Miracle-dz/UniFAR-FTUs huggingface dataset:
Newly Generated 3D Schwann-Like Cell Spheroids From Human Adipose-Derived Stem Cells Using a Modified Protocol

Peripheral nerve injury (PNI) is a relatively frequent type of trauma that results in the suffering of many patients worldwide every year. Schwann cells (SCs) are expected to be applied in cell therapy because of their ability to promote peripheral nerve regeneration. However, the lack of clinically renewable sources of SCs hinders the application of SC-based therapies. Adipose-derived stem cells (ADSCs) have generated great interest in recent years because of their multipotency and ease of harvest, and they have already been verified to differentiate into Schwann-like cells (SLCs) in vitro. However, the efficiency of differentiation and the functions of SLCs remain unsatisfactory. We newly generated three-dimensional (3D) SLC spheroids from ADSCs using a modified protocol with human recombinant peptide (RCP) petaloid μ-piece. Morphological analysis, gene expression analysis by qRT-PCR, ELISA measurement of the secretion capabilities of neurotrophic factors, and neurite formation assay were performed to evaluate the functions of these 3D SLCs in vitro. Motor function recovery was measured in a sciatic nerve injury mouse model to analyze the nerve regeneration-promoting effect of 3D SLCs in vivo. The differentiation efficiency and the secretion of neurotrophic factors were enhanced in 3D SLCs compared with conventional SLCs. 3D SLCs could more effectively promote neurite growth and longer neurite extension in a neuron-like SH-SY5Y model. Additionally, 3D SLCs had a better therapeutic effect on nerve regeneration after transplantation into the sciatic nerve injury mouse model. These findings demonstrated that the potential of ADSC-derived SLCs to promote nerve regeneration could be significantly increased using our modified differentiation protocol and by assembling cells into a 3D sphere conformation. Therefore, these cells have great potential and can be used in the clinical treatment of PNI.
Mental models about heredity among immigrant Latinx adults with limited education from Mexico and Central America

An understanding of genetics is becoming increasingly relevant to receiving medical care. It is important for health care providers and educators, including genetic counselors, to understand patients’ perceptions about trait transmission and their interpretation of terms used in biomedicine. Knowledge about the patient perspective about trait transmission is important when health care providers are not fluent in the patient's language. Sixty Latinx immigrant adults (30 men and 30 women) who were born in Mexico or Central America (MCA) and living in North Carolina were interviewed about their heredity beliefs. By design, most participants had limited education. Eight percent had a least a high school education; 45% had less than a seventh grade education. Semi‐structured, in‐depth interviews were conducted to examine how participants think and discuss trait transmission. The translated transcripts were systematically analyzed using a case‐based approach, supplemented by theme‐based coding. Five lay mental models of heredity were identified that varied in terms of involvement of genes. Four of the five heredity mental models encompass genes; four out of five mental models do not link DNA to heredity. The centrality of blood, whether used metaphorically or literally, varies widely across the models. One model references God and depicts that heredity involves blood and/or genes, but not DNA. The mental models of heredity for most adult immigrants with limited education do not include DNA. Trait transmission by blood appears to have a more prominent role in lay mental models held by Mexicans than Central Americans. Increased patient knowledge about genetics can facilitate shared decision‐making as genetics becomes increasingly relevant to medical care. Efforts to educate people can be most effective when we first understand the layperson's conceptions or mental models. Health care providers and educators should be aware that MCA adults with limited formal education hold diverse mental models about heredity.
A Review: Enhanced City Bus Tracking System

Primary information for the most city transport travellers the bus arrival time. Excessively there often discourages the travellers due to long waiting time at bus stops and for taking buses makes them reluctant. In this system, based on bus passengers’ participatory sensing we are going to present a system which will predict the time of bus arrival. With commodity mobile phones, for estimating the routes of bus traveling and to prediction of arrival time of bus at different bus stops, the passengers of bus are effectively collected and also utilized context of surrounding environmental. On the participating users collaborative effort there solely relies the and it is not dependent from the operating companies of bus, so without support requesting from particular bus operating companies for supporting the universal bus service systems it can be adopted easily. We resort to energy efficient sensing and more generally available resources, including signals of cell tower, statuses of movement, recordings of audio, etc., instead of referring to GPS enabled information of location, to the participatory party and bring less burden by encouraging their participation.
Code-mixing and code-switching

