Miracle-dz/UniFAR-FTUs huggingface dataset:
Indistinguishable photons from deterministically integrated single quantum dots in heterogeneous GaAs/ Si3N4 quantum photonic circuits.

Silicon photonics enables scaling of quantum photonic systems by allowing the creation of extensive, low-loss, reconfigurable networks linking various functional on-chip elements. Inclusion of single quantum emitters onto photonic circuits, acting as on-demand sources of indistinguishable photons or single-photon nonlinearities, may enable large-scale chip-based quantum photonic circuits and networks. Towards this, we use low-temperature $\textit{in situ}$ electron-beam lithography to deterministically produce hybrid GaAs/Si3N4 photonic devices containing single InAs quantum dots precisely located inside nanophotonic structures, which act as efficient, Si3N4 waveguide-coupled on-chip, on-demand single-photon sources. The precise positioning afforded by our scalable fabrication method furthermore allows observation of post-selected indistinguishable photons. This indicates a promising path towards significant scaling of chip-based quantum photonics, enabled by large fluxes of indistinguishable single-photons produced on-demand, directly on-chip.
On Evaluating the Effectiveness of the HoneyBot: A Case Study

In recent years, cyber-physical system (CPS) security as applied to robotic systems has become a popular research area. Mainly because robotics systems have traditionally emphasized the completion of a specific objective and lack security oriented design. Our previous work, HoneyBot \cite{celine}, presented the concept and prototype of the first software hybrid interaction honeypot specifically designed for networked robotic systems. The intuition behind HoneyBot was that it would be a remotely accessible robotic system that could simulate unsafe actions and physically perform safe actions to fool attackers. Unassuming attackers would think they were connected to an ordinary robotic system, believing their exploits were being successfully executed. All the while, the HoneyBot is logging all communications and exploits sent to be used for attacker attribution and threat model creation. In this paper, we present findings from the result of a user study performed to evaluate the effectiveness of the HoneyBot framework and architecture as it applies to real robotic systems. The user study consisted of 40 participants, was conducted over the course of several weeks, and drew from a wide range of participants aged between 18-60 with varying level of technical expertise. From the study we found that research subjects could not tell the difference between the simulated sensor values and the real sensor values coming from the HoneyBot, meaning the HoneyBot convincingly spoofed communications.
Discovery of a Dark, Massive, ALMA-only Galaxy at z ∼ 5–6 in a Tiny 3 mm Survey

We report the serendipitous detection of two 3 mm continuum sources found in deep ALMA Band 3 observations to study intermediate-redshift galaxies in the COSMOS field. One is near a foreground galaxy at 1. ″3, but is a previously unknown dust-obscured star-forming galaxy (DSFG) at probable zCO = 3.329, illustrating the risk of misidentifying shorter wavelength counterparts. The optical-to-millimeter spectral energy distribution (SED) favors a gray λ−0.4 attenuation curve and results in significantly larger stellar mass and SFR compared to a Calzetti starburst law, suggesting caution when relating progenitors and descendants based on these quantities. The other source is missing from all previous optical/near-infrared/submillimeter/radio catalogs (“ALMA-only”), and remains undetected even in stacked ultradeep optical (>29.6 AB) and near-infrared (>27.9 AB) images. Using the ALMA position as a prior reveals faint signal-to-noise ratio ∼ 3 measurements in stacked IRAC 3.6+4.5, ultradeep SCUBA2 850 μm, and VLA 3 GHz, indicating the source is real. The SED is robustly reproduced by a massive M* = 1010.8M⊙ and Mgas = 1011M⊙, highly obscured AV ∼ 4, star-forming SFR ∼ 300 M⊙ yr−1 galaxy at redshift z = 5.5 ± 1.1. The ultrasmall 8 arcmin2 survey area implies a large yet uncertain contribution to the cosmic star formation rate density CSFRD(z = 5) ∼ 0.9 × 10−2 M⊙ yr−1 Mpc−3, comparable to all ultraviolet-selected galaxies combined. These results indicate the existence of a prominent population of DSFGs at z > 4, below the typical detection limit of bright galaxies found in single-dish submillimeter surveys, but with larger space densities ∼3 × 10−5 Mpc−3, higher duty cycles of 50%–100%, contributing more to the CSFRD, and potentially dominating the high-mass galaxy stellar mass function.
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

