Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical profile and treatment outcomes among patients with sporadic and multiple endocrine neoplasia syndrome‐related primary hyperparathyroidism

Accurate demarcation between multiple endocrine neoplasia, type 1 (MEN1)‐ related primary hyperparathyroidism (MPHPT) and sporadic PHPT (SPHPT) is important to plan the management of primary parathyroid disease and surveillance for other endocrine and nonendocrine tumours. The objective of this study is to compare the clinical, biochemical and radiological features and surgical outcomes in patients with MPHPT versus SPHPT and to identify the predictors of MEN1 syndrome in PHPT.
A Novel Initialization Method for Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) [1] algorithms have been successfully employed in many areas of applications, such as image processing and machine learning. This problem can be described briefly as follow: Denoted by the set of all square n-by-n nonnegative matrices, SNMF problem we consider is that for a given symmetric matrix and , finding such that , W is a nonnegative matrix and WW is an approximation of the given symmetric matrix A in some distant metrics(in this paper, authors use Frobenius norm ||.||F as the distant metrics). In some cases, such as A is indefinite and has some negative eigenvalues, will not provide a good approximation for A because cannot absorb the subspaces associated with negative eigenvalues. However, the weighted SNMF can absorb the subspaces associated with both positive and negative eigenvalues. Also, we know that matrix have Choleskey factorization if A is symmetric positive definition. Otherwise, one does where the diagonal matrix D takes care of the negative eigenvalues. Most of existing NMF-related researches are focused on the factorization algorithm. Yang et.al proposed a nonnegative matrix factorization algorithm based a trained projection matrix [2]. Kannan et.al proposed distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems [3]. The need to
When I say … imposter syndrome

The term ‘imposter syndrome’ seems to permeate throughout medical education, echoed throughout the halls by learners and faculty alike. Its origins extend back to 1978, when Clance and Imes first characterised this as an inability to attribute success to one's own efforts, instead attributing this to luck or transient qualities. Since then, studies have reported imposter syndrome in up to half of all medical students and resident physicians. Despite the relatively high incidence reported, the literature in medical education has been relatively scant with one scoping review identifying only 18 articles among physicians and physicians-in-training, with the majority consisting of single-site survey studies. In this paper, I will discuss the concept of imposter syndrome through three lenses: as a spectrum, as a dynamic process and as a double-edged sword.
On the non-linear influence of the edge geometry on vortex shedding in Helmholtz resonators

This study investigates the effect of edge profile of a Helmholtz resonator neck in non-linear regime by means of experiments and large eddy simulations. The dissipation mechanisms in a Helmholtz resonator differ significantly, depending on the sound pressure level. At low levels, i. e. , in the linear regime, thermo-viscous effects are responsible for the dissipation of the acous- tic energy since the oscillating flow follows the neck geometry. However, increasing the sound pressure level results in flow separation at the edges. At these points, vortices form which con- vert acoustic perturbation energy to the hydrodynamic mode. This is a strong non-linear effect increasing the dissipation considerably. To observe this effect, experiments and numerical simu- lations are carried out for combinations of various backing volumes, sound pressure levels, and neck profiles. The neck profiles are selected as 45◦–chamfers due to manufacturing concerns. Hereby, a strong dependence on the edge shape is observed in both experiments and numerical simulations. The presence of the chamfer reduces the vortex shedding in comparison to the sharp edge significantly, which leads to a lower acoustic resistance.
Profiles of Dialogue

According to Walton (1999: 53), a “profile of dialogue is a connected sequence of moves and countermoves in a conversational exchange of a type that is goal-directed and can be represented in a normative model of dialogue. ” I should like to add that, generally, a profile is not to be identified with a single occurrence of a sequence, but is supposed to give us some general features of such sequences at some intermediate level of abstraction. Also a profile, generally, does not display just one (abstract) sequence, but a number of them, organized as branches in a tree. In this paper I hope to clarify the notion of “profile of dialogue” to a certain extent, by giving examples. There will be no attempt to reach a definition of the concept. At the end, I shall indicate how profiles can be useful for the study of argumentation.
A new method for coarsening tetrahedral meshes

To coarsen a mesh, we usually remove a set of selected nodes one by one. Currently, the basic operation used to remove a node is edge collapsing, which does not perform well when applied to handling narrow regions in a tetrahedron mesh and could produce low‐quality elements or even fail to give valid results. To overcome the drawbacks of edge collapsing, we present a new node‐removal operator created by revising a topological transformation called small polyhedron reconnection. This new operator can guarantee success if the cavity that forms after a node is removed is meshable, and it produces higher‐quality results and keeps the nodes unmoved, which is preferred for applications such as multigrid hierarchies.
Brassicaceae display variation in efficiency of photorespiratory carbon-recapturing mechanisms

