Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective Memory Complaints Predict Decline in Memory, Instrumental Activities of Daily Living, and Social Participation in Older Adults: A Fixed-Effects Model

Abstract Importance: Although subjective memory complaints (SMCs) have been suggested to be associated with future memory impairment, limitations in instrumental activities of daily living (IADLs), and social participation restriction, these associations are still inconclusive. Objective: To determine whether changes in SMCs over time predict decline in memory, IADLs, and social participation in older adults. Design: Longitudinal study. Setting: Community. Participants: Sample 1 included 2,493 community-dwelling older adults drawn from the Health and Retirement Study (HRS) data collected between 2004 and 2018. Sample 2 included 1,644 community-dwelling older adults drawn from the HRS data collected between 2008 and 2018. Outcomes and Measures: Self-reported SMCs, memory function, self-reported IADL performance, and self-reported social participation. Results: The mean age of Sample 1 at baseline was 70.16 yr; 1,468 (58.88%) were female. In Sample 1, immediate and delayed memory (all ps < .001) and IADL performance (p < .01) declined over time. Increases in SMCs over time significantly predicted future immediate and delayed memory declines (p < .01 and p < .001, respectively) and future IADL performance decline (p < .001), after controlling for depressive symptoms. The mean age of Sample 2 at baseline was 71.52 yr; 928 (56.45%) were female. In Sample 2, social participation declined over time (all ps < .001). Increases in SMCs over time significantly predicted future social participation decline (p < .05), after controlling for depressive symptoms. Conclusions and Relevance: Increases in SMCs predict future decline in memory, IADL performance, and social participation after accounting for depressive symptoms. What This Article Adds: SMCs can be used as an early indicator of future memory impairment, IADL limitations, and social participation restrictions in older adults. Furthermore, interventions that minimize SMCs may help older adults achieve successful aging.
A high-frequency SSVEP-BCI system based on a 360 Hz refresh rate

Objective. Steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55–62.8 Hz frequency range to implement a 40-target BCI speller that offered both high-performance and user-friendliness. Approach. This study proposed a method that presents stable multi-target stimuli on a monitor with a 360 Hz refresh rate. Real-time generation of stimulus matrix and stimulus rendering was used to ensure stable presentation while reducing the computational load. The 40 targets were encoded using the joint frequency and phase modulation method, offline and online BCI experiments were conducted on 16 subjects using the task discriminant component analysis algorithm for feature extraction and classification. Main results. The online BCI system achieved an average accuracy of 88.87% ± 3.05% and an information transfer rate of 51.83 ± 2.77 bits min−1 under the low flickering perception condition. Significance. These findings suggest the feasibility and significant practical value of the proposed high-frequency SSVEP BCI system in advancing the visual BCI technology.
Open Set Synthetic Image Source Attribution

AI-generated images have become increasingly realistic and have garnered significant public attention. While synthetic images are intriguing due to their realism, they also pose an important misinformation threat. To address this new threat, researchers have developed multiple algorithms to detect synthetic images and identify their source generators. However, most existing source attribution techniques are designed to operate in a closed-set scenario, i.e. they can only be used to discriminate between known image generators. By contrast, new image-generation techniques are rapidly emerging. To contend with this, there is a great need for open-set source attribution techniques that can identify when synthetic images have originated from new, unseen generators. To address this problem, we propose a new metric learning-based approach. Our technique works by learning transferrable embeddings capable of discriminating between generators, even when they are not seen during training. An image is first assigned to a candidate generator, then is accepted or rejected based on its distance in the embedding space from known generators' learned reference points. Importantly, we identify that initializing our source attribution embedding network by pretraining it on image camera identification can improve our embeddings' transferability. Through a series of experiments, we demonstrate our approach's ability to attribute the source of synthetic images in open-set scenarios.
GSA to HDL: Towards principled generation of dynamically scheduled circuits