Alih kode dan campur kode adalah salah satu fenomen a k bahasaan yang sering sekali terjadi dikalangan masyarakat, baik masyarakat bilingual, m u tilingual dan monolingual. Hal ini kurang mendapat perhatian dikalangan orang-orang yang kura ng memperhatikan atau kurang terpusat dalam dunia kebahasaan atau kebudayaan. Tapi bagi s eorang peneliti bahasa atau peneliti fenomena budaya hal ini akan menjadi suatu bahasan mnarik. Oleh karena itu kami ingin membuat penelitian yang bisa mengungkapkan hal ters ebut diatas, dengan lebih ringan dan bisa dipahami semua kalangan, bukan hanya ahli bahasa at upun peneliti budaya. Dijaman seperti sekarang yang kabanyakan masyarakatnya multilingual ism sangat sulit sekali untuk menghindari alih kode atau campur kode. Tapi dalam menggunakan li kode dan campur kode kita harus mengetahuibatasan-batasan yang berlaku agar tidak t erjadi tumpang tindih bahasa dan kebingungan dalam memahami bahasa.
On Fragile Features and Batch Normalization in Adversarial Training

Modern deep learning architecture utilize batch normalization (BN) to stabilize training and improve accuracy. It has been shown that the BN layers alone are surprisingly expressive. In the context of robustness against adversarial examples, however, BN is argued to increase vulnerability. That is, BN helps to learn fragile features. Nevertheless, BN is still used in adversarial training, which is the de-facto standard to learn robust features. In order to shed light on the role of BN in adversarial training, we investigate to what extent the expressiveness of BN can be used to robustify fragile features in comparison to random features. On CIFAR10, we find that adversarially fine-tuning just the BN layers can result in non-trivial adversarial robustness. Adversarially training only the BN layers from scratch, in contrast, is not able to convey meaningful adversarial robustness. Our results indicate that fragile features can be used to learn models with moderate adversarial robustness, while random features cannot
Toward Consistent and Efficient Map-Based Visual-Inertial Localization: Theory Framework and Filter Design

This article focuses on designing a consistent and efficient filter for visual-inertial localization given a prebuilt map. First, we propose a new Lie group with its algebra based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of map information, the proposed invariant EKF is able to naturally preserve the correct observability properties of the system. To consider the uncertainty of map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of map information can be taken into consideration to avoid overconfident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.
Supersymmetric dS n solutions for n ⩾ 5in D = 11 supergravity

We determine the necessary and sufficient conditions for warped product dS n solutions, 5 ⩽ n ⩽ 10, to preserve supersymmetry in D = 11 supergravity, without assuming factorization of the Killing spinors. We prove that for 7 ⩽ n ⩽ 10, all such solutions are flat, with vanishing four-form. We also show that the only warped product dS 6 solutions are either the maximally supersymmetric AdS 7 × S 4 solution, or R1,6×N4 where N 4 is hyperKähler, with vanishing four-form. Supersymmetric warped product dS 5 solutions are then classified; it is shown that all such solutions are generalized M5-brane configurations, for which the transverse space is R×N4 , and N 4 is a hyperKähler manifold. If the four-form is covariantly constant, then N 4 admits a hyperKähler potential.
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs

DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organizations and research labs across scales. This paper aims to significantly reduce training costs with effective use of preemptible instances, i.e., those that can be obtained at a much cheaper price while idle, but may be preempted whenever requested by priority users. Doing so, however, requires new forms of resiliency and efficiency to cope with the possibility of frequent preemptions - a failure model that is drastically different from the occasional failures in normal cluster settings that existing checkpointing techniques target. We present Bamboo, a distributed system that tackles these challenges by introducing redundant computations into the training pipeline, i.e., whereby one node performs computations over not only its own layers but also over some layers in its neighbor. Our key insight is that training large models often requires pipeline parallelism where"pipeline bubbles"naturally exist. Bamboo carefully fills redundant computations into these bubbles, providing resilience at a low cost. Across a variety of widely used DNN models, Bamboo outperforms traditional checkpointing by 3.7x in training throughput, and reduces costs by 2.4x compared to a setting where on-demand instances are used.
Non-determinsitic algebraic rewriting as adjunction

