Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of hand preference in cognition and neuropsychiatric symptoms in neurodegenerative diseases

Abstract Handedness has been shown to be associated with genetic variation involving brain development and neuropsychiatric diseases. Whether handedness plays a role in clinical phenotypes of common neurodegenerative diseases has not been extensively studied. This study used the National Alzheimer’s Coordinating Center database to examine whether self-reported handedness was associated with neuropsychological performance and neuropsychiatric symptoms in cognitively unimpaired individuals (n = 17 670), individuals with Alzheimer’s disease (n = 10 709), behavioural variant frontotemporal dementia (n = 1132) or dementia with Lewy bodies (n = 637). Of the sample, 8% were left-handed, and 2% were ambidextrous. There were small differences in the handedness distributions across the cognitively unimpaired, Alzheimer’s disease, behavioural variant frontotemporal dementia and dementia with Lewy bodies groups (7.2–9.5% left-handed and 0.9–2.2% ambidextrous). After adjusting for age, gender and education, we found faster performance in Trail Making Test A in cognitively unimpaired non-right-handers (ambidextrous and left-handed) compared with right-handers. Excluding ambidextrous individuals, the left-handed cognitively unimpaired individuals had faster Trail Making Test A performance and better Number Span Forward performance than right-handers. Overall, handedness had no effects on most neuropsychological tests and none on neuropsychiatric symptoms. Handedness effect on Trail Making Test A in the cognitively unimpaired is likely to stem from test artefacts rather than a robust difference in cognitive performance. In conclusion, handedness does not appear to affect neuropsychological performance or neuropsychiatric symptoms in common neurodegenerative diseases.
Getting off the racist sidelines: an antiracist approach to mental health supervision and training

ABSTRACT Few codified strategies exist for antiracist supervision practices that prepare mental health providers to challenge racism during clinical care. In this paper, the author articulates an antiracist clinical supervision approach comprised of six action steps for identifying the racism and Whiteness shaping clinical care and supervision and dismantling them: (a) recognizing the historical legacy of racism to avoid perpetuating it, (b) identifying racism as the enduring public health crisis of our time, (c) admitting to being racist in order to engage in antiracism, (d) heightening critical consciousness, (e) cultivating foundational knowledge in racism, and (f) dismantling racism during supervision.
Crack identification using smart paint and machine learning

Information on the presence and location of cracks in civil structures can be precious to support operators in making decisions related to structural management and scheduling informed maintenance. This paper investigates the efficacy of supervised machine learning to solve the inverse electrical impedance tomography problem and to reconstruct the conductivity distribution of a piezoresistive sensing film. This film consists of a conductive paint applied onto structural components, and operators can use its conductivity distribution to identify crack sizes and locations in the underlying structure. A deep neural network is employed to reconstruct a dense conductivity distribution within the painted area by using only voltage measurements collected at sparse boundary locations. Since one of the most challenging aspects of using supervised learning tools for real-world applications is generating a representative training dataset, this paper presents a new approach to test the suitability of synthetic datasets built using a finite element model of the sensing film. Results are reported for four sensing specimens fabricated with two different techniques (i.e., using carbon nanotubes and graphene nanosheets, respectively). Crack-like damage is induced to the substrate of the sensing film and identified using the proposed machine learning technique. Promising results are obtained as compared to conventional methods.
Updates on global epidemiology, risk and prognostic factors of gastric cancer

Gastric cancer (GC) is defined as the primary epithelial malignancy derived from the stomach, and it is a complicated and heterogeneous disease with multiple risk factors. Despite its overall declining trend of incidence and mortality in various countries over the past few decades, GC remains the fifth most common malignancy and the fourth leading cause of cancer-related death globally. Although the global burden of GC has shown a significant downward trend, it remains severe in certain areas, such as Asia. GC ranks third in incidence and mortality among all cancer types in China, and it accounts for nearly 44.0% and 48.6% of new GC cases and GC-related deaths in the world, respectively. The regional differences in GC incidence and mortality are obvious, and annual new cases and deaths are increasing rapidly in some developing regions. Therefore, early preventive and screening strategies for GC are urgently needed. The clinical efficacies of conventional treatments for GC are limited, and the developing understanding of GC pathogenesis has increased the demand for new therapeutic regimens, including immune checkpoint inhibitors, cell immunotherapy and cancer vaccines. The present review describes the epidemiology of GC worldwide, especially in China, summarizes its risk and prognostic factors, and focuses on novel immunotherapies to develop therapeutic strategies for the management of GC patients.
System Design of 3D LiDAR-based Underground Integrated Pipeline Corridor Inspection Robot for Positioning and Map Building

