Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic optimal control problems with delays in the state and in the control via viscosity solutions and an economical application

In this manuscript we consider optimal control problems of deterministic and stochastic differential equations with delays in the state and in the control. First we prove an equivalent Markovian reformulation on Hilbert spaces of the state equation. Then, using the dynamic programming approach for infinite-dimensional systems, we prove that the value function is the unique viscosity solution of the infinite-dimensional Hamilton Jacobi Bellman equation. Finally we apply this result to a stochastic optimal advertising problem with delays in the state and in the control.
International Journal of Affective Engineering

This study examines the assumptions, perceptions, and attitudes towards contemporary robots in different cultures. As culture affects the way technology is perceived, it is essential to explore the psychological reactions through different cultures towards different designs of robots. Only a few studies have examined attitudes towards robots or dealt with the assumptions in a specific culture. The present study determines the influence of crucial factors like culture, prior exposure to robots through the media, and personal experiences of the emotions towards robots and examines the cultural differences between Europe and Japan.
International Journal of Affective Engineering

Media contents are naturally used in our daily life, and cross-modal effects of them are believed. The main example of the cross-modal effects investigated in previous studies was a combination of sight and hearing. This study aims to fundamentally investigate the cross-modal effects of music and scent on subjective feelings and preference. Especially, the main effect of scent is focused on, because it has not been observed. Two music pieces and two scents were employed and combined as four cross-modal stimuli in experiment. 29 subjects participated in the experiment and answered impressions and preference for the combined music piece and scent. Two-way ANOVA showed main effect of scent in one item and main effect of music in three items. Significant Interaction was also observed. These results showed that the main effect of scent exists in the cross-modal stimuli, however, it seems to be weaker than the effect of music pieces.
ESTIMATION OF SATURATED HYDRAULIC CONDUCTIVITY ON THE BASIS OF DRAINAGE POROSITY

The paper presents relations between drainage porosity (n d ) and the saturated hydraulic conductivity (K s ). The undisturbed soil samples were characterised by the saturated hydraulic conductivity, water retention curve (WRC) and estimated sum of the soil macro and mesopores known as the drainage porosity. The latter sum was estimated from the pF curves as a difference between the total porosity ( t ) and the field capacity (FC). Two equations describing these relationships were proposed and the coefficients of the power function of Ahuja et al. [1, 2] were modified. The equations proposed were tested on the original data set and on the independent selected UNSODA data set [23] and compared with other previously published pedotransfer functions (PTFs). The results of the comparative analysis have shown that for 0.03 < d <0.30 m 3 × m -3 , the equation proposed in this paper provides a more accurate assessment of the saturated hydraulic conductivity. It has been also shown that the relation derived is universal and does not depend on the soil texture.
Human immunodeficiency virus (HIV) risk, prevention, and testing behaviors--United States, National HIV Behavioral Surveillance System: men who have sex with men, November 2003-April 2005.

PROBLEM/CONDITION For CDC's goal of reducing the number of new human immunodeficiency virus (HIV) infections to be achieved, data are needed to assess the prevalence of HIV-related risk behaviors at a given time, monitor trends in these behaviors, and assess the correlates of risk. These data also can be used to evaluate the extent to which current HIV-prevention programs are reaching targeted communities and direct future HIV-prevention activities to reduce HIV transmission. REPORTING PERIOD November 2003-April 2005. DESCRIPTION OF SYSTEM The National HIV Behavioral Surveillance (NHBS) System collects risk behavior data from three populations at high risk for HIV infection: men who have sex with men (MSM), injection-drug users, and heterosexual adults in areas in which HIV is prevalent. Data collection began in 2003 among MSM in 17 U.S. metropolitan statistical areas (MSAs), and surveys have been conducted in 25 MSAs since 2005. Participants must be aged >/= 18 years and reside in a participating MSA. RESULTS This report summarizes data gathered during the first cycle (i.e., data collection period) of NHBS (November 2003-April 2005) from approximately 10,000 MSM. The results indicated that >90% of participants had ever been tested for HIV. Of those, 77% had been tested during the preceding 12 months. In addition to their male sex partners, 14% of participants also had at least one female sex partner during the preceding 12 months. Unprotected anal intercourse was reported by 58% with a main male partner (someone with whom the participant had sex and to whom he felt most committed [e.g., a boyfriend, spouse, significant other, or life partner]) and by 36[corrected]% with a casual male partner (someone with whom the participant had sex but who was not considered a main partner). Noninjection drugs were used by 42% of participants during the preceding 12 months; the most commonly used drugs were marijuana (77%), cocaine (37%), ecstasy (29%), poppers (28%), and stimulants (27%). A substantial proportion (80%) of participants had received free condoms during the preceding 12 months, but fewer had participated in individual- or group-level HIV prevention programs (15% and 8%, respectively). INTERPRETATION MSM surveyed engaged in sexual and drug-use behaviors that placed them at increased risk for HIV infection. The majority of MSM surveyed had been tested for HIV infection. Although a substantial proportion of participants had received free condoms, a much smaller proportion had participated in more intensive HIV-prevention programs. PUBLIC HEALTH ACTION NHBS data are used to assess and develop effective HIV-prevention programs and services. Continued collection and reporting of NHBS data from all targeted high-risk populations is needed to monitor behavior trends and assess future HIV prevention needs in these populations. The data are used for local HIV-prevention planning and monitoring in MSAs in which NHBS is conducted.
Role of Inflammation in the Pathogenesis of Diabetic Retinopathy

