Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning

A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning. The desired goals of an algorithm that can shape the learning of an RL agent with external advice include (a) maintaining policy invariance; (b) accelerating the learning of the agent; and (c) learning from arbitrary advice [3]. To address this challenge this paper formulates the problem of incorporating external advice in RL as a multi-armed bandit called shaping-bandits. The reward of each arm of shaping bandits corresponds to the return obtained by following the expert or by following a default RL algorithm learning on the true environment reward.We show that directly applying existing bandit and shaping algorithms that do not reason about the non-stationary nature of the underlying returns can lead to poor results. Thus we propose UCB-PIES (UPIES), Racing-PIES (RPIES), and Lazy PIES (LPIES) three different shaping algorithms built on different assumptions that reason about the long-term consequences of following the expert policy or the default RL algorithm. Our experiments in four different settings show that these proposed algorithms achieve the above-mentioned goals whereas the other algorithms fail to do so.
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability

Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework. This model family represents each graph as a collection of subgraphs, generally extracted by random sampling or with hand-crafted heuristics. Our key observation is that by selecting"meaningful"subgraphs, besides improving the expressivity of a GNN, it is also possible to obtain interpretable results. For this purpose, we introduce a novel framework that jointly predicts the class of the graph and a set of explanatory sparse subgraphs, which can be analyzed to understand the decision process of the classifier. We compare the performance of our framework against standard subgraph extraction policies, like random node/edge deletion strategies. The subgraphs produced by our framework allow to achieve comparable performance in terms of accuracy, with the additional benefit of providing explanations.
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras

Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their complexity is often prohibitive for application at the edge or in robots, where efficiency and latency play crucial role. To address this challenge, we build on the latest developments in event-based vision and spiking neural networks. We propose a new network architecture, inspired by Timelens, that improves the state-of-the-art self-supervised optical flow accuracy when operated both in spiking and non-spiking mode. To implement a real-time pipeline with a physical event camera, we propose a methodology for principled model simplification based on activity and latency analysis. We demonstrate high speed optical flow prediction with almost two orders of magnitude reduced complexity while maintaining the accuracy, opening the path for real-time deployments.
Fundamental limits to near-field optical response

Near-field optics is an exciting frontier of photonics and plasmonics. The tandem of strongly localized fields and enhanced emission rates offers significant opportunities for wide-ranging applications, while also creating basic questions: How large can such enhancements be? To what extent do material losses inhibit optimal response? Over what bandwidths can these effects be sustained? This chapter surveys theoretical techniques for answering these questions. We start with physical intuition and mathematical definitions of the response functions of interest (LDOS, CDOS, SERS, NFRHT, etc.), after which we describe the general theoretical techniques for bounding such functions. Finally, we apply those techniques specifically to near-field optics, for which we describe known bounds, optimal designs, and open questions.
SEA: A Scalable Entity Alignment System

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a full-batch fashion, which prohibits EA from being scalable on large-scale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.
The emergence of burstiness in temporal networks

Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time (IET), namely the duration between successive pairwise interactions. Empirical studies have found IET distributions that are heavy-tailed (or"bursty"), for temporally varying interaction, both physical and digital. But it is difficult to construct theoretical models of time-varying activity on a network that reproduces the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures a desired target IET distribution of single nodes/links, provided the distribution fulfills a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.
The Truss: Body Form Reconstructions in Morphometrics

Strauss, R. E. , and F. L. Bookstein (Museum of Zoology, The University of Michigan, Ann Arbor, Michigan 48109) 1982. The truss: body form reconstruction in morphometrics. Syst. Zool. , 31:113-135. -In principle, any measured distances between landmarks of a form may serve as characters for morphometric analyses. Systematic studies typically are based on a highly biased and repetitious sample of these. But collections of landmarks and the distances among them must be homologous from form to form for comparisons to be meaningful, and an adequate character set should at least permit the full reconstruction of the original configuration of landmarks. We describe a geometric protocol for character selection, the truss network, which enforces systematic coverage of the form and which exhaustively and redundantly archives the landmark configuration. Reconstruction of the form from truss measures provides Cartesian coordinates for landmarks and allows estimation of, and compensation for, measurement error. Samples of forms may be averaged and standardized to one or more common reference sizes by regression of measured distances on a composite measure of body size, followed by reconstruction of the form using distance values predicted by the regression functions at some standard body size. Principal component loadings of distance measures may be indicated directly on the truss network to display patterns of within-group allometry or between-group shape differences. Because the truss enforces use of cross measurements, discrimination among groups may be enhanced. Composite mapped forms are useful in biorthogonal analyses of differences in shape because they allow the comparison of averaged forms among samples. Certain patterns of principal component loadings are concordant with, and provide an initial sampling of, the biorthogonal grids for these deformations. [Allometry; biorthogonal analysis; discriminant analysis; fishes; morphometrics; multivariate analysis; principal components; triangulation; truss. ]
Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering

The widespread adoption of large language models (LLMs), such as OpenAI's ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate plausible-sounding but false outputs, leading to hallucinations. In this article, we discuss the importance of prompt engineering in mitigating these risks and harnessing the full potential of GPT for geotechnical applications. We explore the challenges and pitfalls associated with LLMs and highlight the role of context in ensuring accurate and valuable responses. Furthermore, we examine the development of context-specific search engines and the potential of LLMs to become a natural interface for complex tasks, such as data analysis and design. We also develop a unified interface using natural language to handle complex geotechnical engineering tasks and data analysis. By integrating GPT into geotechnical engineering workflows, professionals can streamline their work and develop sustainable and resilient infrastructure systems for the future.
Structural design with polymorphic uncertainty models

Structural design should be robust with respect to the polymorphic characteristics of the available data and information. Generally, the availability of data and information in engineering practice is limited. Incomplete, fragmentary, diffuse and frequently expert-specified knowledge leads to imprecision in data. In addition, engineers have to cope with the objective variability and fluctuations in material, geometry and loading. The objective of numerical structural design is the computation of reliable and economic structures. The design task is to find the best parameter combination, with respect to a design objective, inside a permissible design space, defined by constraints. In this contribution, strategies for the solution of an optimisation task with polymorphic uncertain design variables and further uncertain a priori parameters are proposed. The active and the passive optimisation strategies are mathematically formulated and numerical implementations are shown. Two engineering examples show the capability of the approaches.
A global nonhydrostatic semi-Lagrangian atmospheric model without orography

Abstract A global nonhydrostatic semi-implicit semi-Lagrangian (SISL) atmospheric model with orography has been developed. The height-based terrain-following σz coordinate of Gal-Chen and Somerville is used to incorporate the orography. A 3D vector form of the SISL formulation is proposed. It is based on the complete Navier–Stokes equations. The model is stable for large time steps of up to 1 h at horizontal/vertical resolution of 2.8125°/1200 m. Isolated bell-shaped mountain profiles and real orography are employed to evaluate the model performance. The sensitivity of the model with orography to the order of accuracy of the uncentering scheme, the reference temperature (T), and size of the time step are similar to that of the model without orography described in Semazzi et al. The authors find that for successful execution of the model, it is important that the orographic height Zs, the reference state mass variable (qs), and T satisfy the hydrostatic balance relationship in the terrain-following σz coor.
Analisi di impatto sul budget sanitario italiano di enzalutamide per il trattamento del carcinoma prostatico metastatico ormono-sensibile

ABSTRACT Budget impact analysis of enzalutamide for the treatment of hormone-sensitive metastatic prostate cancer in Italy Backgroud: After demonstrating additional benefit versus standard of care in ARCHES and ENZAMET studies, enzalutamide was reimbursed in Italy in May 2022 by the National Health Service (NHS) for the treatment of hormone-sensitive metastatic prostate cancer (mHSPC). Objective: This study estimates the financial impact associated to the introduction of enzalutamide for patients with mHSPC. Methods: A budget impact model was developed with a dynamic cost calculator approach, in which the impact on subsequent lines of therapy was considered. The analysis considered the NHS perspective and a 3-year time horizon. Included costs were related to drug acquisition and administration, monitoring, patient follow-up and adverse events. Eligible population was estimated based on published literature, real-world data and experts’ opinion while market shares were assessed considering real-world data and assumptions. National tariffs and published literature were considered for unit costs. Results: Eligible population was estimated at 6,200, 6,206 and 6,212 in years 1, 2 and 3 respectively. The introduction of enzalutamide, considering a progressive increase of market shares of 10%, 18% and 25%, is expected to overall increase NHS healthcare expenditure by € 688 thousands and € 2.6 and € 5.6 million in years 1, 2 and 3 respectively, corresponding on average to 1.55% of the overall prostate cancer expenditure. Results are robust across one-way sensitivity analyses, while confidential discount agreements of on-patent drugs might significantly impact the estimates. Conclusion: The introduction of enzalutamide for the treatment of adult patients with mHSPC is expected to increase patients’ health with a moderate impact on costs for the Italian NHS.
Proton-metal reactions in thin films with Boltzmann distribution similar to nuclear astrophysics

