are heavily interspersed with tables. We create expert curated dataset of question answers to evaluate our approach. We conclude that row level representations with corresponding table header information being included in every cell improves the performance of the retrieval, thus leveraging the structural information present in the tabular data., Comment: 10 pages, 4 figures, 2 tables

This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach called Automatic Semantic Augmentation of Prompts (ASAP), which uses a Large Language Model (LLM) to generate leading summary comments for Java methods. ASAP enhances the $LLM's$ prompt context by integrating static program analysis and information retrieval techniques to identify similar exemplar methods along with their developer-written Javadocs, and serves as the baseline in our study. In contrast, we explore and compare the performance of four simpler approaches that do not require static program analysis, information retrieval, or the presence of exemplars as in the ASAP method. Our methods rely solely on the Java method body as input, making them lightweight and more suitable for rapid deployment in commercial software development environments. We conducted experiments on an Ericsson software project and replicated the study using two widely-used open-source Java projects, Guava and Elasticsearch, to ensure the reliability of our results. Performance was measured across eight metrics that capture various aspects of similarity. Notably, one of our simpler approaches performed as well as or better than the ASAP method on both the Ericsson project and the open-source projects. Additionally, we performed an ablation study to examine the impact of method names on Javadoc summary generation across our four proposed approaches and the ASAP method. By masking the method names and observing the generated summaries, we found that our approaches were statistically significantly less influenced by the absence of method names compared to the baseline. This suggests that our methods are more robust to variations in method names and may derive summaries more comprehensively from the method body than the ASAP approach., Comment: 16 pages, 6 tables, 4 figures. Accepted at the 2024 International Conference on Software Maintenance and Evolution (ICSME) 2024 - Industry Track

Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on open-source LLM for specialized domains has challenges of evaluating generated responses. A popular framework in the literature is the RAG Assessment (RAGAS), a publicly available library which uses LLMs for evaluation. One disadvantage of RAGAS is the lack of details of derivation of numerical value of the evaluation metrics. One of the outcomes of this work is a modified version of this package for few metrics (faithfulness, context relevance, answer relevance, answer correctness, answer similarity and factual correctness) through which we provide the intermediate outputs of the prompts by using any LLMs. Next, we analyse the expert evaluations of the output of the modified RAGAS package and observe the challenges of using it in the telecom domain. We also study the effect of the metrics under correct vs. wrong retrieval and observe that few of the metrics have higher values for correct retrieval. We also study for differences in metrics between base embeddings and those domain adapted via pre-training and fine-tuning. Finally, we comment on the suitability and challenges of using these metrics for in-the-wild telecom QA task., Comment: Accepted for publication in ICML 2024 Workshop on Foundation Models in the Wild

One of the key predictions of the standard inflationary paradigm is the quantum mechanical generation of the transverse and traceless tensor fluctuations due to the rapid accelerated expansion of space, which later constitute a stochastic background of primordial gravitational waves (GWs). The amplitude of the (nearly) scale-invariant inflationary tensor power spectrum at large scales provides us with crucial information about the energy scale of inflation in the case of the minimal inflaton coupling to gravity. Furthermore, the spectral energy density, $\Omega_{_{\rm GW}}(f)$, of the GWs at sufficiently small scales (or, large frequencies $f$) serves as an important observational probe of post-inflationary primordial dynamics. In fact, the small-scale spectral tilt, $n_{_{\rm GW}} = \frac{{\rm d}\log{\Omega_{_{\rm GW}}}}{{\rm d}\log{f}}$, of the spectral energy density of GWs is sensitive to the (unknown) post-inflationary equation of state (EoS), $w$, of the universe; with a softer EoS ($w < 1/3$) leading to a red tilt: $n_{_{\rm GW}} < 0$, while a stiffer EoS ($w > 1/3$) resulting in a blue tilt: $n_{_{\rm GW}} > 0$. The post-inflationary dynamics, however, is generically expected to be quite complex, potentially involving a number of distinct phases. Hence, in this work, we discuss the possibility of multiple sharp transitions, namely $w_1 \to w_2 \to w_3 \to ... \to w_n$, in the EoS of the post-inflationary universe and compute the corresponding spectral energy density of the inflationary GWs. We explicitly determine the region of the parameter space $\lbrace{ w_1, \, w_2, \, w_3, ..., w_n\rbrace}$ which leads to a potentially detectable signal in the upcoming GW detectors, without violating the current constraints., Comment: 52 pages, 15 figures, Github link provided in the paper, Revised version, additional references and appendix