Estimating the uncertainty of a Bayesian model has been investigated for decades. The model posterior is almost always intractable, such that approximation is necessary. In many real-world cases, even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output. A common accurate solution is to use Monte Carlo (MC) integration. However, it needs to maintain a large number of samples, evaluate the model repeatedly and average multiple model outputs. In this paper, we propose a method to approximate the probability distribution over the simplex induced by model posterior, enabling tractable computation of the predictive distribution for classification. The aim is to approximate the induced uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration in testing time. Methodologically, we adapt Wasserstein distance to learn the induced conditional distributions, which is novel for Bayesian learning. The proposed method is universally applicable to Bayesian classification models that allow for posterior sampling. Empirical results validate the strong practical performance of our approach.
G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may generate more labeled training data, which may help improve classification accuracy. Second, in scenarios where real data cannot be released outside certain premises for privacy and/or security reasons, using GAN- synthetic data to conduct training is a plausible alternative. This paper proposes a generalization bound to guarantee the generalization capability of a classifier learning from GAN-synthetic data. This generalization bound helps developers gauge the generalization gap between learning from synthetic data and testing on real data, and can therefore provide the clues to improve the generalization capability.
Risk Factors for Injury in Subelite Rugby League Players

Background Although player fatigue and playing intensity have been suggested to contribute to injuries in rugby league players, no study has confirmed if the level of physical fitness is a risk factor for injury in rugby league players. The aim of this study was to identify risk factors for injury in subelite rugby league players. Hypothesis Low physical fitness levels are risk factors for injury in subelite rugby league players. Study Design Cohort study; Level of evidence, 2. Methods One hundred fifty-three players from a subelite rugby league club underwent preseason measurements of muscular power (vertical jump), speed (10- and 40-m sprint), and maximal aerobic power (multistage fitness test) over 4 competitive seasons. All injuries sustained by players were prospectively recorded over the 4 competitive seasons. Results The risk of injury was greater in players with low 10- and 40-m speed. Players with a low maximal aerobic power had a greater risk of sustaining a contact injury. In addition, players who completed less than 18 weeks of training before sustaining their initial injuries were at greater risk of sustaining a subsequent injury. Conclusions Subelite rugby league players with low speed and maximal aerobic power are at an increased risk of injury. In addition, players who complete less than 18 weeks of training before sustaining an initial injury are at greater risk of sustaining a subsequent injury. These findings highlight the importance of speed and endurance training to reduce the incidence of injury in subelite rugby league players.
The Impact of RDMA on Agreement

Remote Direct Memory Access (RDMA) is becoming widely available in data centers. This technology allows a process to directly read and write the memory of a remote host, with a mechanism to control access permissions. In this paper, we study the fundamental power of these capabilities. We consider the well-known problem of achieving consensus despite failures, and find that RDMA can improve the inherent trade-off in distributed computing between failure resilience and performance. Specifically, we show that RDMA allows algorithms that simultaneously achieve high resilience and high performance, while traditional algorithms had to choose one or another. With Byzantine failures, we give an algorithm that only requires n \geq 2f_P + 1 processes (where f_P is the maximum number of faulty processes) and decides in two (network) delays in common executions. With crash failures, we give an algorithm that only requires n \geq f_P + 1 processes and also decides in two delays. Both algorithms tolerate a minority of memory failures inherent to RDMA, and they provide safety in asynchronous systems and liveness with standard additional assumptions.
Multi-task Learning in Deep Gaussian Processes with Multi-kernel Layers

We present a multi-task learning formulation for Deep Gaussian processes (DGPs), describing a multi-kernel architecture for DGP layers. The proposed model is a non-linear mixture of latent Gaussian processes (GPs) with components shared between the tasks, in addition to separate task-specific components. Our formulation allows for learning complex relationships between tasks. We benchmark our model on three real-world datasets showing empirically that our formulation is able to improve the learning performance and transfer information between the tasks, outperforming state-of-the-art GP-based single-task learning and multi-task learning models.
Revisiting the pulsational characteristics of the exoplanet host star β Pictoris

