Miracle-dz/UniFAR-FTUs huggingface dataset:
Periodic bouncing of a plasmonic bubble in a binary liquid by competing solutal and thermal Marangoni forces

Significance Plasmonic bubbles are relevant in biomedical diagnosis and therapy, catalysis, and micro- and nanomanipulation. Hitherto, their behavior has mainly been analyzed in pure liquids. However, in general, these applications of plasmonic bubbles take place in binary or multicomponent liquids. Here we show that plasmonic bubbles in a binary liquid display surprising phenomena, resulting from the emerging complicated physicochemical hydrodynamics of a vapor bubble in such binary liquids. Depending on the application, these phenomena can be either a benefit or a hindrance. Our results are also relevant for other bubble nucleation processes in multicomponent liquids, such as boiling, electrolysis, or catalysis. The physicochemical hydrodynamics of bubbles and droplets out of equilibrium, in particular with phase transitions, display surprisingly rich and often counterintuitive phenomena. Here we experimentally and theoretically study the nucleation and early evolution of plasmonic bubbles in a binary liquid consisting of water and ethanol. Remarkably, the submillimeter plasmonic bubble is found to be periodically attracted to and repelled from the nanoparticle-decorated substrate, with frequencies of around a few kilohertz. We identify the competition between solutal and thermal Marangoni forces as the origin of the periodic bouncing. The former arises due to the selective vaporization of ethanol at the substrate’s side of the bubble, leading to a solutal Marangoni flow toward the hot substrate, which pushes the bubble away. The latter arises due to the temperature gradient across the bubble, leading to a thermal Marangoni flow away from the substrate, which sucks the bubble toward it. We study the dependence of the frequency of the bouncing phenomenon from the control parameters of the system, namely the ethanol fraction and the laser power for the plasmonic heating. Our findings can be generalized to boiling and electrolytically or catalytically generated bubbles in multicomponent liquids.
Signatures of hierarchical mergers in black hole spin and mass distribution

Recent gravitational wave (GW) observations by LIGO/Virgo show evidence for hierarchical mergers, where the merging BHs are the remnants of previous BH merger events. These events may carry important clues about the astrophysical host environments of the GW sources. In this paper, we present the distributions of the effective spin parameter (χeff), the precession spin parameter (χp), and the chirp mass (mchirp) expected in hierarchical mergers. Under a wide range of assumptions, hierarchical mergers produce (i) a monotonic increase of the average of the typical total spin for merging binaries, which we characterize with $\scriptstyle{{\bar{\chi }}_\mathrm{typ}\equiv \overline{(\chi _\mathrm{eff}^2+\chi _\mathrm{p}^2)^{1/2}}}$, up to roughly the maximum mchirp among first-generation (1g) BHs, and (ii) a plateau at ${\bar{\chi }}_\mathrm{typ}\sim 0.6$ at higher mchirp. We suggest that the maximum mass and typical spin magnitudes for 1g BHs can be estimated from ${\bar{\chi }}_\mathrm{typ}$ as a function of mchirp. The GW data observed in LIGO/Virgo O1–O3a prefers an increase in ${\bar{\chi }}_\mathrm{typ}$ at low mchirp, which is consistent with the growth of the BH spin magnitude by hierarchical mergers at ∼2σ confidence. A Bayesian analysis using the χeff, χp, and mchirp distributions suggests that 1g BHs have the maximum mass of ∼15–$30\, {\rm M}_\odot$ if the majority of mergers are of high-generation BHs (not among 1g–1g BHs), which is consistent with mergers in active galactic nucleus discs and/or nuclear star clusters, while if mergers mainly originate from globular clusters, 1g BHs are favoured to have non-zero spin magnitudes of ∼0.3. We also forecast that signatures for hierarchical mergers in the ${\bar{\chi }}_\mathrm{typ}$ distribution can be confidently recovered once the number of GW events increases to ≳ O(100).
Nanomechanical probing and strain tuning of the Curie temperature in suspended Cr$_2$Ge$_2$Te$_6$ heterostructures