Diabetic retinopathy (DR) remains a major cause of worldwide preventable blindness. The microvasculature of the retina responds to hyperglycemia through a number of biochemical changes, including activation of protein kinase C, increased advanced glycation end products formation, polyol pathway, and oxidative stress, and activation of the renin angiotensin system (RAS). There is an accumulating body of evidence that inflammation plays a prominent role in the pathogenesis of DR.
Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs

Validating the safety of Autonomous Vehicles (AV s) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for which there is not representative training data. By increasing the coverage of different road and traffic conditions and by including corner cases in simulation-based scenario testing, the safety of AVs can be improved. However, the creation of corner case scenarios including multiple agents is non-trivial. Our approach allows engineers to generate novel, realistic corner cases based on historic traffic data and to explain why situations were safety -critical. In this paper, we introduce Probabilistic Lane Graphs (PLGs) to describe a finite set of lane positions and directions in which vehicles might travel. The structure of PLGs is learnt directly from spatio-temporal traffic data. The graph model represents the actions of the drivers in response to a given state in the form of a probabilistic policy. We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AV s.
Fiber optic computing using distributed feedback

The widespread adoption of machine learning and other matrix intensive computing algorithms has renewed interest in analog optical computing, which has the potential to perform large-scale matrix multiplications with superior energy scaling and lower latency than digital electronics. However, most optical techniques rely on spatial multiplexing, requiring a large number of modulators and detectors, and are typically restricted to performing a single kernel convolution operation per layer. Here, we introduce a fiber-optic computing architecture based on temporal multiplexing and distributed feedback that performs multiple convolutions on the input data in a single layer. Using Rayleigh backscattering in standard single mode fiber, we show that this technique can efficiently apply a series of random nonlinear projections to the input data, facilitating a variety of computing tasks. The approach enables efficient energy scaling with orders of magnitude lower power consumption than GPUs, while maintaining low latency and high data-throughput. Optical techniques adopted in optical computing rely on spatial multiplexing, requiring numerous integrated elements and restricting the architecture to perform a single kernel convolution per layer. The authors demonstrate a fiber-optic computing architecture based on temporal multiplexing that performs multiple convolutions in a single layer.
University of Birmingham High resolution magic angle spinning 1H NMR of childhood brain and nervous system tumours

Background: Brain and nervous system tumours are the most common solid cancers in children. Molecular characterisation of these tumours is important for providing novel biomarkers of disease and identifying molecular pathways which may provide putative targets for new therapies. 1H magic angle spinning NMR spectroscopy (1H HR-MAS) is a powerful tool for determining metabolite profiles from small pieces of intact tissue and could potentially provide important molecular information. Methods: Forty tissue samples from 29 children with glial and primitive neuro-ectodermal tumours were analysed using HR-MAS (600 MHz Varian gHX nanoprobe). Tumour spectra were fitted to a library of individual metabolite spectra to provide metabolite values. These values were then used in a two tailed t-test and multi-variate analysis employing a principal component analysis and a linear discriminant analysis. Classification accuracy was estimated using a leave-one-out analysis and B632+ bootstrapping. Results: Glial tumours had significantly (two tailed t-test p < 0.05) higher creatine and glutamine and lower taurine, phosphoethanolamine, phosphorylcholine and choline compared with primitive neuro-ectodermal tumours. Classification accuracy was 90%. Medulloblastomas (n = 9) had significantly (two tailed t-test p < 0.05) higher creatine, glutamine, phosphorylcholine, glycine and scyllo-inositol than neuroblastomas (n = 7), classification accuracy was 94%. Supratentorial primitive neuro-ectodermal tumours had metabolite profiles in keeping with other primitive neuro-ectodermal tumours whilst ependymomas (n = 2) had metabolite profiles intermediate between pilocytic astrocytomas (n = 10) and primitive neuro-ectodermal tumours. Conclusion: HR-MAS identified key differences in the metabolite profiles of childhood brain and nervous system improving the molecular characterisation of these tumours. Further investigation of the underlying molecular pathways is required to assess their potential as targets for new agents.
1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation

Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. In this report, we present further improvements to the SOTA VIS method, DVIS. First, we introduce a denoising training strategy for the trainable tracker, allowing it to achieve more stable and accurate object tracking in complex and long videos. Additionally, we explore the role of visual foundation models in video instance segmentation. By utilizing a frozen VIT-L model pre-trained by DINO v2, DVIS demonstrates remarkable performance improvements. With these enhancements, our method achieves 57.9 AP and 56.0 AP in the development and test phases, respectively, and ultimately ranked 1st in the VIS track of the 5th LSVOS Challenge. The code will be available at https://github.com/zhang-tao-whu/DVIS.
Fast Feedforward Networks

We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks. We demonstrate that FFFs are up to 220x faster than feedforward networks, up to 6x faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution. Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance.
Complications after endovascular stent-grafting of thoracic aortic diseases

Background: To update our experience with thoracic aortic stent-graft treatment over a 5-year period, with special consideration for the occurrence and management of complications. Methods: From December 2000 to June 2006, 52 patients with thoracic aortic pathologies underwent endovascular repair; there were 43 males (83%) and 9 females, mean age 63 ± 19 years (range 17–87). Fourteen patients (27%) were treated for degenerative thoracic aortic aneurysm, 12 patients (24%) for penetrating aortic ulcer, 8 patients (15%) for blunt traumatic injury, 7 patients (13%) for acute type B dissection, 6 patients (11%) for a type B dissecting aneurysm; 5 patients (10%) with thoraco-abdominal aortic aneurysms were excluded from the analyses. Fifteen patients (32%) underwent emergency treatment. Overall, mean EuroSCORE was 9 ± 3 (median 15, range 3–19). All procedures were performed in the theatre under general anesthesia. All complications occurring during hospitalisation were recorded. Follow-up protocol featured CT-A, and chest Xrays 1, 4 and 12 months after intervention, and annually thereafter. Results: Primary technical success was achieved in all patients; procedures never aborted because of access difficulty. Conversion to standard open repair was never required. Mean duration of the procedure was 119 ± 75 minutes (median 90, range 45–285). Mean blood loss was 254 mL (range 50–1200 mL). The mean length of the aorta covered by the SGs was 192 ± 21 mm (range 100– 360). The LSA was over-stented in 17 cases (17/47, 36%). Overall 30-day operative mortality was 6.4% (3/47). Major complications included pneumonia (n = 9), cerebrovascular accidents (n = 4), arrhythmia (n = 4), acute renal failure (n = 3), and colic ischemia (n = 1). Overall, endoleak rate was 14%. Conclusion: Although this report is a retrospective and not comparative analysis of thoracic aortic repair, the combined minor and major morbidity rate was lower than previous reported to results of either electively and emergency performed conventional repair. Background Endovascular treatment for aortic disease has emerged as an alternative mode of treatment for thoracic aortic diseases (TADs) that is particularly attractive for patients with Published: 12 September 2006 Journal of Cardiothoracic Surgery 2006, 1:26 doi:10.1186/1749-8090-1-26 Received: 09 August 2006 Accepted: 12 September 2006 This article is available from: http://www.cardiothoracicsurgery. org/content/1/1/26 © 2006 Piffaretti et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Rate-Optimal Policy Optimization for Linear Markov Decision Processes

We study regret minimization in online episodic linear Markov Decision Processes, and obtain rate-optimal $\widetilde O (\sqrt K)$ regret where $K$ denotes the number of episodes. Our work is the first to establish the optimal (w.r.t.~$K$) rate of convergence in the stochastic setting with bandit feedback using a policy optimization based approach, and the first to establish the optimal (w.r.t.~$K$) rate in the adversarial setup with full information feedback, for which no algorithm with an optimal rate guarantee is currently known.
FwdLLM: Efficient FedLLM using Forward Gradient