The proton reactions in host metals like palladium, nickel, or titanium generate elements up to a proton number Z = 82 (lead), where the generation probability follows a kind ofBoltzmann distribution. This is very similar to the standard abundance distribution of the elements in the universe for heavy elements. The analogy leads to a relation to the magic numbers of the nuclear shell model, to its alternative (more general) foundation on the Bagge series contrary to the spin model of Jensen and Goeppert-Mayer, and to new large magic numbers in agreement with Greiner et al.'s results on superheavy elements.
Construction of Meanings in Living and Artificial Agents

TAKAC, Martin: Construction of Meanings in Living and Artificial Agents. [Dissertation thesis]. Comenius University in Bratislava. Faculty of Mathematics, Physics and Informatics; Department of Applied Informatics. Thesis advisor: Doc. RNDr. Ľubica Benuskova, PhD. Thesis defense committee: Applied Informatics. Committee chairman: . Qualification degree: Philosophiae doctor in Applied Informatics. Bratislava, 2007. 171 p. This thesis addresses the issues of the nature and origin of understanding and meanings in language with the computational modeling methodology. It reviews formal semantic theories, studies meanings in living organisms within their evolutionary context and takes into account empirical findings about language acquisition by children. It analyzes meanings in existing computational systems with respect to grounding in interaction with real or simulated environments. The main contributions of the thesis follow: First, we define a new original semantics based on so-called identification criteria. The semantics allows for representation of objects, properties, relations, changes, complex situations and events. All meanings can be constructed by extracting cross-situational similarities among instances of a category. Both the theory and mechanisms of meaning construction are specified rigorously enough to allow for implementation in computational models. Second, we present two computational models of interaction-grounded meaning construction. In the model of individual category construction, the instances are grouped to categories by common motor programs (affordances), while in the model of social learning, focused on the influence of naming on category formation, entities are considered members of the same category, if they are labeled with the same word by an external teacher. Results of experimenting with both models validate the proposed meaningformation mechanisms. Third, we report and analyze simulation results of an experiment focused on the dynamics of meanings in iterated intergenerational transmission.
Are stimulant and non‐stimulant drugs effective and safe for treating ADHD co‐occurring with epilepsy in children? A Cochrane Review summary with commentary

The aim of this commentary is to discuss from a rehabilitation perspective the Cochrane Review ‘Stimulant and nonstimulant drug therapy for people with attention deficit hyperactivity disorder and epilepsy’ by Eaton et al.,1* published by The Cochrane Epilepsy Group. This Cochrane Corner is produced in agreement with Developmental Medicine & Child Neurology by Cochrane Rehabilitation with viewsa of the review summary author in the ‘implications for practice’ section.
Single-step SNPBLUP evaluation in six German beef cattle breeds.