Abstract Carbon-concentrating mechanisms enhance the carboxylase efficiency of Rubisco by providing supra-atmospheric concentrations of CO2 in its surroundings. Beside the C4 photosynthesis pathway, carbon concentration can also be achieved by the photorespiratory glycine shuttle which requires fewer and less complex modifications. Plants displaying CO2 compensation points between 10 ppm and 40 ppm are often considered to utilize such a photorespiratory shuttle and are termed ‘C3–C4 intermediates’. In the present study, we perform a physiological, biochemical, and anatomical survey of a large number of Brassicaceae species to better understand the C3–C4 intermediate phenotype, including its basic components and its plasticity. Our phylogenetic analysis suggested that C3–C4 metabolism evolved up to five times independently in the Brassicaceae. The efficiency of the pathway showed considerable variation. Centripetal accumulation of organelles in the bundle sheath was consistently observed in all C3–C4-classified taxa, indicating a crucial role for anatomical features in CO2-concentrating pathways. Leaf metabolite patterns were strongly influenced by the individual species, but accumulation of photorespiratory shuttle metabolites glycine and serine was generally observed. Analysis of phosphoenolpyruvate carboxylase activities suggested that C4-like shuttles have not evolved in the investigated Brassicaceae. Convergent evolution of the photorespiratory shuttle indicates that it represents a distinct photosynthesis type that is beneficial in some environments.
Highly Driven Polymer Translocation in the Presence of External Constraints: Simulations and Theory

Langevin Dynamics simulations of polymer translocation are performed where the polymer is stretched via two opposing forces applied on the first and last monomer before, and during, translocation. In this setup, polymer translocation is achieved by imposing a bias between the two pulling forces such that there is net drift towards the trans-side. Under the influence of pre-stretching forces, the elongated polymer ensemble contains fewer variations in conformations compared to an unstretched ensemble. Simulations demonstrate that this reduced spread in initial conformations yields a reduced variation in translocation times relative to the mean translocation time. This e↵ect is explored for di↵erent ratios of the amplitude of thermal fluctuations to driving forces to control for the relative influence of the thermal path sampled by the polymer. Since the variance in translocation times is due to contributions coming from sampling both thermal noise and initial conformations, our simulations o↵er independent control over the two main sources of noise, and allow us to shed light on how they both contribute to translocation dynamics. Experimentally relevant conditions are highlighted and shown to correspond to a significant decrease in the variation of translocation times, thus indicating that stretching DNA prior to translocation could assist in nanopore-based sequencing and sizing applications. PACS numbers: 87.15. ap, 82.35. Lr, 82.35. Pq
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification

The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection.
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer

Deep neural networks often suffer from poor generalization due to complex and non-convex loss landscapes. Sharpness-Aware Minimization (SAM) is a popular solution that smooths the loss landscape by minimizing the maximized change of training loss when adding a perturbation to the weight. However, indiscriminate perturbation of SAM on all parameters is suboptimal and results in excessive computation, double the overhead of common optimizers like Stochastic Gradient Descent (SGD). In this paper, we propose Sparse SAM (SSAM), an efficient and effective training scheme that achieves sparse perturbation by a binary mask. To obtain the sparse mask, we provide two solutions based on Fisher information and dynamic sparse training, respectively. We investigate the impact of different masks, including unstructured, structured, and $N$:$M$ structured patterns, as well as explicit and implicit forms of implementing sparse perturbation. We theoretically prove that SSAM can converge at the same rate as SAM, i.e., $O(\log T/\sqrt{T})$. Sparse SAM has the potential to accelerate training and smooth the loss landscape effectively. Extensive experimental results on CIFAR and ImageNet-1K confirm that our method is superior to SAM in terms of efficiency, and the performance is preserved or even improved with a perturbation of merely 50\% sparsity. Code is available at https://github.com/Mi-Peng/Systematic-Investigation-of-Sparse-Perturbed-Sharpness-Aware-Minimization-Optimizer.
LISA Dynamics&Control: Closed-loop Simulation and Numerical Demonstration of Time Delay Interferometry