Two-dimensional (2D) magnetic materials with strong magnetostriction are interesting systems for strain-tuning the magnetization, enabling potential for realizing spintronic and nanomagnetic devices. Realizing this potential requires understanding of the magneto-mechanical coupling in the 2D limit. In this work, we suspend thin Cr$_2$Ge$_2$Te$_6$ layers, creating nanomechanical membrane resonators. We probe its mechanical and magnetic properties as a function of temperature and strain. Pronounced signatures of magneto-elastic coupling are observed in the temperature-dependent resonance frequency of these membranes near $T_{\rm C}$. We further utilize Cr$_2$Ge$_2$Te$_6$ in heterostructures with thin layers of WSe$_2$ and FePS$_3$, which have positive thermal expansion coefficients, to compensate the negative thermal expansion coefficient of Cr$_2$Ge$_2$Te$_6$ and quantitatively probe the corresponding $T_{\rm C}$. Finally, we induce a strain of $0.016\%$ in a suspended heterostructure via electrostatic force and demonstrate a resulting enhancement of $T_{\rm C}$ by $2.5 \pm 0.6$ K in the absence of an external magnetic field.
Joint Online Learning and Decision-making via Dual Mirror Descent

We consider an online revenue maximization problem over a finite time horizon subject to lower and upper bounds on cost. At each period, an agent receives a context vector sampled i.i. d. from an unknown distribution and needs to make a decision adaptively. The revenue and cost functions depend on the context vector as well as some fixed but possibly unknown parameter vector to be learned. We propose a novel offline benchmark and a new algorithm that mixes an online dual mirror descent scheme with a generic parameter learning process. When the parameter vector is known, we demonstrate an $O(\sqrt{T})$ regret result as well an $O(\sqrt{T})$ bound on the possible constraint violations. When the parameter is not known and must be learned, we demonstrate that the regret and constraint violations are the sums of the previous $O(\sqrt{T})$ terms plus terms that directly depend on the convergence of the learning process.
CoDR: Computation and Data Reuse Aware CNN Accelerator

Computation and Data Reuse is critical for the resource-limited Convolutional Neural Network (CNN) accelerators. This paper presents Universal Computation Reuse to exploit weight sparsity, repetition, and similarity simultaneously in a convolutional layer. Moreover, CoDR decreases the cost of weight memory access by proposing a customized Run-Length Encoding scheme and the number of memory accesses to the intermediate results by introducing an input and output stationary dataflow. Compared to two recent compressed CNN accelerators with the same area of 2.85 mm^2, CoDR decreases SRAM access by 5.08x and 7.99x, and consumes 3.76x and 6.84x less energy.
On Generating and Labeling Network Traffic with Realistic, Self-Propagating Malware

—Research and development of techniques which detect or remediate malicious network activity require access to diverse, realistic, contemporary data sets containing labeled ma- licious connections. In the absence of such data, said techniques cannot be meaningfully trained, tested, and evaluated. Synthet- ically produced data containing fabricated or merged network trafﬁc is of limited value as it is easily distinguishable from real trafﬁc by even simple machine learning (ML) algorithms. Real network data is preferable, but while ubiquitous is broadly both sensitive and lacking in ground truth labels, limiting its utility for ML research. This paper presents a multi-faceted approach to generating a data set of labeled malicious connections embedded within anonymized network trafﬁc collected from large production networks. Real-world malware is defanged and introduced to simulated, secured nodes within those networks to generate realistic trafﬁc while maintaining sufﬁcient isolation to protect real data and infrastructure. Network sensor data, including this embedded malware trafﬁc, is collected at a network edge and anonymized for research use. Network trafﬁc was collected and produced in accordance with the aforementioned methods at two major educational institutions. The result is a highly realistic, long term, multi- institution data set with embedded data labels spanning over 1.5 trillion connections and over a petabyte of sensor log data. The usability of this data set is demonstrated by its utility to our artiﬁcial intelligence and machine learning (AI/ML) research program.
Geometric Methods for Efficient Planar Swimming of Copepod Nauplii