Urban underground integrated pipe corridor is used for the water, electricity, gas, communications, and other pipelines into the underground space to effectively alleviate road traffic congestion, urban construction infrastructure, and other problems. After the completion of the corridor, the corridor inspection is a huge problem, as the underground space is complex and difficult to locate. For the need to detect the concentration of toxic and harmful gases inside the corridor, it is difficult to ensure the safety of the lives of the underground staff. With the development of artificial intelligence and digital twin, the corridor inspection robot gradually becomes the “perfect candidate” for inspection work. Thus, we present a system design of 3D LIDAR-based robot positioning and map building underground where GPS is not available.
Transfer learning on structural brain age models to decode cognition in MS: a federated learning approach

Introduction: Classical deep learning research requires lots of centralised data. However, data sets are often stored at different clinical centers, and sharing sensitive patient data such as brain images is difficult. In this manuscript, we investigated the feasibility of federated learning, sending models to the data instead of the other way round, for research on brain magnetic resonant images of people with multiple sclerosis (MS). Methods: Using transfer learning on a previously published brain age model, we trained a model to decode performance on the symbol digit modalities test (SDMT) of patients with multiple sclerosis from structural T1 weighted MRI. Three international centers in Brussels, Greifswald and Prague participated in the project. In Brussels, one computer served as the server coordinating the FL project, while the other served as client for model training on local data (n=97). The other two clients were Greifswald (n=104) and Prague (n=100). Each FL round, the server sent a global model to the clients, where its fully connected layer was updated on the local data. After collecting the local models, the server applied a weighted average of two randomly picked clients, yielding a new global model. Results: After 22 federated learning rounds, the average validation loss across clients reached a minimum. The model appeared to have learned to assign SDMT values close to the mean with a mean absolute error of 9.04, 10.59 and 10.71 points between true and predicted SDMT on the test data sets of Brussels, Greifswald and Prague respectively. The overall test MAE across all clients was 10.13 points. Conclusion: Federated learning is feasible for machine learning research on brain MRI of persons with MS, setting the stage for larger transfer learning studies to investigate the utility of brain age latent representations in cogni- tive decoding tasks.
Forest conversion into pasture selects dung beetle traits at different biological scales depending on species pool composition

Abstract The conversion of forests into open areas has large effects on the diversity and structure of native communities. The intensity of these effects may vary between regions, depending on the existence of native species adapted to open habitats in the regional pool or the time since habitat change. We assess the differences in species richness and functional diversity of dung beetle communities (Coleoptera: Scarabaeinae) between native forests and novel pasturelands of the Atlantic Forest and the Cerrado, two biomes with contrasting histories of human occupation in Brazil. We conducted standardized surveys in seven forest fragments and adjacent pastures in each region and measured 14 traits in individuals collected in each type of habitat at each particular site. We calculated functional richness, functional evenness, functional divergence, and community‐weighted mean of traits for each area, and analyzed individual variation through nested variance decomposition and Trait Statistics. Communities were richer and more numerous at the Cerrado. We did not find any consistent relationship between functional diversity and forest conversion beyond the changes in species diversity. Although landscape changes were more recent at the Cerrado, the colonization of the new habitat by native species already adapted to open habitats lessens the functional loss in this biome. This indicates that habitat change's effects on trait diversity depend on the regional species pool rather than on time since land conversion. Forest conversion effects were primarily due to internal filtering. The effects of external filtering only appear at the intraspecific variance level, with contrasting differences between the Cerrado, where traits related to relocation behavior and size are selected, and the Atlantic Forest, where selection operates for traits related to relocation behavior and flight. These results evidence the importance of considering individual variance to address the responses of dung beetle communities to forest conversion.
Cost rate heuristics for semi-Markov decision processes

In response to the computational complexity of the dynamic programming/backwards induction approach to the development of optimal policies for semi-Markov decision processes, we propose a class of heuristics resulting from an inductive process which proceeds forwards in time. These heuristics always choose actions in such a way as to minimize some measure of the current cost rate. We describe a procedure for calculating such cost rate heuristics. The quality of the performance of such policies is related to the speed of evolution (in a cost sense) of the process. A simple model of preventive maintenance is described in detail. Cost rate heuristics for this problem are calculated and assessed computationally.
Late recurrence of chromophobe renal cell carcinoma to unusual sites after 12 years of radical nephrectomy and radiotherapy: a rare case report from Syria