Context. Exoplanet properties crucially depend on the parameters of their host stars: more accurate stellar parameters yield more accurate exoplanet characteristics. When the exoplanet host star shows pulsations, asteroseismology can be used for an improved description of the stellar parameters. Aims. We aim to revisit the pulsational properties of β Pic and identify its pulsation modes from normalized amplitudes in five different passbands. We also investigate the potential presence of a magnetic field. Methods. We conducted a frequency analysis using three seasons of BRITE-Constellation observations in the two BRITE filters, the about 620-day-long bRing light curve, and the nearly 8-year-long SMEI photometric time series. We calculated normalized amplitudes using all passbands and including previously published values obtained from ASTEP observations. We investigated the magnetic properties of β Pic using spectropolarimetric observations conducted with the HARPSpol instrument. Using 2D rotating models, we fit the normalized amplitudes and frequencies through Monte Carlo Markov chains. Results. We identify 15 pulsation frequencies in the range from 34 to 55 d−1, where two, F13 at 53.6917 d−1 and F11 at 50.4921 d−1, display clear amplitude variability. We use the normalized amplitudes in up to five passbands to identify the modes as three ℓ = 1, six ℓ = 2, and six ℓ = 3 modes. β Pic is shown to be non-magnetic with an upper limit of the possible undetected dipolar field of 300 Gauss. Conclusions. Multiple fits to the frequencies and normalized amplitudes are obtained, including one with a near equator-on inclination for β Pic, which corresponds to our expectations based on the orbital inclination of β Pic b and the orientation of the circumstellar disk. This solution leads to a rotation rate of 27% of the Keplerian breakup velocity, a radius of 1.497 ± 0.025 R⊙, and a mass of 1.797 ± 0.035 M⊙. The ∼2% errors in radius and mass do not account for uncertainties in the models and a potentially erroneous mode-identification.
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR

In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for dinner party recordings obtained by multiple microphone arrays are (1) heavy speech overlaps, (2) severe noise and reverberation, (3) very natural conversational content, and possibly (4) insufficient training data. As an example of a dinner party scenario, we have chosen the data presented during the CHiME-5 speech recognition challenge, where the baseline ASR had a 73.3% word error rate (WER), and even the best performing system at the CHiME-5 challenge had a 46.1% WER. We extensively investigated a combination of the guided source separation-based speech enhancement technique and an already proposed strong ASR backend and found that a tight combination of these techniques provided substantial accuracy improvements. Our final system achieved WERs of 39.94% and 41.64% for the development and evaluation data, respectively, both of which are the best published results for the dataset. We also investigated with additional training data on the official small data in the CHiME-5 corpus to assess the intrinsic difficulty of this ASR task.
Rethinking Full Connectivity in Recurrent Neural Networks

Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully connected. This places a heavy computational and memory burden on hardware, restricting adoption in practical low-cost and low-power devices. Compared to fully convolutional models, the costly sequential operation of RNNs severely hinders performance on parallel hardware. This paper challenges the convention of full connectivity in RNNs. We study structurally sparse RNNs, showing that they are well suited for acceleration on parallel hardware, with a greatly reduced cost of the recurrent operations as well as orders of magnitude less recurrent weights. Extensive experiments on challenging tasks ranging from language modeling and speech recognition to video action recognition reveal that structurally sparse RNNs achieve competitive performance as compared to fully-connected networks. This allows for using large sparse RNNs for a wide range of real-world tasks that previously were too costly with fully connected networks.
Regulators of G‐protein signalling in Aspergillus nidulans: RgsA downregulates stress response and stimulates asexual sporulation through attenuation of GanB (Gα) signalling

Regulators of G‐protein signalling play a crucial role in controlling the degree of heterotrimeric G‐protein signalling. In addition to the previously studied flbA, we have identified three genes (rgsA, rgsB and rgsC) encoding putative RGS proteins in the genome of Aspergillus nidulans. Characterization of the rgsA gene revealed that RgsA downregulates pigment production and conidial germination, but stimulates asexual sporulation (conidiation). Deletion of rgsA (ΔrgsA) resulted in reduced colony size with increased aerial hyphae, elevated accumulation of brown pigments as well as enhanced tolerance of conidia and vegetative hyphae against oxidative and thermal stress. Moreover, ΔrgsA resulted in conidial germination in the absence of a carbon source. Deletion of both flbA and rgsA resulted in an additive phenotype, suggesting that the G‐protein pathways controlled by FlbA and RgsA are different. Morphological and metabolic alterations caused by ΔrgsA were suppressed by deletion of ganB encoding a Gα subunit, indicating that the primary role of RgsA is to control negatively GanB‐mediated signalling. Overexpression of rgsA caused inappropriate conidiation in liquid submerged culture, supporting the idea that GanB signalling represses conidiation. Our findings define a second and specific RGS–Gα pair in A. nidulans, which may govern upstream regulation of fungal cellular responses to environmental changes.
Ride-share matching algorithms generate income inequality