High-level synthesis (HLS) refers to the automatic translation of a software program written in a high-level language into a hardware design. Modern HLS tools have moved away from the traditional approach of static (compile time) scheduling of operations to generating dynamic circuits that schedule operations at run time. Such circuits trade-off area utilisation for increased dynamism and throughput. However, existing lowering flows in dynamically scheduled HLS tools rely on conservative assumptions on their input program due to both the intermediate representations (IR) utilised as well as the lack of formal specifications on the translation into hardware. These assumptions cause suboptimal hardware performance. In this work, we lift these assumptions by proposing a new and efficient abstraction for hardware mapping; namely h-GSA, an extension of the Gated Single Static Assignment (GSA) IR. Using this abstraction, we propose a lowering flow that transforms GSA into h-GSA and maps h-GSA into dynamically scheduled hardware circuits. We compare the schedules generated by our approach to those by the state-of-the-art dynamic-scheduling HLS tool, Dynamatic, and illustrate the potential performance improvement from hardware mapping using the proposed abstraction.
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequently, the dominance in this domain has shifted towards Transformer, MLP, and CNN approaches. The substantial number of recurrent iterations are the fundamental reasons behind the limitations of RNNs in LTSF. To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF). RNNs that combine these strategies, namely SegRNN, significantly reduce the required recurrent iterations for LTSF, resulting in notable improvements in forecast accuracy and inference speed. Extensive experiments demonstrate that SegRNN not only outperforms SOTA Transformer-based models but also reduces runtime and memory usage by more than 78%. These achievements provide strong evidence that RNNs continue to excel in LTSF tasks and encourage further exploration of this domain with more RNN-based approaches. The source code is coming soon.
Deadlock-free, Safe, and Decentralized Multi-Robot Navigation in Social Mini-Games via Discrete-Time Control Barrier Functions

We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed that ensure safety and resolve deadlocks, optimally preventing deadlocks in a minimally invasive and decentralized fashion remains an open problem. We first formalize the objective as a non-cooperative, non-communicative, partially observable multi-robot navigation problem in constrained spaces with multiple conflicting agents, which we term as social mini-games. Formally, we solve a discrete-time optimal receding horizon control problem leveraging control barrier functions for safe long-horizon planning. Our approach to ensuring liveness rests on the insight that \textit{there exists barrier certificates that allow each robot to preemptively perturb their state in a minimally-invasive fashion onto liveness sets i.e. states where robots are deadlock-free}. We evaluate our approach in simulation as well on physical robots using F$1/10$ robots, a Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, hallway, and corridor intersection scenario. Compared to both fully decentralized and centralized approaches with and without deadlock resolution capabilities, we demonstrate that our approach results in safer, more efficient, and smoother navigation, based on a comprehensive set of metrics including success rate, collision rate, stop time, change in velocity, path deviation, time-to-goal, and flow rate.
Careful at Estimation and Bold at Exploration

Exploration strategies in continuous action space are often heuristic due to the infinite actions, and these kinds of methods cannot derive a general conclusion. In prior work, it has been shown that policy-based exploration is beneficial for continuous action space in deterministic policy reinforcement learning(DPRL). However, policy-based exploration in DPRL has two prominent issues: aimless exploration and policy divergence, and the policy gradient for exploration is only sometimes helpful due to inaccurate estimation. Based on the double-Q function framework, we introduce a novel exploration strategy to mitigate these issues, separate from the policy gradient. We first propose the greedy Q softmax update schema for Q value update. The expected Q value is derived by weighted summing the conservative Q value over actions, and the weight is the corresponding greedy Q value. Greedy Q takes the maximum value of the two Q functions, and conservative Q takes the minimum value of the two different Q functions. For practicality, this theoretical basis is then extended to allow us to combine action exploration with the Q value update, except for the premise that we have a surrogate policy that behaves like this exploration policy. In practice, we construct such an exploration policy with a few sampled actions, and to meet the premise, we learn such a surrogate policy by minimizing the KL divergence between the target policy and the exploration policy constructed by the conservative Q. We evaluate our method on the Mujoco benchmark and demonstrate superior performance compared to previous state-of-the-art methods across various environments, particularly in the most complex Humanoid environment.
Multi-Objective Improvement of Android Applications

Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. Previous work on automated improvement of non-functional properties in mobile apps failed to address the inherent trade-offs between such properties. We propose a practical approach and the first open-source tool, GIDroid (2023), for multi-objective automated improvement of Android apps. In particular, we use Genetic improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.
A Semi-Automatic Oriental Ink Painting Framework for Robotic Drawing From 3D Models

