Miracle-dz/UniFAR-FTUs huggingface dataset:
Topology and Geometry of the Third-Party Domains Ecosystem

Over the years, web content has evolved from simple text and static images hosted on a single server to a complex, interactive and multimedia-rich content hosted on different servers. As a result, a modern website during its loading time fetches content not only from its owner's domain but also from a range of third-party domains providing additional functionalities and services. Here, we infer the network of the third-party domains by observing the domains' interactions within users' browsers from all over the globe. We find that this network possesses structural properties commonly found in complex networks, such as power-law degree distribution, strong clustering, and small-world property. These properties imply that a hyperbolic geometry underlies the ecosystem's topology. We use statistical inference methods to find the domains' coordinates in this geometry, which abstract how popular and similar the domains are. The hyperbolic map we obtain is meaningful, revealing the large-scale organization of the ecosystem. Furthermore, we show that it possesses predictive power, providing us the likelihood that third-party domains are co-hosted; belong to the same legal entity; or merge under the same entity in the future in terms of company acquisition. We also find that complementarity instead of similarity is the dominant force driving future domains' merging. These results provide a new perspective on understanding the ecosystem's organization and performing related inferences and predictions.
Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor

The step length is an important parameter in gait analysis. Long-term monitoring applications for gait analysis are often based on inertial measurement units (IMUs) due to their low-cost and unobtrusive nature. Spatial gait parameters, such as step or stride length, are therefore not directly accessible. In this contribution, we focus on model-based algorithms for step length estimation based on a pendant-integrated IMU during slow walking speeds. We present a model-based approach to estimate the step length, which is divided into two successive steps. As the first part of our approach, we present an algorithm for estimation of the vertical displacement of the center of mass (CoM) during gait. Based on this estimate, we present a novel approach to estimate the step length, which we have deduced from a previously published, simplified gait model. The algorithm is compared to a commonly known approach for accelometry-based step length prediction and validated against reference data obtained from a force plate-integrated treadmill for gait analysis during a clinical study with ten healthy subjects. Due to the applicability to gait stability assessment in elderly or gait impaired patients, we focus on slow walking speeds (1−4 km h−1). The presented algorithms outperform the existing approach and the proposed model calculations provide a more accurate prediction. For the vertical displacement, we achieved a precision of 9.3% (CoV) with an RMSE of 1.5 mm in terms of the trajectory amplitude during normal gait patterns. The step length estimation yields satisfying results with a relative prediction error of lower than 10% for walking speeds of 2−4kmh−1.
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing

We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral distribution, this model is well suited for capturing complex correlation structures which often arise in applications. We propose a novel family of approximate message passing (AMP) algorithms for signal estimation, and rigorously characterize their performance in the high-dimensional limit via a state evolution recursion. Our rotationally invariant AMP has complexity of the same order as the existing AMP derived under the restrictive assumption of a Gaussian design; our algorithm also recovers this existing AMP as a special case. Numerical results showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal in some settings), but obtained with a much lower complexity, as the proposed algorithm does not require a computationally expensive singular value decomposition.
Cavity-Enhanced Vernier Spectroscopy with a Chip-Scale Mid-Infrared Frequency Comb

Chip-scale optical frequency combs can provide broadband spectroscopy for diagnosing complex organic molecules. They are also promising as miniaturized laser spectrometers in applications ranging from atmospheric chemistry to geological science and the search for extraterrestrial life. While optical cavities are commonly used to boost sensitivity, it is challenging to realize a compact cavity-enhanced comb-based spectrometer. Here, we apply the Vernier technique to free-running operation of an interband cascade laser frequency comb in a simple linear geometry that performs cavity-enhanced chemical sensing. A centimeter-scale high-finesse cavity simultaneously provides selective mode filtering and enhancement of the path length to 30 meters. As a proof-of-concept, we sense transient open-path releases of ppm-level difluoroethane with 2 ms temporal resolution over a 1 THz optical bandwidth centered at 3.64 $\mu$m.
JAB1 deletion in oligodendrocytes causes senescence-induced inflammation and neurodegeneration in mice

