Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept for a 3D-printed soft rotary actuator driven by a shape-memory alloy

In line with the recent development of soft actuators involving shape-memory alloys (SMAs) embedded in compliant structures, this paper proposes a concept for a rotary actuator driven by a SMA wire placed inside a 3D-printed helical structure. The concept consists of using the one-way memory effect of the SMA (activated by Joule heating) to create the rotation of a material point of the structure, while the inverse rotation is obtained during the return to ambient temperature thanks to the structure’s elasticity. The study was performed in three steps. First, a prototype was designed from a chain of design rules, and tested to validate the feasibility of the concept. Thermal and geometrical measurements were performed using infrared and visible-range stereo cameras. A clockwise rotation (250°) followed by an anti-clockwise rotation (−200°) were obtained, enabling us to validate the concept despite the partial reversibility of the movement. Second, finite element simulations were performed to improve rotation reversibility. The high compliance of the mechanical system required a framework of large displacements for the calculations (in the strength of materials sense), due to the high structural flexibility. Finally, a second prototype was constructed and tested. Attention was paid to the rotation (fully reversible rotation of 150° reached) as well as to parasitic movements due to overall structural deformation. This study opens new prospects for the design and analysis of 3D-printed soft actuators activated by smart materials.
Superhumps in the cataclysmic variable BG Tri

We present a detailed photometric study of the bright cataclysmic variable BG Tri using ground-based observations mainly from Rozhen Observatory, ASAS-SN, TESS, and WASP sky surveys. We report the discovery of a negative superhump with P−sh = 0.1515(2) days and a co-existing superorbital variation with P = 3.94(53) days in data from 2019 and 2020. A positive superhump with P+sh = 0.1727(14) days is also discovered in data from 2006. The obtained negative superhump deficit ϵ− = 0.044(1) and the positive superhump excess ϵ+ = 0.090(9) give us an independent photometric evaluation of the mass ratio (q) of the system, which we find to be q− = 0.37(2) and q+ = 0.40(5) respectively. We also present a study of the quasi-periodic oscillations (QPOs) and stochastic variability (flickering) in BG Tri. The light curves show a rich mixture of simultaneously overlapping quasi-periods ranging from 5 to 25 minutes. The multi-colour (UBVRI) photometric observations from Rozhen Observatory reveal the typical increase of the flickering amplitudes to the shorter wavelengths. The recently introduced A60 amplitude of the flickering light source in all studied photometric bands is systematically lower when the negative superhump is gone in season 2021.
Dataset Condensation with Latent Space Knowledge Factorization and Sharing

In this paper, we introduce a novel approach for systematically solving dataset condensation problem in an efficient manner by exploiting the regularity in a given dataset. Instead of condensing the dataset directly in the original input space, we assume a generative process of the dataset with a set of learnable codes defined in a compact latent space followed by a set of tiny decoders which maps them differently to the original input space. By combining different codes and decoders interchangeably, we can dramatically increase the number of synthetic examples with essentially the same parameter count, because the latent space is much lower dimensional and since we can assume as many decoders as necessary to capture different styles represented in the dataset with negligible cost. Such knowledge factorization allows efficient sharing of information between synthetic examples in a systematic way, providing far better trade-off between compression ratio and quality of the generated examples. We experimentally show that our method achieves new state-of-the-art records by significant margins on various benchmark datasets such as SVHN, CIFAR10, CIFAR100, and TinyImageNet.
Generating people flow from architecture of real unseen environments

Mapping people dynamics is a crucial skill, because it enables robots to coexist in human-inhabited environments. However, learning a model of people dynamics is a time consuming process which requires observation of large amount of people moving in an environment. Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model only able to describe the dynamics of the environment it has been built in. However, the effect of architectural geometry on people movement can be used to estimate their dynamics, and recent work has looked into learning maps of dynamics from geometry. So far however, these methods have evaluated their performance only on small-size synthetic data, leaving the actual ability of these approaches to generalize to real conditions unexplored. In this work we propose a novel approach to learn people dynamics from geometry, where a model is trained and evaluated on real human trajectories in large-scale environments. We then show the ability of our method to generalize to unseen environments, which is unprecedented for maps of dynamics.
Foraging in Particle Systems via Self-Induced Phase Changes