Introduction and Importance: In 2020, renal cell carcinoma (RCC) had an incidence of 73 750 new cases. This cancer is well known for its ability to give early and late metastases to some usual and unusual sites. The term ‘late recurrence’ is widely used to indicate a period exceeding 10 years from curative nephrectomy. This not-understood behaviour is almost specific to RCC, and it happens in a range between 4.3 and 11% of cases of RCC. Case Presentation: We report a case of a 67-year-old nonalcoholic smoker Syrian male presented with a 2-month painful mass located at the left upper posterolateral abdominal wall’s region. He has had a history of left chromophobe cell RCC treated with radical nephrectomy with adjuvant radiotherapy for 12 years. In light of computed tomography’s findings, a surgical biopsy was performed, and a pathological and immunohistochemical examination confirmed the diagnosis of chromophobe RCC. Clinical Discussion: Malignant cells seeding the surgical cut path and staying dormant for 12 years is the best theory of many to explain our case. Conclusion: We reported evidence for the potential of a relatively indolent histologic type of RCC (i.e. chromophobe cell carcinoma) to cause late recurrence after 12 years to a very rare site (i.e. abdominal wall’s superficial muscles). Research should focus on addressing late recurrence to determine the best surveillance protocols; investigating malignant cells seeding during surgery to improve surgical oncology’s outcomes; and studying late recurrence’s genetics to boost our targeted therapy options.
Structure, dynamics and multiple length-scales in network-forming materials

Relationships between the structural and dynamical properties of network-forming materials are investigated. A generic model is utilised for systems of stoichiometry MX2 which are linked in the sense that they can all be usefully considered as constructed from linked MX4 tetrahedra. A single model parameter (the anion polarizability) is varied systematically to control the mean MXM bond angles (and hence the network topologies). The networks evolve from those dominated by corner-sharing units to those dominated by edge-sharing structural motifs. These changes are accompanied by changes in the characteristic length-scales, with the emergence of ordering on intermediate length-scales. Key dynamical properties (the liquid relaxation just above the melting point and the liquid fragility) are studied and their relationship to the underlying static structure analysed.
Evaluation of the Effectiveness and Perceived Benefits of Interventional Structured Infection Prevention and Control Training Module Introduced in the Undergraduate Medical Curricula

Introduction: Assessing and improving infection prevention and control (IPC) knowledge and practicing skills among medical students who are the future medical practitioners is crucial for reducing the burden of healthcare-associated infections (HAIs). In this study, we assessed the IPC knowledge of undergraduate clinical-year medical students before and after interventional IPC modular training and evaluated the effectiveness and students’ perception on structured modular IPC training presented to them. Methods: This cross-sectional interventional study was conducted on single medical cohort comprising of 145 final-year undergraduate medical students of the academic year 2022-23 at COMHS. Pre-test, post-test, and feedback questionnaire were used as the assessing tools. The data were collected, entered into Excel sheet, and analyzed using SPSS software version 22. McNemar and Paired-T tests were carried out, and a p value<0.05 was considered significant. Feedback of the questionnaire was analyzed using 3 Point Likert Scale as agree, neutral, and disagree. Results: Overall, mean IPC knowledge scores after training (37.65±1.37) was significantly higher as compared to before training (25.13±4.51). Prior knowledge scores on certain aspects of IPC such as duration of hand washing, steps of hand washing, sequence of donning and doffing of PPE, use of N95 mask, and appropriate sharp and needle precautions, and biomedical waste management were varied from 13.6% to 65.6%. However, overall participants’ knowledge (p value <0.001) on these aspects increased significantly after the training. The majority of the participants (>90%) perceived IPC training as an excellent tool to improve IPC knowledge and practicing skills. Conclusion: IPC training had a significant impact in gaining adequate IPC knowledge and practicing skills among our participants. Therefore, it is recommended that IPC training should be implemented in the undergraduate medical curriculum with greater emphasis on practicing skills.
Mathematical Game Theory

These lecture notes attempt a mathematical treatment of game theory akin to mathematical physics. A game instance is defined as a sequence of states of an underlying system. This viewpoint unifies classical mathematical models for 2-person and, in particular, combinatorial and zero-sum games as well as models for investing and betting. n-person games are studied with emphasis on notions of utilities, potentials and equilibria, which allows to subsume cooperative games as special cases. The represenation of a game theoretic system in a Hilbert space furthermore establishes a link to the mathematical model of quantum mechancis and general interaction systems.
Latency Target based Analysis of the DASH.js Player