Copepod nauplii are larval crustaceans with important ecological functions. Due to their small size, they experience an environment of low Reynolds number within their aquatic habitat. Here we provide a mathematical model of a swimming copepod nauplius with two legs moving in a plane. This model allows for both rotation and two-dimensional displacement by the periodic deformation of the swimmer’s body. The system is studied from the framework of optimal control theory, with a simple cost function designed to approximate the mechanical energy expended by the copepod. We find that this model is sufficiently realistic to recreate behavior similar to those of observed copepod nauplii, yet much of the mathematical analysis is tractable. In particular, we show that the system is controllable, but there exist singular configurations where the degree of non-holonomy is non-generic. We also partially characterize the abnormal extremals and provide explicit examples of families of abnormal curves. Finally, we numerically simulate normal extremals and observe some interesting and surprising phenomena.
FLAW3D: A Trojan-Based Cyber Attack on the Physical Outcomes of Additive Manufacturing

Additive manufacturing (AM) systems such as 3-D printers use inexpensive microcontrollers that rarely feature cybersecurity defenses. This is a risk, especially given the rising threat landscape within the larger digital manufacturing domain. In this work, we demonstrate this risk by presenting the design and study of a malicious Trojan (the FLAW3D bootloader) for AVR-based Marlin-compatible 3-D printers ($>$100 commercial models). We show that the Trojan can hide from programming tools, and even within tight design constraints (less than 1.7 KB in size), it can compromise the quality of additively manufactured prints and reduce tensile strengths by up to 50%.
Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer’s Disease

Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has mainly focused on point representations, meshes also contain connectivity information and are therefore a more comprehensive characterization of the underlying anatomical surface. In this work, we evaluate four recent geometric deep learning approaches that operate on mesh representations. These approaches can be grouped into template-free and template-based approaches, where the template-based methods need a more elaborate pre-processing step with the definition of a common reference template and correspondences. We compare the different networks for the prediction of Alzheimer’s disease based on the meshes of the hippocampus. Our results show advantages for template-based methods in terms of accuracy, number of learnable parameters, and training speed. While the template creation may be limiting for some applications, neuroimaging has a long history of building templates with automated tools readily available. Overall, working with meshes is more involved than working with simplistic point clouds, but they also offer new avenues for designing geometric deep learning architectures.
Axial momentum gains of ions and electrons in magnetic nozzle acceleration

Fully kinetic simulations of magnetic nozzle acceleration were conducted to investigate the axial momentum gains of ions and electrons with electrostatic and Lorentz forces. The axial momentum gains per ion and electron are directly calculated from the kinetics of charged particles, indicating that electrons in the magnetic nozzle obtain the net axial momentum by the Lorentz force, even though they are decelerated by the electrostatic force. Whereas ions are also accelerated by the electrostatic force, the axial momentum gain of electrons increases significantly with increasing magnetic field strength and becomes dominant in the magnetic nozzle. In addition, it is clearly shown that the axial momentum gain of electrons is due to the electron momentum conversion from the radial to the axial direction, resulting in a significant increase in the thrust and exhaust velocity.
Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study

Abstract Aims Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalized risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilize a homogenous set of features and require the presence of a physician. The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments. Methods and results Across 466 052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data. Conclusion We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilized in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.
X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering

Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered attention as a promising technique for enhancing transfer learning under low-resource scenarios: particularly for cross-lingual transfer in Natural Language Understanding (NLU). In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU. Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages. Our analysis reveals that X-METRA-ADA can leverage limited data for faster adaptation.
What is Wrong with One-Class Anomaly Detection?

From a safety perspective, a machine learning method embedded in real-world applications is required to distinguish irregular situations. For this reason, there has been a growing interest in the anomaly detection (AD) task. Since we cannot observe abnormal samples for most of the cases, recent AD methods attempt to formulate it as a task of classifying whether the sample is normal or not. However, they potentially fail when the given normal samples are inherited from diverse semantic labels. To tackle this problem, we introduce a latent class-condition-based AD scenario. In addition, we propose a confidence-based self-labeling AD framework tailored to our proposed scenario. Since our method leverages the hidden class information, it successfully avoids generating the undesirable loose decision region that one-class methods suffer. Our proposed framework outperforms the recent one-class AD methods in the latent multi-class scenarios.
Large Scale Record Linkage in the Presence of Missing Data