The foraging problem asks how a collective of particles with limited computational, communication and movement capabilities can autonomously compress around a food source and disperse when the food is depleted or shifted, which may occur at arbitrary times. We would like the particles to iteratively self-organize, using only local interactions, to correctly gather whenever a food particle remains in a position long enough and search if no food particle has existed recently. Unlike previous approaches, these search and gather phases should be self-induced so as to be indefinitely repeatable as the food evolves, with microscopic changes to the food triggering macroscopic, system-wide phase transitions. We present a stochastic foraging algorithm based on a phase change in the fixed magnetization Ising model from statistical physics: Our algorithm is the first to leverage self-induced phase changes as an algorithmic tool. A key component of our algorithm is a careful token passing mechanism ensuring a dispersion broadcast wave will always outpace a compression wave. We also present a highly structured alternative algorithm that gathers by incrementally building a spiral tightly wrapped around the food particle.
System Fingerprints Detection for DeepFake Audio: An Initial Dataset and Investigation

Many effective attempts have been made for deepfake audio detection. However, they can only distinguish between real and fake. For many practical application scenarios, what tool or algorithm generated the deepfake audio also is needed. This raises a question: ‘Can we detect the system fingerprints of deepfake audio? ’ Therefore, this paper conducts a preliminary investigation to detect system fingerprints of deepfake audio. Experiments are conducted on deep-fake audio datasets from five latest deep-learning speech synthesis systems. The results show that LFCC features are relatively more suitable for system fingerprints detection. Moreover, the ResNet achieves the best detection results among LCNN and x-vector based models. The t-SNE visualization shows that different speech synthesis systems generate distinct system fingerprints.
New conceptions of truth foster misinformation in online public political discourse

The spread of online misinformation is increasingly perceived as a major problem for societal cohesion and democracy [1, 2]. Much attention has focused on the role of social media as a vector of misinformation [3]. The role of political leaders has attracted less research attention, even though leaders demonstrably inﬂuence media coverage [4] and public opinion [5], and even though politicians who “speak their mind” are perceived by segments of the public as authentic and honest even if their statements are unsupported by evidence or facts [6–8]. Here we show that in the last decade, politicians’ concept of truth has undergone a distinct shift, with authentic but evidence-free belief-speaking becoming more prominent and more diﬀerentiated from evidence-based truth seeking. We analyze communications by members of the U.S. Congress on Twitter between 2011 and 2022 and show that political speech has fractured into two distinct components related to belief-speaking and evidence-based truth-seeking, respectively, and that belief-speaking is related to spreading of untrustworthy information. We show that in tweets by conservative members of Congress, an increase in belief-speaking of 10% is associated with a decrease of 6.8 points of quality (using the NewsGuard scoring system) in the sources shared in a tweet. In addition, we ﬁnd that an increase of belief-speaking language by 10% in the shared articles themselves is associated with a drop in NewsGuard score of 4.3 points for members of both parties. By contrast, increase in truth-seeking language is associated with a slight increase in quality of sources. The results support the hypothesis that the current ﬂood of misinformation in political discourse is in part driven by a new understanding of truth and honesty that has replaced reliance on evidence with the invocation of subjective belief.
SINDy for delay-differential equations: application to model bacterial zinc response

We extend the data-driven method of sparse identification of nonlinear dynamics (SINDy) developed by Brunton et al., Proc. Natl Acad. Sci. USA 113 (2016) to the case of delay differential equations (DDEs). This is achieved in a bilevel optimization procedure by first applying SINDy for fixed delay and then subsequently optimizing the error of the reconstructed SINDy model over delay times. We test the SINDy-delay method on a noisy short dataset from a toy DDE and show excellent agreement. We then apply the method to experimental data of gene expressions in the bacterium Pseudomonas aeruginosa subject to the influence of zinc. The derived SINDy model suggests that the increase in zinc concentration mainly affects the time delay and not the strengths of the interactions between the different agents controlling the zinc export mechanism.
Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer

Artificial intelligence (AI) models have been developed for predicting clinically relevant biomarkers, including microsatellite instability (MSI), for colorectal cancers (CRC). However, the current deep-learning networks are data-hungry and require large training datasets, which are often lacking in the medical domain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that only required relatively small datasets, but achieved the state-of-the-art (SOTA) predictive performance. Our Swin-T workflow not only substantially outperformed published models in an intra-study cross-validation experiment using TCGA-CRC-DX dataset (N = 462), but also showed excellent generalizability in cross-study external validation and delivered a SOTA AUROC of 0.90 for MSI using the MCO dataset for training (N = 1065) and the same TCGA-CRC-DX for testing. Similar performance (AUROC = 0·91) was achieved by Elche and colleagues using ~8000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient using small training datasets and exhibits robust predictive performance with only 200 – 500 training samples. These data indicate that Swin-T may be 5–10 times more efficient than the current state-of-the-art algorithms for MSI based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models showed promise as pre-screening tests for MSI status and BRAF mutation status, which could exclude and reduce the samples before the subsequent standard testing in a cascading diagnostic workflow to allow turnaround time reduction and cost saving.
Dynamic percolation of electric conductivity and a trend towards fractal skeletal structuring in a random ensemble of magnetized nanodust