The Laser Interferometer Space Antenna (LISA), space-based gravitational wave observatory involves a complex multidimensional closed-loop dynamical system. Its instrument performance is expected to be less efficiently isolated from platform motion than was its simpler technological demonstrator, LISA Pathfinder. It is of crucial importance to understand and model LISA dynamical behavior accurately to understand the propagation of dynamical excitations through the response of the instrument down to the interferometer data streams. More generally, simulation of the system allows for the preparation of the processing and interpretation of in-flight metrology data. In this work, we present a comprehensive mathematical modeling of the closed-loop system dynamics and its numerical implementation within the LISA Consortium simulation suite. We provide, for the first time, a full time-domain numerical demonstration of post-processing Time Delay Interferometer techniques combining multiple position measurements with realistic control loops to create a synthetic Michelson interferometer. We show that in the absence of physical coupling to spacecraft and telescope motion (through tilt-to-length, stiffness and actuation cross-talk) the effect of noisy spacecraft motion is efficiently suppressed to a level below the noise originating in the rest of the instrument.
Decentralized Motor Skill Learning for Complex Robotic Systems

Reinforcement learning (RL) has achieved remarkable success in complex robotic systems (eg. quadruped locomotion). In previous works, the RL-based controller was typically implemented as a single neural network with concatenated observation input. However, the corresponding learned policy is highly task-specific. Since all motors are controlled in a centralized way, out-of-distribution local observations can impact global motors through the single coupled neural network policy. In contrast, animals and humans can control their limbs separately. Inspired by this biological phenomenon, we propose a Decentralized motor skill (DEMOS) learning algorithm to automatically discover motor groups that can be decoupled from each other while preserving essential connections and then learn a decentralized motor control policy. Our method improves the robustness and generalization of the policy without sacrificing performance. Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.
Dynamical Hall responses of disordered superconductors

We extend the Mattis-Bardeen theory for the dynamical response of superconductors to include different types of Hall responses. This is possible thanks to a recent modification of the quasiclassical Usadel equation, which allows for analyzing Hall effects in disordered superconductors and including the precise frequency dependence of such effects. Our results form a basis for analyzing dynamical experiments especially on novel thin-film superconductors, where ordinary Hall and spin Hall effects can both show up.
Achieving RGB-D level Segmentation Performance from a Single ToF Camera

Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras. In this work, we show that it is possible to obtain the same level of accuracy as RGB-D cameras on a semantic segmentation task using infrared (IR) and depth images from a single Time-of-Flight (ToF) camera. In order to fuse the IR and depth modalities of the ToF camera, we introduce a method utilizing depth-specific convolutions in a multi-task learning framework. In our evaluation on an in-car segmentation dataset, we demonstrate the competitiveness of our method against the more costly RGB-D approaches.
Observer-Based Boundary Control Design for the Suppression of Stick–Slip Oscillations in Drilling Systems With Only Surface Measurements

We develop an observer-based boundary controller for the rotary table to suppress stick–slip oscillations and to maintain the angular velocity of the drill string at a desired value during a drilling process despite unknown friction torque and by using only surface measurements. The control design is based on a distributed model of the drill string. The obtained infinite dimensional model is converted to an ordinary differential equation–partial differential equation (ODE–PDE) coupled system. The observer-based controller is designed by reformulating the problem as the stabilization of an linear time-invariant (LTI) system which is affected by a constant unknown disturbance and has simultaneous actuator and sensor delays. The main contribution of the controller is that it requires only surface measurements. We prove that the equilibrium of the closed-loop system is exponentially stable, and that the angular velocity regulation is achieved with the estimations of unknown friction torque and drill bit velocity. The effectiveness of the controller is demonstrated using numerical simulations. [DOI: 10.1115/1.4036549]
Scalable tensor methods for nonuniform hypergraphs

While multilinear algebra appears natural for studying the multiway interactions modeled by hypergraphs, tensor methods for general hypergraphs have been stymied by theoretical and practical barriers. A recently proposed adjacency tensor is applicable to nonuniform hypergraphs, but is prohibitively costly to form and analyze in practice. We develop tensor times same vector (TTSV) algorithms for this tensor which improve complexity from $O(n^r)$ to a low-degree polynomial in $r$, where $n$ is the number of vertices and $r$ is the maximum hyperedge size. Our algorithms are implicit, avoiding formation of the order $r$ adjacency tensor. We demonstrate the flexibility and utility of our approach in practice by developing tensor-based hypergraph centrality and clustering algorithms. We also show these tensor measures offer complementary information to analogous graph-reduction approaches on data, and are also able to detect higher-order structure that many existing matrix-based approaches provably cannot.
Beyond Active Engagement: The Significance of Lurkers in a Polarized Twitter Debate