Oligodendrocytes are the primary target of demyelinating disorders, and progressive neurodegenerative changes may evolve in the CNS. DNA damage and oxidative stress are considered key pathogenic events, but the underlying molecular mechanisms remain unclear. Moreover, animal models do not fully recapitulate human diseases, complicating the path to effective treatments. Here we report that mice with cell-autonomous deletion of the nuclear COP9 signalosome component CSN5 (JAB1) in oligodendrocytes develop DNA damage and defective DNA repair in myelinating glial cells. Interestingly, oligodendrocytes lacking JAB1 expression underwent a senescence-like phenotype that fostered chronic inflammation and oxidative stress. These mutants developed progressive CNS demyelination, microglia inflammation, and neurodegeneration, with severe motor deficits and premature death. Notably, blocking microglia inflammation did not prevent neurodegeneration, whereas the deletion of p21CIP1 but not p16INK4a pathway ameliorated the disease. We suggest that senescence is key to sustaining neurodegeneration in demyelinating disorders and may be considered a potential therapeutic target.
A low power test pattern generation for built-in self-test based circuits

A low-power test pattern generation, named the low switching activity test pattern generation (LSA-TPG), is proposed to reduce the power dissipation of built-in self-test (BIST)-based circuits during test. A single input changing (SIC) test pattern is generated by a counter and a Gray encoder which is called the SICG (single input changing generator). The built-in test vectors are generated by the SIC patterns which are exclusive-ORed with seeds generated by the modified linear feedback shift register (LFSR). All the test vectors are SIC patterns during the 2m test clock period; thus the switching activities of the test vectors are greatly reduced in test mode without compromising fault coverage. The proposed structure has the advantages of low test power and low hardware overhead. LSA-TPG is independent of circuit under test (CUT) and flexible enough to be used in both BIST and scan-based BIST architectures. The proposed architecture increases the correlation among the test patterns with negligible impact on test length. Experiments conducted on ISCAS'89 benchmark circuits demonstrate that the proposed scheme gives better fault coverage with a large reduction in test power dissipation.
Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection

Copy detection, which is a task to determine whether an image is a modified copy of any image in a database, is an unsolved problem. Thus, we addressed copy detection by training convolutional neural networks (CNNs) with contrastive learning. Training with a large memory-bank and hard data augmentation enables the CNNs to obtain more discriminative representation. Our proposed negative embedding subtraction further boosts the copy detection accuracy. Using our methods, we achieved 1st place in the Facebook AI Image Similarity Challenge: Descriptor Track. Our code is publicly available here: \url{https://github.com/lyakaap/ISC21-Descriptor-Track-1st}
Designing a Real-Time IoT Data Streaming Testbed for Horizontally Scalable Analytical Platforms: Czech Post Case Study

There is a growing trend for enterprise-level Internet of Things (IoT) applications requiring real-time horizontally scalable data processing platforms. Real-time processing platforms receiving data streams from sensor networks (e.g., autonomous and connected vehicles, smart security for businesses and homes, smartwatches, fitness trackers, and other wearables) require distributed MQTT brokers. This case study presents an IoT data streaming testbed platform prepared for the Czech Post. The presented platform has met the throughput requirement of 2 million messages per 24 hours (comprising SMS and emails). The tested MQTT broker runs on a single virtual node of a horizontally scalable testbed platform. Soon the Czech Post will modernise its eServices to increase package deliveries aligned with eCommerce and eGovernment demands. The presented testbed platform fulfils all requirements, and it is also capable of processing thousands of messages per second. The presented platform and concepts are transferable to healthcare systems, transport operations, the automotive industry, and other domains such as smart cities.
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization

Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick-based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial-and-error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high-dimensional space according to the inputs. Furthermore, the online computational efficiency improves by approximately 100 times compared with the conventional KPCA. With the Tennessee Eastman (TE) process benchmark, the effectiveness and superiority of the proposed method is illustrated.
Greedy-based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning

Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decomposition (MVD) suffer from relative overgeneralization. As a result, they can not ensure optimal consistency (i.e., the correspondence between individual greedy actions and the maximal true Q value). In this paper, we derive the expression of the joint Q value function of LVD and MVD. According to the expression, we draw a transition diagram, where each self-transition node (STN) is a possible convergence. To ensure optimal consistency, the optimal node is required to be the unique STN. Therefore, we propose the greedy-based value representation (GVR), which turns the optimal node into an STN via inferior target shaping and further eliminates the non-optimal STNs via superior experience replay. In addition, GVR achieves an adaptive trade-off between optimality and stability. Our method outperforms state-of-the-art baselines in experiments on various benchmarks. Theoretical proofs and empirical results on matrix games demonstrate that GVR ensures optimal consistency under sufficient exploration.
Principle and application of ball surface acoustic wave (SAW) sensor