Numerical modeling of electrodynamic aggregation is carried out for a random ensemble of magnetized nanodust taken as a many body system of strongly magnetized thin rods (i.e., one-dimensional static magnetic dipoles), which possess electric conductivity and static electric charge, screened with its own static plasma sheath. The self-assembling of quasi-linear filaments from an ensemble of randomly situated basic blocks and the electric short-circuiting between biased electrodes are shown to be supported by the alignment of blocks in an external magnetic field. Statistical analysis of short-circuiting time allows tracing the dynamic percolation of electric conductivity and shows a decrease of percolation threshold for volume fraction, as compared with the observed percolation of carbon nanotubes in liquids and polymer composites. Modeling of short-circuiting stage of evolution is continued with tracing the dynamics of pinching of electric current filaments to show the interplay of all the magnetic and electric mechanisms of filaments networking. A trend towards a fractal skeletal structuring (namely, repeat of original basic block at a larger length scale) is illustrated with the evidence for generation of a bigger magnetic dipole.
Coherence behavior of strongly coupled bosonic modes

We study the effect of the intermode coupling in the generation of coherence when two bosonic modes are bilinearly coupled. We consider the case for which the two modes are weakly coupled and the rotating-wave approximation (RWA) applies and the case for which they are strongly coupled and the (RWA) does not apply. Then, in the regime of validity of (RWA), there is no coherence generation solely due to squeezing effects, which means there is an exchange of excitation between the modes and negligible squeezing. On the other hand, if the two modes are strongly coupled coherence is generated by the squeezing interaction. For the system of two bosonic modes weakly coupled with a Markovian bath at temperature $T$ the coherence decreases with $T$. In general, when both kinds of couplings (exchange of excitation and squeezing) are turned on the exchange of excitation contributes to generating more coherence in comparison with a purely squeezing coupling. Thus, the coherence decreases more slowly with the temperature when both couplings are present. Finally, we explore the case when only one of the two coupled modes interacts with a Markovian bath while the other one remains free from the environment. We observe that the intermode coupling induces oscillations in the coherence and fidelity dynamics similar to the behavior for the coupling with a non-Markovian environment.
Controlled quantized adiabatic transport in a superlattice Wannier-Stark ladder

The Born–Fock theorem is one of the most fundamental theorems of quantum mechanics and forms the basis for reliable and efficient navigation in the Hilbert space of a quantum system with a time-dependent Hamiltonian by adiabatic evolution. In the absence of level crossings, i.e. without degeneracies, and under adiabatic time evolution all eigenstates of the Hamiltonian keep their energetic order, labeled by a conserved integer quantum number. Thus, controlling the eigenstates of the Hamiltonian and their energetic order in asymptotic limits allows one to engineer a perfect adiabatic transfer between a large number of initial and target states. The fidelity of the state transfer is only limited by adiabaticity and the selection of target states is controlled by the integer invariant labeling the order of eigenstates. We show here, for the example of a finite superlattice Wannier-Stark ladder, i.e. a one-dimensional lattice with alternating hopping amplitudes and constant potential gradient, that such an adiabatic control of eigenstates can be used to induce perfectly quantized single-particle transport across a pre-determined number of lattice sites. We dedicate this paper to the memory of our late friend and colleague Bruce Shore, who was an expert in adiabatic processes and taught us much about this field.
A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost

Energy supply security is a significant part of China’s security, directly influencing national security and economic and social sustainability. To ensure both China’s present and the future energy supply, it is essential to evaluate and forecast the energy supply level. However, forecasting the energy supply security level is difficult because energy supply security is dynamic, many factors affect it and there is a lack of accurate and comprehensive data. Therefore, based on previous studies and according to the characteristics of energy supply and the social development of China, first, the authors apply a comprehensive evaluation method to quantify the energy supply security. Second, based on the ARIMA-XGBoost hybrid model, the authors create two novel approaches for forecasting the energy supply security level of China. The authors find that: (1) energy supply security is dynamic, and green development has become the theme of China’s energy development. The energy industry urgently needs to provide more high-quality ecological energy products to meet the people’s desire for a beautiful ecological environment; (2) since the mean absolute percentage errors are below 4.5% when forecasting the energy supply security indicators, the ARIMA-XGBoost hybrid model is more accurate for forecasting China’s energy supply security level and (3) the security level of China’s energy supply has developed periodic features; the ESSI can improve by about 0.2 every five years, but, due to the low starting point and multiple types of constraints, it is difficult to reach the safety level in a short time.
The Value of Out-of-Distribution Data