A plethora of sentence embedding models makes it challenging to choose one, especially for technical domains rich with specialized vocabulary. In this work, we domain adapt embeddings using telecom, health and science datasets for question answering. We evaluate embeddings obtained from publicly available models and their domain-adapted variants, on both point retrieval accuracies, as well as their (95\%) confidence intervals. We establish a systematic method to obtain thresholds for similarity scores for different embeddings. As expected, we observe that fine-tuning improves mean bootstrapped accuracies. We also observe that it results in tighter confidence intervals, which further improve when pre-training is preceded by fine-tuning. We introduce metrics which measure the distributional overlaps of top-$K$, correct and random document similarities with the question. Further, we show that these metrics are correlated with retrieval accuracy and similarity thresholds. Recent literature shows conflicting effects of isotropy on retrieval accuracies. Our experiments establish that the isotropy of embeddings (as measured by two independent state-of-the-art isotropy metric definitions) is poorly correlated with retrieval performance. We show that embeddings for domain-specific sentences have little overlap with those for domain-agnostic ones, and fine-tuning moves them further apart. Based on our results, we provide recommendations for use of our methodology and metrics by researchers and practitioners.

Large language models have limited context capacity, hindering reasoning over long conversations. We propose the Hierarchical Aggregate Tree memory structure to recursively aggregate relevant dialogue context through conditional tree traversals. HAT encapsulates information from children nodes, enabling broad coverage with depth control. We formulate finding best context as optimal tree traversal. Experiments show HAT improves dialog coherence and summary quality over baseline contexts, demonstrating the techniques effectiveness for multi turn reasoning without exponential parameter growth. This memory augmentation enables more consistent, grounded longform conversations from LLMs, Comment: 6 pages, 2 figures

Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social stigma and lack of access to mental health care. With the popularity of social media, people have turned to online forums, such as Reddit to express their feelings and seek support. This provides the opportunity to support people with the aid of artificial intelligence. Social media posts can be classified, using text classification, to help connect people with professional help. However, these systems fail to account for the inherent uncertainty in classifying mental health conditions. Unlike other areas of healthcare, mental health conditions have no objective measurements of disease often relying on expert opinion. Thus when formulating deep learning problems involving mental health, using hard, binary labels does not accurately represent the true nature of the data. In these settings, where human experts may disagree, fuzzy or soft labels may be more appropriate. The current work introduces a novel label smoothing method which we use to capture any uncertainty within the data. We test our approach on a five-label multi-class classification problem. We show, our semi-supervised deep label smoothing method improves classification accuracy above the existing state of the art. Where existing research reports an accuracy of 43\% on the Reddit C-SSRS dataset, using empirical experiments to evaluate our novel label smoothing method, we improve upon this existing benchmark to 52\%. These improvements in model performance have the potential to better support those experiencing mental distress. Future work should explore the use of probabilistic methods in both natural language processing and quantifying contributions of both epistemic and aleatoric uncertainty in noisy datasets.

Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rTMS treatment outcomes for most patients using fMRI connectivity features. While these models can reliably predict treatment outcomes for many patients for some underrepresented fMRI connectivity measures DNN models are unable to reliably predict treatment outcomes. As such we propose a novel method, Diversity Enhancing Conditional General Adversarial Network (DE-CGAN) for oversampling these underrepresented examples. DE-CGAN creates synthetic examples in difficult-to-classify regions by first identifying these data points and then creating conditioned synthetic examples to enhance data diversity. Through empirical experiments we show that a classification model trained using a diversity enhanced training set outperforms traditional data augmentation techniques and existing benchmark results. This work shows that increasing the diversity of a training dataset can improve classification model performance. Furthermore, this work provides evidence for the utility of synthetic patients providing larger more robust datasets for both AI researchers and psychiatrists to explore variable relationships.

Retrieval augmented generation (RAG) for technical documents creates challenges as embeddings do not often capture domain information. We review prior art for important factors affecting RAG and perform experiments to highlight best practices and potential challenges to build RAG systems for technical documents., Comment: Published as a Tiny Paper at ICLR 2024

In this paper, the multicommodity network flow (MCNF) problem is formulated as a mixed integer programing model which is known as NP-hard, aiming to optimize the vehicle routing and minimize the total travel cost. We explore the potential of quantum computing, specifically quantum annealing, by comparing its performance in terms of solution quality and efficiency against the traditional method. Our findings indicate that quantum annealing holds significant promise for enhancing computation in large-scale transportation logistics problems.

Complex systems are not entirely decomposable, hence interdependences arise at the interfaces in complex projects. When changes occur, significant risks arise at these interfaces as it is hard to identify, manage and visualise the systemic consequences of changes. Particularly problematic are the interfaces in which there are multiple interdependencies, which occur where the boundaries between design components, contracts and organisation coincide, such as between design disciplines. In this paper, we propose an approach to digital twin-based interface management, through an underpinning state-of-the-art review of the existing technical literature and a small pilot to identify the characteristics of future data-driven solutions. We set out an approach to digital twin-based interface management and an agenda for research on advanced methodologies for managing change in complex projects. This agenda includes the need to integrate work on identifying systems interfaces, change propagation and visualisation, and the potential to significantly extend the limitations of existing solutions by using developments in the digital twin, such as linked data, semantic enrichment, network analyses, natural language processing (NLP)-enhanced ontology and machine learning., Comment: 11 pages, 5 figures

Large Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, requiring further domain expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful biomedical text rooted in established knowledge. Compared to the existing RAG technique for Knowledge Graphs, the proposed method utilizes minimal graph schema for context extraction and uses embedding methods for context pruning. This optimization in context extraction results in more than 50% reduction in token consumption without compromising the accuracy, making a cost-effective and robust RAG implementation on proprietary LLMs. KG-RAG consistently enhanced the performance of LLMs across diverse biomedical prompts by generating responses rooted in established knowledge, accompanied by accurate provenance and statistical evidence (if available) to substantiate the claims. Further benchmarking on human curated datasets, such as biomedical true/false and multiple-choice questions (MCQ), showed a remarkable 71% boost in the performance of the Llama-2 model on the challenging MCQ dataset, demonstrating the framework's capacity to empower open-source models with fewer parameters for domain specific questions. Furthermore, KG-RAG enhanced the performance of proprietary GPT models, such as GPT-3.5 and GPT-4. In summary, the proposed framework combines explicit and implicit knowledge of KG and LLM in a token optimized fashion, thus enhancing the adaptability of general-purpose LLMs to tackle domain-specific questions in a cost-effective fashion., Comment: 29 pages, 5 figures, 1 table, 1 supplementary file

This paper provides an overview of the historical progression of distributed optimization techniques, tracing their development from early duality-based methods pioneered by Dantzig, Wolfe, and Benders in the 1960s to the emergence of the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. The initial focus on Lagrangian relaxation for convex problems and decomposition strategies led to the refinement of methods like the Alternating Direction Method of Multipliers (ADMM). The resurgence of interest in distributed optimization in the late 2000s, particularly in machine learning and imaging, demonstrated ADMM's practical efficacy and its unifying potential. This overview also highlights the emergence of the proximal center method and its applications in diverse domains. Furthermore, the paper underscores the distinctive features of ALADIN, which offers convergence guarantees for non-convex scenarios without introducing auxiliary variables, differentiating it from traditional augmentation techniques. In essence, this work encapsulates the historical trajectory of distributed optimization and underscores the promising prospects of ALADIN in addressing non-convex optimization challenges., Comment: 23 pages