Detection of hydrogen gas is a crucial task for establishing safety and reliability of fuel cells, a key technology for the environment and our society. However, hydrogen is difficult to detect and various hydrogen sensors have many drawbacks. Here we report a novel hydrogen gas sensor, the ball surface acoustic wave (SAW) sensor, using Pd or PdNi sensitive film. The ball SAW sensor is based on a novel phenomenon, diffraction-free propagation of collimated beam along an equator of sphere. The resultant ultra-multiple roundtrips of SAW makes it possible to achieve highest sensitivity among SAW sensors. Moreover, it enables to use a very thin sensitive film, and consequently the shortest response time (2 s) was realized. In terms of the sensing range, it has the widest range of 10 ppm to 100% among any hydrogen sensors including FET or resistivity sensors. The response time was less than 1 s for 3.0% hydrogen concentration in nitrogen gas, evaluated by using a newly developed digital quadrature detector.
Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles

Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks such as suturing can lead to surgeon fatigue, motivating the automation of suturing. As visual tracking of a thin reflective needle is extremely challenging, prior work has modified the needle with nonreflective contrasting paint. As a step towards automation of a suturing subtask without modifying the needle, we propose HOUSTON: Handover of Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that uses a learned active sensing policy with a stereo camera to iteratively localize and align the needle into a visible and accessible pose for the other gripper. To compensate for robot positioning and needle perception errors, the algorithm then executes a high-precision grasping motion that uses multiple cameras. Physical experiments with the da Vinci Research Kit (dVRK) suggest a success rate of 96.7% on needles used in training, and 75 - 92.9% on needles unseen in training. On sequential handovers, HOUSTON successfully executes 32.4 handovers on average before failure. To our knowledge, this work is the first to study handover of unmodified surgical needles. See https: / /tinyurl. com/houston-surgery for additional materials including details about offline datasets and model architectures.
Search for Intermediate-mass Black Holes at Low Redshift with Intra-night Variability

We present a sample of intermediate-mass black hole (IMBH) candidates based on the detection of a broad Hα emission line and variability, which are selected from the Sloan Digital Sky Survey Data Release 7. By performing spectral decomposition of emission lines as well as a visual inspection, we initially identified 131 targets with a broad Hα line among a large sample of emission line galaxies. We further selected 25 IMBH candidates whose estimated black hole mass (M BH) is less than 106 M ⊙. To constrain the nature of these candidates, we analyzed X-ray properties and performed an intra-night variability monitoring campaign with optical telescopes. Based on the optical variability analysis, we report a sample of 11 targets with detected intra-night variability as the best IMBH candidates, which are suitable for follow-up observations for accurate M BH determination such as reverberation mapping campaigns.
Computationally efficient models for quantization effects in MOS electron and hole accumulation layers

In this paper, models appropriate for device simulators are developed which account for the quantum mechanical nature of accumulated regions. Accumulation layer quantization is important in deep submicron (/spl les/0.25 /spl mu/m) MOS devices in the overlapped source/drain extension regions, in accumulation mode SOI devices, and in buried-channel PMOS structures. Computationally efficient models suitable for routine device simulation are presented that predict the reduction of the accumulated net electron (hole) sheet charge when quantization of the electron (hole) accumulation region is accounted for. The results of comparisons with self-consistent simulations support the validity of these models. In addition, simulation results will be shown which illustrate that when inversion layer quantum mechanical effects are modeled, it is also necessary to account for accumulation layer quantum mechanical effects in order to obtain more physically accurate as well as numerically stable solutions.
The Galaxy Starburst/Main-sequence Bimodality over Five Decades in Stellar Mass at z ≈ 3–6.5

We study the relation between stellar mass (M *) and star formation rate (SFR) for star-forming galaxies over approximately five decades in stellar mass ( 5.5≲log10(M*/M⊙)≲10.5 ) at z ≈ 3–6.5. This unprecedented coverage has been possible thanks to the joint analysis of blank non-lensed fields (COSMOS/SMUVS) and cluster lensing fields (Hubble Frontier Fields) that allow us to reach very low stellar masses. Previous works have revealed the existence of a clear bimodality in the SFR–M * plane with a star formation Main Sequence and a starburst cloud at z ≈ 4–5. Here we show that this bimodality extends to all star-forming galaxies and is valid in the whole redshift range z ≈ 3–6.5. We find that starbursts constitute at least ≈20% of all star-forming galaxies with M * ≳ 109 M ⊙ at these redshifts and reach a peak of 40% at z = 4–5. More importantly, 60%–90% of the total SFR budget at these redshifts is contained in starburst galaxies, indicating that the starburst mode of star formation is dominant at high redshifts. Almost all the low stellar mass starbursts with log10(M*/M⊙)≲8.5 have ages comparable to the typical timescales of a starburst event, suggesting that these galaxies are being caught in the process of formation. Interestingly, galaxy formation models fail to predict the starburst/main-sequence bimodality and starbursts overall, suggesting that the starburst phenomenon may be driven by physical processes occurring at smaller scales than those probed by these models.
Estimating the Ages of Open Star Clusters from Properties of Their Extended Tidal Tails