Creating visually pleasing stylized ink paintings from 3D models is a challenge in robotic manipulation. We propose a semi-automatic framework that can extract expressive strokes from 3D models and draw them in oriental ink painting styles by using a robotic arm. The framework consists of a simulation stage and a robotic drawing stage. In the simulation stage, geometrical contours were automatically extracted from a certain viewpoint and a neural network was employed to create simplified contours. Then, expressive digital strokes were generated after interactive editing according to user's aesthetic understanding. In the robotic drawing stage, an optimization method was presented for drawing smooth and physically consistent strokes to the digital strokes, and two oriental ink painting styles termed as Noutan (shade) and Kasure (scratchiness) were applied to the strokes by robotic control of a brush's translation, dipping and scraping. Unlike existing methods that concentrate on generating paintings from 2D images, our framework has the advantage of rendering stylized ink paintings from 3D models by using a consumer-grade robotic arm. We evaluate the proposed framework by taking three standard models and a user-defined model as examples. The results show that our framework is able to draw visually pleasing oriental ink paintings with expressive strokes.
Learning the solution operator of a nonlinear parabolic equation using physics informed deep operator network

This study focuses on addressing the challenges of solving analytically intractable differential equations that arise in scientific and engineering fields such as Hamilton-Jacobi-Bellman. Traditional numerical methods and neural network approaches for solving such equations often require independent simulation or retraining when the underlying parameters change. To overcome this, this study employs a physics-informed DeepONet (PI-DeepONet) to approximate the solution operator of a nonlinear parabolic equation. PI-DeepONet integrates known physics into a deep neural network, which learns the solution of the PDE.
Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation

The quality of a wood log in the wood industry depends heavily on the presence of both outer and inner defects, including inner knots that are a result of the growth of tree branches. Today, locating the inner knots require the use of expensive equipment such as X-ray scanners. In this paper, we address the task of predicting the location of inner defects from the outer shape of the logs. The dataset is built by extracting both the contours and the knots with X-ray measurements. We propose to solve this binary segmentation task by leveraging convolutional recurrent neural networks. Once the neural network is trained, inference can be performed from the outer shape measured with cheap devices such as laser profilers. We demonstrate the effectiveness of our approach on fir and spruce tree species and perform ablation on the recurrence to demonstrate its importance.
Primordial Black Hole Merger Rate in f(R) Gravity

Primordial black holes (PBHs) are known as one of the potential candidates for dark matter. They are expected to have formed due to the direct gravitational collapse of density fluctuations in the early Universe. In this regard, examining the merger rate of PBHs within modified theories of gravity can offer a deeper insight into their abundance. In this work, we delve into the calculation of the merger rate of PBHs within the theoretical framework of f(R) gravity. Our analysis reveals an enhancement in the merger rate of PBHs compared to that obtained from general relativity. Additionally, modulating the field strength f R0 induces shifts in the PBH merger rate, presenting a potential observational signature of modified gravity. We also explore the upper bounds on the abundance of PBHs obtained from f(R) gravity models by comparing the results with gravitational-wave and observational data. The results indicate that in certain regions not excluded by benchmarking data, the parameter space for these upper bounds may be considered reliable.
Automatic Task Parallelization of Dataflow Graphs in ML/DL models

Several methods exist today to accelerate Machine Learning(ML) or Deep-Learning(DL) model performance for training and inference. However, modern techniques that rely on various graph and operator parallelism methodologies rely on search space optimizations which are costly in terms of power and hardware usage. Especially in the case of inference, when the batch size is 1 and execution is on CPUs or for power-constrained edge devices, current techniques can become costly, complicated or inapplicable. To ameliorate this, we present a Critical-Path-based Linear Clustering approach to exploit inherent parallel paths in ML dataflow graphs. Our task parallelization approach further optimizes the structure of graphs via cloning and prunes them via constant propagation and dead-code elimination. Contrary to other work, we generate readable and executable parallel Pytorch+Python code from input ML models in ONNX format via a new tool that we have built called {\bf Ramiel}. This allows us to benefit from other downstream acceleration techniques like intra-op parallelism and potentially pipeline parallelism. Our preliminary results on several ML graphs demonstrate up to 1.9$\times$ speedup over serial execution and outperform some of the current mechanisms in both compile and runtimes. Lastly, our methods are lightweight and fast enough so that they can be used effectively for power and resource-constrained devices, while still enabling downstream optimizations.
MORPHOLOGICAL STUDY OF ACHILLES TENDON