Despite the potential of online sharing economy platforms such as Uber, Lyft, or Foodora to democratize the labor market, these services are often accused of fostering unfair working conditions and low wages. These problems have been recognized by researchers and regulators but the size and complexity of these socio-technical systems, combined with the lack of transparency about algorithmic practices, makes it difficult to understand system dynamics and large-scale behavior. This paper combines approaches from complex systems and algorithmic fairness to investigate the effect of algorithm design decisions on wage inequality in ride-hailing markets. We first present a computational model that includes conditions about locations of drivers and passengers, traffic, the layout of the city, and the algorithm that matches requests with drivers. We calibrate the model with parameters derived from empirical data. Our simulations show that small changes in the system parameters can cause large deviations in the income distributions of drivers, leading to a highly unpredictable system which often distributes vastly different incomes to identically performing drivers. As suggested by recent studies about feedback loops in algorithmic systems, these initial income differences can result in enforced and long-term wage gaps.
Aorta Movement in Patients With Scoliosis After Posterior Surgery

Study Design. Retrospective analysis. Objective. To evaluate movement of the aorta in patients with scoliosis who have undergone the posterior correction and fusion. Summary of Background Data. Surgeons check preoperative imaging for pedicle screw placement, but past analyses indicated that the aorta shifts after scoliosis surgery. Few studies, however, evaluated the aorta movement in detail. Methods. A total of 22 patients with a right thoracic curve underwent posterior instrumentation and fusion. The average age at surgery was 17.2 years. The average of the preoperative Cobb angle was 65.2° which decreased to 20.0°. Computed-tomographic data were analyzed by multiplanar reconstruction. In our coordinate system, the middle of the base of the left superior facet was set as the origin and a line connecting the middle points of both bases of the superior facets was defined as the X-axis. We defined the angle and the distance to describe the aorta position and analyzed the movement of the aorta relative to the spine. Deformity parameters were examined to determine their correlation with the aorta parameters. We simulated variable pedicle screw placement and defined a warning pedicle when the aorta enters the expected area of the screw and examined them in 24 scenarios. Results. The aorta moved 4.7 ± 3.0 mm on an average. The aorta had a tendency to migrate in the anteromedial direction and this movement correlated with preoperative apical vertebral translation, preoperative sagittal alignment, and change of sagittal alignment. The ratio of warning pedicles at the middle thoracic level (T7–T9) increased after deformity correction. Conclusion. The aorta moved anteromedially relative to the spine after the posterior correction and the risk of the aorta by a pedicle screw increased by correction of the deformity at the middle thoracic spine. Surgeons are recommended to anticipate the aorta movement in the surgical planning.
Address Instance-level Label Prediction in Multiple Instance Learning

\textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown. Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level. This restricts their application in many tasks, where the instance-level labels are more interested. In this paper, we propose a novel algorithm, whose loss is specifically defined at the instance level, to address instance-level label prediction in MIL. We prove that the loss of this algorithm can be unbiasedly and consistently estimated without using instance labels, under the i.i. d assumption. Empirical study validates the above statements and shows that the proposed algorithm can achieve superior instance-level and comparative bag-level performance, compared to state-of-the-art MIL methods. In addition, it shows that the proposed method can achieve similar results as the fully supervised model (trained with instance labels) for label prediction at the instance level.
Dynamics of T cell receptor distributions following acute thymic atrophy and resumption

Naive human T cells are produced and developed in the thymus, which atrophies abruptly and severely in response to physical or psychological stress. To understand how an instance of stress affects the size and “diversity” of the peripheral naive T cell pool, we derive a mean-field autonomous ODE model of T cell replenishment that allows us to track the clone abundance distribution (the mean number of different TCRs each represented by a specific number of cells). We identify equilibrium solutions that arise at different rates of T cell production, and derive analytic approximations to the dominant eigenvalues and eigenvectors of the mathematical model linearized about these equilibria. From the forms of the eigenvalues and eigenvectors, we estimate rates at which counts of clones of different sizes converge to and depart from equilibrium values—that is, how the number of clones of different sizes ”adjusts” to the changing rate of T cell production. Under most physiological realizations of our model, the dominant eigenvalue (representing the slowest dynamics of the clone abundance distribution) scales as a power law in the thymic output for low output levels, but saturates at higher T cell production rates. Our analysis provides a framework for quantitatively understanding how the clone abundance distribution evolves under small changes in the overall T cell production rate.
Lifelong Bayesian Optimization

Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time. To be suitable for "lifelong" Bayesian Optimization, an algorithm needs to scale with the ever increasing number of acquisitions and should be able to leverage past optimizations in learning the current best model. We cast the problem of model selection as a black-box function optimization problem. In LBO, we exploit the correlation between functions by using components of previously learned functions to speed up the learning process for newly arriving datasets. Experiments on real and synthetic data show that LBO outperforms standard BO algorithms applied repeatedly on the data.