We develop a general model theoretic semantics to rewriting beyond the usual confluence and termination assumptions. This is based on preordered algebra which is a model theory that extends many sorted algebra. In this framework we characterise rewriting in arbitrary algebras rather than term algebras (called algebraic rewriting) as a persistent adjunction and use this result, on the one hand for proving the soundness and the completeness of an abstract computational model of rewriting that underlies the non-deterministic programming with Maude and CafeOBJ, and on the other hand for developing a compositionality result for algebraic rewriting in the context of the pushout-based modularisation technique.
Motion Planning and Robust Tracking for the Heat Equation using Boundary Control

Robust output tracking is addressed in this paper for a heat equation with Neumann boundary conditions and anti-collocated boundary input and output. The desired reference tracking is solved using the well-known flatness and Lyapunov approaches. The reference profile is obtained by solving the motion planning problem for the nominal plant. To robustify the closed-loop system in the presence of the disturbances and uncertainties, it is then augmented with PI feedback plus a discontinuous component responsible for rejecting matched disturbances with \textit{a priori} known magnitude bounds. Such control law only requires the information of the system at the same boundary as the control input is located. The resulting dynamic controller globally exponentially stabilizes the error dynamics while also attenuating the influence of Lipschitz-in-time external disturbances and parameter uncertainties. For the case when the motion planning is performed over the uncertain plant, an exponential Input-to-State Stability is obtained, preserving the boundedness of the tracking error norm. The proposed controller relies on a discontinuous term that however passes through an integrator, thereby minimizing the chattering effect in the plant dynamics. The performance of the closed-loop system, thus designed, is illustrated in simulations under different kinds of reference trajectories in the presence of external disturbances and parameter uncertainties.
Law of Total Probability in Quantum Theory and Its Application in Wigner’s Friend Scenario

It is well-known that the law of total probability does not generally hold in quantum theory. However, recent arguments on some of the fundamental assumptions in quantum theory based on the extended Wigner’s friend scenario show a need to clarify how the law of total probability should be formulated in quantum theory and under what conditions it still holds. In this work, the definition of conditional probability in quantum theory is extended to POVM measurements. A rule to assign two-time conditional probability is proposed for incompatible POVM operators, which leads to a more general and precise formulation of the law of total probability. Sufficient conditions under which the law of total probability holds are identified. Applying the theory developed here to analyze several quantum no-go theorems related to the extended Wigner’s friend scenario reveals logical loopholes in these no-go theorems. The loopholes exist as a consequence of taking for granted the validity of the law of total probability without verifying the sufficient conditions. Consequently, the contradictions in these no-go theorems only reconfirm the invalidity of the law of total probability in quantum theory rather than invalidating the physical statements that the no-go theorems attempt to refute.
Learning Weighting Map for Bit-Depth Expansion within a Rational Range

Bit-depth expansion (BDE) is one of the emerging technologies to display high bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth. In this paper, we design a bit restoration network (BRNet) to learn a weight for each pixel, which indicates the ratio of the replenished value within a rational range, invoking an accurate solution without modifying the given high-order bit information. To make the network adaptive for any bit-depth degradation, we investigate the issue in an optimization perspective and train the network under progressive training strategy for better performance. Moreover, we employ Wasserstein distance as a visual quality indicator to evaluate the difference of color distribution between restored image and the ground truth. Experimental results show our method can restore colorful images with fewer artifacts and false contours, and outperforms state-of-the-art methods with higher PSNR/SSIM results and lower Wasserstein distance. The source code will be made available at https://github.com/yuqing-liu-dut/bit-depth-expansion
Complexity of quantum circuits via sensitivity, magic, and coherence