The COVID-19 infection fatality rate (IFR) is the proportion of individuals infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately affects older individuals, age-specific IFR estimates are imperative to facilitate comparisons of the impact of COVID-19 between locations and prioritize distribution of scare resources. However, there lacks a coherent method to synthesize available data to create estimates of IFR and seroprevalence that vary continuously with age and adequately reflect uncertainties inherent in the underlying data. In this paper we introduce a novel Bayesian hierarchical model to estimate IFR as a continuous function of age that acknowledges heterogeneity in population age structure across locations and accounts for uncertainty in the estimates due to seroprevalence sampling variability and the imperfect serology test assays. Our approach simultaneously models test assay characteristic, serology, and death data, where the serology and death data are often available only for binned age groups. Information is shared across locations through hierarchical modeling to improve estimation of the parameters with limited data. Modeling data from 26 developing country locations during the first year of the COVID-19 pandemic, we found seroprevalence did not change dramatically with age, and the IFR at age 60 was above the high-income country benchmark for most locations.

Recent studies have shown that the underlying neural mechanisms of human speech comprehension can be analyzed using a match-mismatch classification of the speech stimulus and the neural response. However, such studies have been conducted for fixed-duration segments without accounting for the discrete processing of speech in the brain. In this work, we establish that word boundary information plays a significant role in sentence processing by relating EEG to its speech input. We process the speech and the EEG signals using a network of convolution layers. Then, a word boundary-based average pooling is performed on the representations, and the inter-word context is incorporated using a recurrent layer. The experiments show that the modeling accuracy can be significantly improved (match-mismatch classification accuracy) to 93% on a publicly available speech-EEG data set, while previous efforts achieved an accuracy of 65-75% for this task., Comment: 5 pages, 4 figures, 4 tables, accepted to Interspeech2023 conference

The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this paper, we analyze capabilities and limitations of incorporating such models in conversational interfaces for the telecommunication domain, specifically for enterprise wireless products and services. Using Cradlepoint's publicly available data for our experiments, we present a comparative analysis of the responses from such models for multiple use-cases including domain adaptation for terminology and product taxonomy, context continuity, robustness to input perturbations and errors. We believe this evaluation would provide useful insights to data scientists engaged in building customized conversational interfaces for domain-specific requirements., Comment: 11 pages, 2 figures, 8 tables

Quantum entanglement and squeezing have significantly improved phase estimation and imaging in interferometric settings beyond the classical limits. However, for a wide class of non-interferometric phase imaging/retrieval methods vastly used in the classical domain e.g., ptychography and diffractive imaging, a demonstration of quantum advantage is still missing. Here, we fill this gap by exploiting entanglement to enhance imaging of a pure phase object in a non-interferometric setting, only measuring the phase effect on the free-propagating field. This method, based on the so-called "transport of intensity equation", is quantitative since it provides the absolute value of the phase without prior knowledge of the object and operates in wide-field mode, so it does not need time-consuming raster scanning. Moreover, it does not require spatial and temporal coherence of the incident light. Besides a general improvement of the image quality at a fixed number of photons irradiated through the object, resulting in better discrimination of small details, we demonstrate a clear reduction of the uncertainty in the quantitative phase estimation. Although we provide an experimental demonstration of a specific scheme in the visible spectrum, this research also paves the way for applications at different wavelengths, e.g., X-ray imaging, where reducing the photon dose is of utmost importance., Comment: arXiv admin note: text overlap with arXiv:2109.10095