We analyse the low latency performance of the three Adaptive Bitrate (ABR) algorithms in the dash.js Dynamic Adaptive Streaming over HTTP (DASH) player with respect to a range of latency targets and configuration options. We perform experiments on our DASH Testbed which allows for testing with a range of real world derived network profiles. Our experiments enable a better understanding of how latency targets affect quality of experience (QoE), and how well the different algorithms adhere to their targets. We find that with dash.js v4.5. 0 the default Dynamic algorithm achieves the best overall QoE. We show that whilst the other algorithms can achieve higher video quality at lower latencies, they do so only at the expense of increased stalling. We analyse the poor performance of L2A-LL in our tests and develop modifications which demonstrate significant improvements. We also highlight how some low latency configuration settings can be detrimental to performance.
Recompositing of Vast Irregularly-Sampled Seismic Data via Compressed Sensing Framework: An FPOCS Based on Seislet Transform Approach

Acquiring seismic data from irregular topographic surface is oftently oppressed by irregular and nonequivalent source-receiver arrays and even more it yields bad traces after storing the original signal. In the light of preprocessing seismic data, we have to extract out most of the given signal, thus further processing and interpretation can obtain extremely accurate outcomes. We applied Compressed Sensing theorem on Sigmoid vast irregularly-sampled seismic data based on the fast projection onto convex sets (FPOCS) algorithm with sparsity constraint in the seislet transform domain, which gives faster convergence than other conventional methods and is preserving an optimum signals recovery. The FPOCS seislet transform approach can achieve accurate and high data recovery results than other methods because of a much sparser structure in the seislet transform domain as demonstrated. Moreover, FPOCS algorithm is also efficient in minimizing the number of required iterations to achieve optimum data refilling.
C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference

Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer from high computational and communication costs due to the expensive multi-party computation (MPC) protocols. Existing literature has developed lighter MPC protocols to yield more efficient PI schemes. We, in contrast, propose to lighten them by introducing an empirically-defined privacy evaluation. To that end, we reformulate the threat model of PI and use inference data privacy attacks (IDPAs) to evaluate data privacy. We then present an enhanced IDPA, named distillation-based inverse-network attack (DINA), for improved privacy evaluation. Finally, we leverage the findings from DINA and propose C2PI, a two-party PI framework presenting an efficient partitioning of the neural network model and requiring only the initial few layers to be performed with MPC protocols. Based on our experimental evaluations, relaxing the formal data privacy guarantees C2PI can speed up existing PI frameworks, including Delphi [1] and Cheetah [2], up to 2.89× and 3.88× under LAN and WAN settings, respectively, and save up to 2.75× communication costs.
A Virtual 3D Hair Reconstruction Method from a 2D Picture

In this paper, we present a method for reconstructing virtual 3D hair from a 2D human picture. The proposed method only needs the user to provide a 2D positive picture as the reference texture. A canny operator combined with region hair color is used to detect the edge of the hair picture. The three ellipsoidal head modal axis parameters are then calculated from the edge contour. A multi-triangle mesh is constructed by adding point sets on the edge and interior. After that the Cartesian-triangular coordinate transform is used and the corresponding depth of each texture pixel is given to obtain the initial 3D hair shape. In order to simulate a multi-level real hair strip effect we use a label map to simulate hair strip growth from the edge of the given 2D positive picture. A B-Spline function is then applied to refine the preliminary hair strips. Finally 3D hair with the texture features of the given picture is modeled. The experimental results show that this method can be used to model 3D hair strips from a 2D picture with very few manual operations, presenting good stereo vision effect.
SHIELD: Thwarting Code Authorship Attribution

Authorship attribution has become increasingly accurate, posing a serious privacy risk for programmers who wish to remain anonymous. In this paper, we introduce SHIELD to examine the robustness of different code authorship attribution approaches against adversarial code examples. We define four attacks on attribution techniques, which include targeted and non-targeted attacks, and realize them using adversarial code perturbation. We experiment with a dataset of 200 programmers from the Google Code Jam competition to validate our methods targeting six state-of-the-art authorship attribution methods that adopt a variety of techniques for extracting authorship traits from source-code, including RNN, CNN, and code stylometry. Our experiments demonstrate the vulnerability of current authorship attribution methods against adversarial attacks. For the non-targeted attack, our experiments demonstrate the vulnerability of current authorship attribution methods against the attack with an attack success rate exceeds 98.5\% accompanied by a degradation of the identification confidence that exceeds 13\%. For the targeted attacks, we show the possibility of impersonating a programmer using targeted-adversarial perturbations with a success rate ranging from 66\% to 88\% for different authorship attribution techniques under several adversarial scenarios.