A BSTRACT . Quantum circuit complexity—a measure of the minimum number of gates needed to implement a given unitary transformation—is a fundamental concept in quantum computation, with widespread applications ranging from determining the running time of quantum algorithms to understanding the physics of black holes. In this work, we study the complexity of quantum circuits using the notions of sensitivity, average sensitivity (also called inﬂu-ence), magic, and coherence. We characterize the set of unitaries with vanishing sensitivity and show that it coincides with the family of matchgates. Since matchgates are tractable quantum circuits, we have proved that sensitivity is necessary for a quantum speedup. As magic is another measure to quantify quantum advantage, it is interesting to understand the relation between magic and sensitivity. We do this by introducing a quantum version of the Fourier entropy-inﬂuence relation. Our results are pivotal for understanding the role of sensitivity, magic, and coherence in quantum computation.
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning

How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment? Here, we address both problems by employing a deep reinforcement learning model to optimize the social learning strategies (SLSs) of agents in a cooperative game in a multi-dimensional landscape. Throughout the training for maximizing the overall payoff, we find that the agent spontaneously learns various concepts of social learning, such as copying, focusing on frequent and well-performing neighbors, self-comparison, and the importance of balancing between individual and social learning, without any explicit guidance or prior knowledge about the system. The SLS from a fully trained agent outperforms all of the traditional, baseline SLSs in terms of mean payoff. We demonstrate the superior performance of the reinforcement learning agent in various environments, including temporally changing environments and real social networks, which also verifies the adaptability of our framework to different social settings.
Federated Progressive Sparsification (Purge, Merge, Tune)+

To improve federated training of neural networks, we develop FedSparsify, a sparsification strategy based on progressive weight magnitude pruning. Our method has several benefits. First, since the size of the network becomes increasingly smaller, computation and communication costs during training are reduced. Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates. Third, the final sparsified model is significantly smaller, which improves inference efficiency and optimizes operations latency during encrypted communication. We show experimentally that FedSparsify learns a subnetwork of both high sparsity and learning performance. Our sparse models can reach a tenth of the size of the original model with the same or better accuracy compared to existing pruning and nonpruning baselines.
Quantum error mitigation by hidden inverses protocol in superconducting quantum devices

We present a method to improve the convergence of variational algorithms based on hidden inverses (HIs) to mitigate coherent errors. In the context of error mitigation, this means replacing the hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H 2 ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how HIs and randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high level performance.
Sparse-Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models

This work presents an approach for modelling and tracking previously unseen objects for robotic grasping tasks. Using the motion of objects in a scene, our approach segments rigid entities from the scene and continuously tracks them to create a dense and sparse model of the object and the environment. While the dense tracking enables interaction with these models, the sparse tracking makes this robust against fast movements and allows to redetect already modelled objects. The evaluation on a dual-arm grasping task demonstrates that our approach 1) enables a robot to detect new objects online without a prior model and to grasp these objects using only a simple parameterisable geometric representation, and 2) is much more robust compared to the state of the art methods.
Cleanformer: A microphone array configuration-invariant, streaming, multichannel neural enhancement frontend for ASR

This work introduces the Cleanformer , a streaming multichannel neural based enhancement frontend for automatic speech recognition (ASR). This model has a conformer-based architecture which takes as inputs a single channel each of raw and enhanced signals, and uses self-attention to derive a time-frequency mask. The enhanced input is generated by a multichannel adaptive noise cancellation algorithm known as Speech Cleaner, which makes use of noise context to derive its ﬁlter taps. The time-frequency mask is applied to the noisy input to produce enhanced output features for ASR. Detailed evalu-ations are presented with simulated and re-recorded datasets in speech-based and non-speech-based noise that show signiﬁcant reduction in word error rate (WER) when using a large-scale state-of-the-art ASR model. It also will be shown to signiﬁcantly outperform enhancement using a beamformer with ideal steering. The enhancement model is agnostic of the number of microphones and array conﬁguration and, therefore, can be used with different microphone arrays without the need for retrain-ing. It is demonstrated that performance improves with more microphones, up to 4, with each additional microphone providing a smaller marginal beneﬁt. Speciﬁcally, for an SNR of -6dB, relative WER improvements of about 80% are shown in both noise conditions.