Record linkage is aimed at the accurate and efficient identification of records that represent the same entity within or across disparate databases. It is a fundamental task in data integration and increasingly required for accurate decision making in application domains ranging from health analytics to national security. Traditional record linkage techniques calculate string similarities between quasi-identifying (QID) values, such as the names and addresses of people. Errors, variations, and missing QID values can however lead to low linkage quality because the similarities between records cannot be calculated accurately. To overcome this challenge, we propose a novel technique that can accurately link records even when QID values contain errors or variations, or are missing. We first generate attribute signatures (concatenated QID values) using an Apriori based selection of suitable QID attributes, and then relational signatures that encapsulate relationship information between records. Combined, these signatures can uniquely identify individual records and facilitate fast and high quality linking of very large databases through accurate similarity calculations between records. We evaluate the linkage quality and scalability of our approach using large real-world databases, showing that it can achieve high linkage quality even when the databases being linked contain substantial amounts of missing values and errors.
HMS: Hierarchical Modality Selection for Efficient Video Recognition

Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally expensive but also neglects the fact that different videos rely on different modalities for predictions. This paper introduces Hierarchical Modality Selection (HMS), a simple yet efficient multimodal learning framework for efficient video recognition. HMS operates on a low-cost modality, i.e. , audio clues, by default, and dynamically decides on-the-fly whether to use computationally-expensive modalities, including appearance and motion clues, on a per-input basis. This is achieved by the collaboration of three LSTMs that are organized in a hierarchical manner. In particular, LSTMs that operate on high-cost modalities contain a gating module, which takes as inputs lower-level features and historical information to adaptively determine whether to activate its corresponding modality; otherwise it simply reuses historical information. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate the proposed approach can effectively explore multimodal information for improved classification performance while requiring much less computation.
CATE meets ML - Conditional Average Treatment Effect and Machine Learning

For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only the average but a personalized treatment effect - the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains meta-learners, like the Doubly-Robust, R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and 401(k) example, we find a positive treatment effect for all observations but conflicting evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.
Building a Fast Lane for Mg Diffusion in α-MoO3 by Fluorine Doping

Following previous experimental work examining a layered oxyfluoride as cathode material for Mg-ion batteries [Incorvati et al., Chem. Mater. 2016, 28, 17], we study the role of fluorination on the structural and electronic properties of molybdenum trioxide and its impact on Mg intercalation and diffusion using first-principles methods. Although bulk α-MoO3 is a 2D layered compound, we find that it provides 3D channels for Mg diffusion. When F atoms are incorporated into the α-MoO3 lattice, they replace the O atoms sitting at a specific crystallographic site that is linked by two nearest Mo atoms within a single Mo–O layer. As a consequence of F substitution, the local Mo-anion bonds are distorted, which leads to closure of the electronic band gap. From the analysis of zone center phonon vibrational frequencies, it is found that the local Mo-anion bonding strength is weakened by replacing O2– with F–, which ultimately facilitates Mg diffusion through the F-substituted lattice. For example, it is shown tha.
Successful Mechanical Thrombectomy for Basilar Artery Occlusion in a Seven-Year-Old Male

Acute arterial strokes in children are rare but can potentially cause lasting and often permanent neurological deficits. Mechanical thrombectomy has a well-established efficacy and safety profile in adult stroke management, but in the pediatric population, it is yet to be proven efficacious and safe. We present a case of a seven-year-old male who presented with multiple episodes of generalized tonic-clonic seizures after sustaining a neck injury by falling from a trampoline. National Institutes of Health (NIH) on presentation was 21. Neurological exam revealed dilated nonreactive pupils, dysconjugate gaze, severe dysarthria, bilateral ptosis, and movement of upper and lower extremities only to noxious stimuli. Magnetic resonance imaging (MRI) of brain without contrast revealed infarcted areas in the left pons, midbrain, and cerebellar regions. Computed tomographic angiogram (CTA) of head demonstrated left vertebral artery dissection with associated complete occlusion of the distal basilar artery. Successful recanalization was achieved with mechanical thrombectomy six hours after presentation. Mechanical thrombectomy treatment resulted in a significant neurological recovery with NIH of 1. This case supports the growing evidence of the efficacy and safety of mechanical thrombectomy in children.