Address for Correspondence: Dr. Shilpa Gosavi, Professor, Department of Anatomy, Bharati Vidyapeeth (Deemed to be university) Medical College, Pune, India.Mob. 9822218073, E-Mail: sngosavi@yahoo.com Background: The calcaneal tendon is the thickest and strongest tendon in the human body which is attached to the midpoint of the posterior surface of calcaneum. It is a frequent site of rupture, degenerative changes and inflammation. Aim: To study the anatomical and morphological details of attachment of Achilles tendon. Materials and Methods: The study was carried out on 70 adult cadaveric lower limbs in two Medical colleges. Various linear measurements were taken with the help of digital vernier caliper, accurate up to 0.01 mm. Results: Presence of Achilles tendon in all cadaveric limbs was observed in the present study. Bursa between the tendon at insertion and calcaneus was seen in all specimens. Length of tendon of gastrocnemius was observed to vary between 16.5 cm to 29.5 cm. The width at musculotendinous junction with gastrocnemius was observed to vary between 3.42 cm to 6.84 cm. Other measurements taken were minimum width, thickness at insertion, length of gastrocnemius aponeurosis medially and laterally. Conclusion: In the present study, variations in the morphology of Gastrocnemius were observed. Understanding these variations of the gastrocnemius tendon and aponeurosis will aid the surgeon in performing surgical procedures such as pedicle tendon flap for reconstructive surgery and arthroplasty, Endoscopic Gastrocnemius Recession (EGR), on the Achilles tendon and prevent iatrogenic complications. It is also useful to the Anatomists as it will help them integrate in their teaching the variations and thus better prepare the students for their clinical tenures/work/future.
Hybrid sample size calculations for cluster randomised trials using assurance

Sample size determination for cluster randomised trials (CRTs) is challenging as it requires robust estimation of the intra-cluster correlation coefficient (ICC). Typically, the sample size is chosen to provide a certain level of power to reject the null hypothesis in a hypothesis test. This relies on the minimal clinically important difference (MCID) and estimates for the standard deviation, ICC and possibly the coefficient of variation of the cluster size. Varying these parameters can have a strong effect on the sample size. In particular, it is sensitive to small differences in the ICC. A relevant ICC estimate is often not available, or the available estimate is imprecise. If the ICC used is far from the unknown true value, this can lead to trials which are substantially over- or under-powered. We propose a hybrid approach using Bayesian assurance to find the sample size for a CRT with a frequentist analysis. Assurance is an alternative to power which incorporates uncertainty on parameters through a prior distribution. We suggest specifying prior distributions for the standard deviation, ICC and coefficient of variation of the cluster size, while still utilising the MCID. We illustrate the approach through the design of a CRT in post-stroke incontinence. We show assurance can be used to find a sample size based on an elicited prior distribution for the ICC, when a power calculation discards all information in the prior except a single point estimate. Results show that this approach can avoid misspecifying sample sizes when prior medians for the ICC are very similar but prior distributions exhibit quite different behaviour. Assurance provides an understanding of the probability of success of a trial given an MCID and can be used to produce sample sizes which are robust to parameter uncertainty. This is especially useful when there is difficulty obtaining reliable parameter estimates.
Koncepcja zbioru ogolnych zasad planowania turystyki zrownowazonej na obszarach chronionych

Streszczenie. Celem jest przedstawienie koncepcji zbioru dziesięciu ogólnych zasad planowania funkcji turystyki zrównoważonej na przyrodniczych obszarach chronionych i innych terenach o dużej wartości krajobrazu. Koncepcja ta odzwierciedla poglądy Autorki na sposób udostępnienia obszarów chronionych do celów turystycznych. Zasady te mają charakter strategiczny i dotyczą programu, monitoringu i sterowania ruchem turystycznym zarówno w odniesieniu do pojedynczego obszaru chronionego, jak i zespołu tych obszarów w regionie. Stosowane łącznie, powinny umożliwić osiągnięcie równowagi pomiędzy użytkowaniem turystycznym a zachowaniem walorów przyrodniczo-krajobrazowych. Turystyka jest bardzo ważną funkcją obszarów chronionych, dlatego istnieje potrzeba rozwijania jej na tych terenach. Zaproponowano pojęcie „maksymalnej ekologicznej turystyki” (maximum ecological carrying tourism), która oznacza możliwie szerokie udostępnienie turystyczne terenu i maksymalizację zysków z turystyki z jednoczesnym zachowaniem jakości, ilości i układu przestrzennego walorów krajobrazu przyrodniczego.