We expect the generalization error to improve with more samples from a similar task, and to deteriorate with more samples from an out-of-distribution (OOD) task. In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples. As the number of OOD samples increases, the generalization error on the target task improves before deteriorating beyond a threshold. In other words, there is value in training on small amounts of OOD data. We use Fisher's Linear Discriminant on synthetic datasets and deep networks on computer vision benchmarks such as MNIST, CIFAR-10, CINIC-10, PACS and DomainNet to demonstrate and analyze this phenomenon. In the idealistic setting where we know which samples are OOD, we show that these non-monotonic trends can be exploited using an appropriately weighted objective of the target and OOD empirical risk. While its practical utility is limited, this does suggest that if we can detect OOD samples, then there may be ways to benefit from them. When we do not know which samples are OOD, we show how a number of go-to strategies such as data-augmentation, hyper-parameter optimization, and pre-training are not enough to ensure that the target generalization error does not deteriorate with the number of OOD samples in the dataset.
Derandomizing Directed Random Walks in Almost-Linear Time

In this article, we present the first deterministic directed Laplacian L systems solver that runs in time almost-linear in the number of non-zero entries of L. Previous reductions imply the first deterministic almost-linear time algorithms for computing various fundamental quantities on directed graphs including stationary distributions, personalized PageRank, hitting times and escape probabilities.We obtain these results by introducing partial symmetrization, a new technique that makes the Laplacian of an Eulerian directed graph “less directed” in a useful sense, which may be of independent interest. The usefulness of this technique comes from two key observations: Firstly, the partially symmetrized Laplacian preconditions the original Eulerian Laplacian well in Richardson iteration, enabling us to construct a solver for the original matrix from a solver for the partially symmetrized one. Secondly, the undirected structure in the partially symmetrized Laplacian makes it possible to sparsify the matrix very crudely, i.e. with large spectral error, and still show that Richardson iterations convergence when using the sparsified matrix as a preconditioner. This allows us to develop deterministic sparsification tools for the partially symmetrized Laplacian.Together with previous reductions from directed Laplacians to Eulerian Laplacians, our technique results in the first deterministic almost-linear time algorithm for solving linear equations in directed Laplacians. To emphasize the generality of our new technique, we show that two prominent existing (randomized) frameworks for solving linear equations in Eulerian Laplacians can be derandomized in this way: the squaring-based framework of [1] and the sparsified Cholesky-based framework of [2].
Am I Private and If So, how Many?: Communicating Privacy Guarantees of Differential Privacy with Risk Communication Formats

Every day, we have to decide multiple times, whether and how much personal data we allow to be collected. This decision is not trivial, since there are many legitimate and important purposes for data collection, for examples, the analysis of mobility data to improve urban traffic and transportation. However, often the collected data can reveal sensitive information about individuals. Recently visited locations can, for example, reveal information about political or religious views or even about an individual's health. Privacy-preserving technologies, such as differential privacy (DP), can be employed to protect the privacy of individuals and, furthermore, provide mathematically sound guarantees on the maximum privacy risk. However, they can only support informed privacy decisions, if individuals understand the provided privacy guarantees. This article proposes a novel approach for communicating privacy guarantees to support individuals in their privacy decisions when sharing data. For this, we adopt risk communication formats from the medical domain in conjunction with a model for privacy guarantees of DP to create quantitative privacy risk notifications. We conducted a crowd-sourced study with 343 participants to evaluate how well our notifications conveyed the privacy risk information and how confident participants were about their own understanding of the privacy risk. Our findings suggest that these new notifications can communicate the objective information similarly well to currently used qualitative notifications, but left individuals less confident in their understanding. We also discovered that several of our notifications and the currently used qualitative notification disadvantage individuals with low numeracy: these individuals appear overconfident compared to their actual understanding of the associated privacy risks and are, therefore, less likely to seek the needed additional information before an informed decision. The promising results allow for multiple directions in future research, for example, adding visual aids or tailoring privacy risk communication to characteristics of the individuals.