Large Language Models (LLMs) are transforming the landscape of mobile intelligence. Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM. Though recent efforts have addressed the network issue induced by the vast model size, they have not practically mitigated vital challenges concerning integration with mobile devices, such as significant memory consumption and sluggish model convergence. In response to these challenges, this work introduces FwdLLM, an innovative FL protocol designed to enhance the FedLLM efficiency. The key idea of FwdLLM to employ backpropagation (BP)-free training methods, requiring devices only to execute ``perturbed inferences''. Consequently, FwdLLM delivers way better memory efficiency and time efficiency (expedited by mobile NPUs and an expanded array of participant devices). FwdLLM centers around three key designs: (1) it combines BP-free training with parameter-efficient training methods, an essential way to scale the approach to the LLM era; (2) it systematically and adaptively allocates computational loads across devices, striking a careful balance between convergence speed and accuracy; (3) it discriminatively samples perturbed predictions that are more valuable to model convergence. Comprehensive experiments with five LLMs and three NLP tasks illustrate FwdLLM's significant advantages over conventional methods, including up to three orders of magnitude faster convergence and a 14.6x reduction in memory footprint. Uniquely, FwdLLM paves the way for federated learning of billion-parameter LLMs such as LLaMA on COTS mobile devices -- a feat previously unattained.
Edinburgh Research Explorer Maintaining Binding in Working Memory: Comparing the Effects of Intentional Goals and Incidental Affordances

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Perceived Stigma and Associated Factors Among People Living with HIV Attending ART Clinics in Public Health Facilities of Dessie City, Ethiopia

Introduction: Despite the fact that responses to epidemics of human immune-deficiency virus (HIV) have gradually improved; it remains one of the world’s public health challenges, particularly in low- and middle-income countries. Evidence is still limited in this area and this study aimed to assess the magnitude of perceived stigma and associated factors among people living with HIV (PLWH) in public health facilities of Dessie city, Ethiopia. Methods: A health facility-based cross-sectional study was conducted among a total of 422 PLWH from September 1 to October 30, 2019 in Dessie city. A pretested structured interviewer-administered questionnaire was used to collect the data and it was analyzed using Stata/SE 14. Multivariable binary logistic regression analysis was used to identify factors associated with perceived stigma. The adjusted odds ratio (AOR) along with 95% confidence interval (CI) was estimated to measure the strength and direction of the association. Statistical significance was declared at P value less than 0.05. Results: The overall magnitude of perceived stigma among people living with HIV in public health facilities of Dessie city was 41.93% [95% CI: (37.25, 46.75)]. Perceived stigma was positively associated with being female [AOR=2.08, 95% CI: (1.26, 3.46)], living in rural areas [AOR=1.80, 95% CI: (1.10, 2.94)] and not disclosing HIV status [AOR=2.36, 95% CI: (1.19, 4.66)]. Conclusion: In this study, the magnitude of perceived stigma was high as compared to UNAIDS standard, and sex, place of residence, and disclosure of HIV serostatus were significantly associated with perceived stigma. The findings suggested that special emphasis should be placed on rural residents in order to reduce perceived stigma. Moreover, counseling should focus on disclosing HIV serostatus to anyone else interested to disclose particularly for females.
Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests

This paper underscores the vital role of the chi-square test within political science research utilizing structural equation modeling (SEM). The ongoing debate regarding the inclusion of chi-square test statistics alongside fit indices in result presentations has sparked controversy. Despite the recognized limitations of relying solely on the chi-square test, its judicious application can enhance its effectiveness in evaluating model fit and specification. To exemplify this, we present three common scenarios pertinent to political science research where fit indices may inadequately address goodness-of-fit concerns, while the chi-square statistic can be effectively harnessed. Through Monte Carlo simulations, we examine strategies for enhancing chi-square tests within these scenarios, showcasing the potential of appropriately employed chi-square tests to provide a comprehensive model fit assessment. Our recommendation is to report both the chi-square test and fit indices, with a priority on precise model specification to ensure the trustworthiness of model fit indicators.
New Bounds on Fuzzy Dark Matter from Galaxy-Galaxy Strong-Lensing Observations

Fuzzy Dark Matter (FDM) has recently gained attention as a motivated candidate for the dark matter (DM) content of the Universe, as opposed to the commonly assumed cold DM (CDM), since the soliton profile intrinsic to FDM models was found to be particularly well suited to reproduce observed galaxy mass profiles. While FDM as a single DM component has been strongly constrained by multiple probes, there remained a mass window between $10^{-25}\,\mathrm{eV}$ and $10^{-24}\,\mathrm{eV}$ in which it can comprise a large portion ($\gtrsim\mathcal{O}(10\%)$) of the total DM. In this work, we consider gravitational lensing measurements in the strong lensing regime, which are one of the only means to directly constrain the distribution and profile of DM in astronomical bodies. Using a simple model that combines a soliton FDM component with a Navarro-Frenk-White (NFW) profile, we explore under what conditions DM halos with this hybrid profile are able to reproduce the observed Einstein radii of several known lenses. We find that FDM with a particle mass of $\lesssim 10^{-24}\,{\rm eV}$ cannot explain the observations if it makes up more than $\sim10\%$ of the total DM, effectively closing the lingering FDM mass window.