The implementation of genomic selection for six German beef cattle populations was evaluated. Although the multiple-step implementation of genomic selection is the status quo in most national dairy cattle evaluations, the breeding structure of German beef cattle, coupled with the shortcoming and complexity of the multiple-step method, makes single step a more attractive option to implement genomic selection in German beef cattle populations. Our objective was to develop a national beef cattle single-step genomic evaluation in five economically important traits in six German beef cattle populations and investigate its impact on the accuracy and bias of genomic evaluations relative to the current pedigree-based evaluation. Across the six breeds in our study, 461,929 phenotyped and 14,321 genotyped animals were evaluated with a multi-trait single-step model. To validate the single-step model, phenotype data in the last 2 years were removed in a forward validation study. For the conventional and single-step approaches, the genomic estimated breeding values of validation animals and other animals were compared between the truncated and the full evaluations. The correlation of the GEBVs between the full and truncated evaluations in the validation animals was slightly higher in the single-step evaluation. The regression of the full GEBVs on truncated GEBVs was close to the optimal value of 1 for both the pedigree-based and the single-step evaluations. The SNP effect estimates from the truncated evaluation were highly correlated with those from the full evaluation, with values ranging from 0.79 to 0.94. The correlation of the SNP effect was influenced by the number of genotyped animals shared between the full and truncated evaluations. The regression coefficients of the SNP effect of the full evaluation on the truncated evaluation were all close to the expected value of 1, indicating unbiased estimates of the SNP markers for the production traits. The Manhattan plot of the SNP effect estimates identified chromosomal regions harbouring major genes for muscling and body weight in breeds of French origin. Based on the regression intercept and slope of the GEBVs of validation animals, the single-step evaluation was neither inflated nor deflated across the six breeds. Overall, the single-step model resulted in a more accurate and stable evaluation. However, due to the small number of genotyped individuals, the single-step method only provided slightly better results when compared to the pedigree-based method.
Random chaotic dynamical systems group and stream cipher algorithms

The characteristics of the existing cipher algorithms for chaotic system were briefly analyzed in this paper.In order to provide a more effective encryption technique,the concept of random chaotic dynamical systems group was given.The random chaotic systems group can generate complex chaotic systems sequence under given conditions.Therefore,a stream cipher algorithm based on the random chaotic dynamical systems group was presented directly.Its encryption process could be influenced by secret keys,plaintext and specialty of chaotic systems group.Compared with other algorithms,the algorithm has been proved of higher security.The results of experiments show stream cipher algorithm based on the random chaotic dynamical systems group is easy to realize.Meanwhile,it has been tested with good encryption result and large secret space.
People With Type 1 Diabetes of African Caribbean Ethnicity Are at Increased Risk of Developing Sight-Threatening Diabetic Retinopathy

OBJECTIVE There is limited information on the effect of ethnicity on the development of referable sight-threatening diabetic retinopathy (STDR) in people with type 1 diabetes. This study describes the risk factors for STDR in a diverse cohort of people with type 1 diabetes attending a regional diabetes eye screening service. RESEARCH DESIGN AND METHODS Clinical and digital retinal imaging data from 1,876 people with type 1 diabetes (50% women, 72.1% Caucasian, 17.3% African Caribbean, 2.9% Asian, and 7.6% other) with no retinopathy at baseline, attending surveillance eye screening were reviewed. Referable STDR was defined as the presence of any moderate to severe nonproliferative or preproliferative diabetic retinopathy or proliferative diabetic retinopathy or maculopathy in either eye as per U.K. National Diabetic Eye Screening criteria. Median follow-up was 6 years. RESULTS The median (interquartile range) age of the cohort was 29 (21, 41) years. Of the cohort of 1,876 people, 359 (19%) developed STDR. People who developed STDR had higher baseline HbA1c, raised systolic blood pressure (SBP), longer diabetes duration, and were more often of African Caribbean origin (24% vs. 15.6%; P < 0.05 for all). In multivariable Cox regression analyses, African Caribbean ethnicity (hazard ratio [HR] 1.39, 95% CI 1.09–1.78, P = 0.009), baseline SBP (HR 1.01, 95% CI 1.00–1.01, P = 0.033), and baseline HbA1c (HR 1.01, 95% CI 1.00–1.01, P = 0.0001) emerged as independent risk factors for STDR. CONCLUSIONS We observed that people with type 1 diabetes of African Caribbean ethnicity are at significantly greater risk of STDR. Further research is required to understand the mechanisms that explain this novel observation.
Study of Punctured Binary Sequence Pair with Two-Level Autocorrelation

Taking advantages of punctured binary sequence pair and two-level autocorrelation,a new periodic correlation signal called punctured binary sequence pair with two-level autocorrelation is proposed.For the purpose of effective searching,the combined admissibility conditions of punctured binary sequence pair with two-level autocorrelation are discussed.And some punctured binary sequence pairs with two-level autocorrelation are given.