The most accurate current methods for determining the ages of open star clusters, stellar associations, and stellar streams are based on isochrone fitting or the lithium depletion boundary. We propose another method for dating these objects based on the morphology of their extended tidal tails, which have been recently discovered around several open star clusters. Assuming that the tidal tails originate from the stars released from the cluster during early gas expulsion, or that they form in the same star-forming region as the cluster (i.e., being coeval with the cluster), we derive the analytical formula for the tilt angle β between the long axis of the tidal tail and the orbital direction for clusters or streams on circular trajectories. Since at a given Galactocentric radius β is only a function of age t regardless of the initial properties of the cluster, we estimate the cluster age by inverting the analytical formula β = β(t). We illustrate the method on a sample of 12 objects, which we compiled from the literature, and we find a reasonable agreement with previous dating methods in ≈70% of the cases. This can probably be improved by taking into account the eccentricity of the orbits and by revisiting the dating methods based on stellar evolution. The proposed morphological method is suitable for relatively young clusters (age ≲300 Myr), where it provides a relative age error of the order of 10%−20% for an error in the observed tilt angle of 5°.
Assessment of high‐order IMEX methods for incompressible flow

This paper investigates the competitiveness of semi‐implicit Runge‐Kutta (RK) and spectral deferred correction (SDC) time‐integration methods up to order six for incompressible Navier‐Stokes problems in conjunction with a high‐order discontinuous Galerkin method for space discretization. It is proposed to harness the implicit and explicit RK parts as a partitioned scheme, which provides a natural basis for the underlying projection scheme and yields a straight‐forward approach for accommodating nonlinear viscosity. Numerical experiments on laminar flow, variable viscosity and transition to turbulence are carried out to assess accuracy, convergence and computational efficiency. Although the methods of order 3 or higher are susceptible to order reduction due to time‐dependent boundary conditions, two third‐order RK methods are identified that perform well in all test cases and clearly surpass all second‐order schemes including the popular extrapolated backward difference method. The considered SDC methods are more accurate than the RK methods, but become competitive only for relative errors smaller than ca 10−5$$ 1{0}^{-5} $$ .
Hysteresis and jumps in the I-V curves of disordered two-dimensional materials

The superconductor-insulator transition (SIT) in thin-ﬁlm disordered superconductors is a hallmark example of a quantum phase transition. Despite being observed more than 30 years ago, its nature is still under a lively debate. One intriguing observations concerns the insulating side of the transition, which exhibits some unusual properties. Among them is its current-voltage relation ( I − V curve), which includes (i) a conductance that changes abruptly by several orders of magnitude with increasing voltage, (ii) hysteretic behavior, and (iii) multiple (sometimes more than 100) smaller current jumps near the transition. Some models have been suggested before, but no model has been successful in accounting for the observed behavior in full. One commonly used approach is to model the disordered sample as a two-dimensional array of conducting islands, where charge carriers tunnel from one island to its neighbors. In those models, fast relaxation is assumed, and the system is treated as always being in electrostatic and thermal equilibrium. Those models are successful in explaining some measurement results, including the phase transition itself, but they fail to reproduce hysteresis in their predicted I − V curves. Here, we suggest incorporating ﬁnite relaxation time into an array model. We show that, in the slow relaxation limit, our model can reproduce hysteresis and multiple jumps in the I − V curve. Based on our results, we argue that a similar behavior should also be observed in two-dimensional normal (nonsuperconducting) arrays. This claim is supported by past observations. We analyze the role of diﬀerent parameters in our model, determine the range of relevant timescales in the problem, and compare our results with selected measurements.
Physics-based Mesh Deformation with Haptic Feedback and Material Anisotropy

We present a physics-based framework to simulate porous, deformable materials and interactive tools with haptic feedback that can reshape it. In order to allow the material to be moulded non-homogeneously, we propose an algorithm to change the material properties of the object depending on its water content. We present a multi-resolution, multi-timescale simulation framework to enable stable visual and haptic feedback at interactive rates. We test our model for physical consistency, accuracy, interactivity and appeal through a user study and quantitative performance evaluation.