The emergence of new public forums in the shape of online social media has introduced unprecedented challenges to public discourse, including polarization, misinformation, and the emergence of echo chambers. While existing research has extensively studied the behavior of active users within echo chambers, little attention has been given to the hidden audience, also known as lurkers, who passively consume content without actively engaging. This study aims to estimate the share of the hidden audience and investigate their interplay with the echo chamber effect. Using Twitter as a case study, we analyze a polarized political debate to understand the engagement patterns and factors influencing the hidden audience's presence. Our findings reveal a relevant fraction of users that consume content without active interaction, which underscores the importance of considering their presence in online debates. Notably, our results indicate that the engagement of the hidden audience is primarily influenced by factors such as the reliability of media sources mentioned in tweets rather than the ideological stance of the user that produced the content. These findings highlight the need for a comprehensive understanding of the hidden audience's role in online debates and how they may influence public opinion.
Beyond-Voice: Towards Continuous 3D Hand Pose Tracking on Commercial Home Assistant Devices

The surging popularity of home assistants and their voice user interface (VUI) have made them an ideal central control hub for smart home devices. However, current form factors heavily rely on VUI, which poses accessibility and usability issues; some latest ones are equipped with additional cameras and displays, which are costly and raise privacy concerns. These concerns jointly motivate Beyond-Voice, a novel high-fidelity acoustic sensing system that allows commodity home assistant devices to track and reconstruct hand poses continuously. It transforms the home assistant into an active sonar system using its existing onboard microphones and speakers. We feed a high-resolution range profile to the deep learning model that can analyze the motions of multiple body parts and predict the 3D positions of 21 finger joints, bringing the granularity for acoustic hand tracking to the next level. It operates across different environments and users without the need for personalized training data. A user study with 11 participants in 3 different environments shows that Beyond-Voice can track joints with an average mean absolute error of 16.47mm without any training data provided by the testing subject.
Tidal Truncation of Circumplanetary Disks Fails above a Critical Disk Aspect Ratio

We use numerical simulations of circumplanetary disks to determine the boundary between disks that are radially truncated by the tidal potential and those where gas escapes the Hill sphere. We consider a model problem, in which a coplanar circumplanetary disk is resupplied with gas at an injection radius smaller than the Hill radius. We evolve the disk using the Phantom smoothed particle hydrodynamics code until a steady state is reached. We find that the most significant dependence of the truncation boundary is on the disk aspect ratio H/R. Circumplanetary disks are efficiently truncated for H/R ≲ 0.2. For H/R ≃ 0.3, up to about half of the injected mass, depending on the injection radius, flows outward through the decretion disk and escapes. As expected from analytic arguments, the conditions (H/R and Shakura–Sunyaev α) required for tidal truncation are independent of planet mass. A simulation with larger α = 0.1 shows stronger outflow than one with α = 0.01, but the dependence on transport efficiency is less important than variations of H/R. Our results suggest two distinct classes of circumplanetary disks: tidally truncated thin disks with dust-poor outer regions, and thicker actively decreting disks with enhanced dust-to-gas ratios. Applying our results to the PDS 70 c system, we predict a largely truncated circumplanetary disk, but it is possible that enough mass escapes to support an outward flow of dust that could explain the observed disk size.
Point-based Value Iteration for Neuro-Symbolic POMDPs

Neuro-symbolic artificial intelligence is an emerging area that combines traditional symbolic techniques with neural networks. In this paper, we consider its application to sequential decision making under uncertainty. We introduce neuro-symbolic partially observable Markov decision processes (NS-POMDPs), which model an agent that perceives a continuous-state environment using a neural network and makes decisions symbolically, and study the problem of optimising discounted cumulative rewards. This requires functions over continuous-state beliefs, for which we propose a novel piecewise linear and convex representation (P-PWLC) in terms of polyhedra covering the continuous-state space and value vectors, and extend Bellman backups to this representation. We prove the convexity and continuity of value functions and present two value iteration algorithms that ensure finite representability by exploiting the underlying structure of the continuous-state model and the neural perception mechanism. The first is a classical (exact) value iteration algorithm extending $\alpha$-functions of Porta et al (2006) to the P-PWLC representation for continuous-state spaces. The second is a point-based (approximate) method called NS-HSVI, which uses the P-PWLC representation and belief-value induced functions to approximate value functions from below and above for two types of beliefs, particle-based and region-based. Using a prototype implementation, we show the practical applicability of our approach on two case studies that employ (trained) ReLU neural networks as perception functions, dynamic car parking and an aircraft collision avoidance system, by synthesising (approximately) optimal strategies. An experimental comparison with the finite-state POMDP solver SARSOP demonstrates that NS-HSVI is more robust to particle disturbances.