Miracle-dz/UniFAR-FTUs huggingface dataset:
11 Tera-FLOP per Second Photonic Convolutional Accelerator for Deep Learning Optical Neural Networks

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 TeraFLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8 bit resolution for 10 kernels simultaneously, enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability

We propose an efficient algorithm to visualise symmetries in neural networks. Typically, models are defined with respect to a parameter space, where non-equal parameters can produce the same input-output map. Our proposed method, GENNI, allows us to efficiently identify parameters that are functionally equivalent and then visualise the subspace of the resulting equivalence class. By doing so, we are now able to better explore questions surrounding identifiability, with applications to optimisation and generalizability, for commonly used or newly developed neural network architectures.
Uniform fluctuation and wandering bounds in first passage percolation

We consider first passage percolation on certain isotropic random graphs in $\mathbb{R}^d$. We assume exponential concentration of passage times $T(x,y)$, on some scale $\sigma_r$ whenever $|y-x|$ is of order $r$, with $\sigma_r$ "growning like $r^\chi$" for some $0<\chi<1$. Heuristically this means transverse wandering of geodesics should be at most of order $\Delta_r = (r\sigma_r)^{1/2}$. We show that in fact uniform versions of exponential concentration and wandering bounds hold: except with probability exponentially small in $t$, there are no $x,y$ in a natural cylinder of length $r$ and radius $K\Delta_r$ for which either (i) $|T(x,y) - ET(x,y)|\geq t\sigma_r$, or (ii) the geodesic from $x$ to $y$ wanders more than distance $\sqrt{t}\Delta_r$ from the cylinder axis. We also establish that for the time constant $\mu = \lim_n ET(0,ne_1)/n$, the "nonrandom error" $|\mu|x| - ET(0,x)|$ is at most a constant multiple of $\sigma(|x|)$.
Can the Helium-detonation Model Explain the Observed Diversity of Type Ia Supernovae?

We study a sample of 16 Type Ia supernovae (SNe Ia) having both spectroscopic and photometric observations within 2–3 days after the first light. The early B − V colors of such a sample tend to show a continuous distribution. For objects with normal ejecta velocity (NV), the C ii λ6580 feature is always visible in the early spectra, while it is absent or very weak in the high-velocity (HV) counterpart. Moreover, the velocities of the detached high-velocity features (HVFs) of the Ca II near-IR triplet (CaIR3) above the photosphere are found to be much higher in HV objects than in NV objects, with typical values exceeding 30,000 km s−1 at 2–3 days. We further analyze the relation between the velocity shift of late-time [Fe II] lines (v[Fe II]) and host galaxy mass. We find that all HV objects have redshifted v[Fe II], while NV objects have both blue- and redshifted v[Fe II]. It is interesting to point out that the objects with redshifted v[Fe II] are all located in massive galaxies, implying that HV and a portion of NV objects may have similar progenitor metallicities and explosion mechanisms. We propose that, with a geometric/projected effect, the He-detonation model may account for the similarity in birthplace environment and the differences seen in some SNe Ia, including B − V colors, C II features, CaIR3 HVFs at early times, and v[Fe II] in the nebular phase. Nevertheless, some features predicted by He-detonation simulation, such as the rapidly decreasing light curve, deviate from the observations, and some NV objects with blueshifted nebular v[Fe II] may involve other explosion mechanisms.
An End-to-end Method for Producing Scanning-robust Stylized QR Codes

Quick Response (QR) code is one of the most worldwide used two-dimensional codes. ~Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works adopt fixed generation algorithms and therefore can only generate QR codes with a pre-defined style. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end method, named ArtCoder, to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust. ~To guarantee that the generated stylized QR codes are still scanning-robust, we propose a Sampling-Simulation layer, a module-based code loss, and a competition mechanism. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application.
PD-L1 Immunohistochemistry Comparability and Their Correlation with Clinical Characteristics in NSCLC

Background PD-L1 expression is an important predictive factor of response to therapy with immune checkpoint inhibitors (ICIs). This study was designed to retrospectively analyze the concordance of PD-L1 measurements using three different assays (Dako22C3, Dako28-8, and SP142) in NSCLC patients and to find possible predictors of high PD-L1 expression. Materials and Methods Data of 144 patients with histologically confirmed NSCLC and available PD-L1 measurements treated at the Taoyuan General Hospital from 2018 to 2019 were retrospectively reviewed in the study. Patients' characteristics, including age, sex, clinical stage (T, N, and M) of NSCLC (AJCC, 8th edition), and EGFR/ALK alterations, were analyzed for association with PD-L1 expression. Results Measurements of PD-L1 expression levels with Dako22C3 and Dako28-8 were comparable while SP142 showed lower levels of PD-L1 expression. The overall agreement between Dako22C3 and Dako28-8 was 82.2% and 91.6% for both 1% and 50% TPS cut-offs, respectively. The above findings were confirmed by Cohen's kappa. In addition, we found that PD-L1 expression was significantly associated with advanced N stage but not with T and M stages. Conclusion Dako22C3 and Dako28-8 showed comparable results in assessing PD-L1 levels. Future prospective studies are needed to validate these findings. N stage may be a good predictor for PD-L1 expression.
The Dusty Heart of NGC 4151 Revealed by λ ∼ 1–40 μm Reverberation Mapping and Variability: A Challenge to Current Clumpy Torus Models

We probe the dusty environment of the archetypical Type 1 active galactic nucleus (AGN) in NGC 4151 with comprehensive IR reverberation mapping over several decades, in the J (∼1.22 μm), H (∼1.63 μm), K (∼2.19 μm), L (∼3.45 μm), and N bands (∼10.6 μm), plus multiple measurements at 20–40 μm. At 1–4 μm, the hot dust reverberation signals come from two distinct dust populations at separate radii (∼0.033 pc and ∼0.076 pc), with temperatures of ∼1500–2500 K and ∼900–1000 K, consistent with the expected properties of sublimating graphite and silicate dust grains. The domination of the torus infrared output by carbon and silicate grains near their sublimation temperatures and radii may account for the general similarity of AGN near-IR spectral energy distributions. The torus inner edge defined by the hottest dust remains at roughly the same radius independent of the AGN optical luminosity over ∼25 yr. The emission by hot dust warmed directly by the optical/UV AGN output has increased gradually by ∼4% yr−1, indicating a possibly growing torus. A third dust component at ∼700 K does not seem to participate directly in the IR reverberation behavior, and its emission may originate deep in the circumnuclear torus. We find a reverberation signal at ∼10 μm with an inferred radius for the warm dust of ∼2.2–3.1 pc. The lack of variability at 20–40 μm indicates that the far-IR emission comes from even more extended regions. The torus properties revealed by dust reverberation analysis are inconsistent with predictions from pure clumpy torus models. Instead, the longer-wavelength emission possibly originates in a flared torus or the polar wind.
On a Variational Approximation based Empirical Likelihood ABC Method

Many scientifically well-motivated statistical models in natural, engineering, and environmental sciences are specified through a generative process. However, in some cases, it may not be possible to write down the likelihood for these models analytically. Approximate Bayesian computation (ABC) methods allow Bayesian inference in such situations. The procedures are nonetheless typically computationally intensive. Recently, computationally attractive empirical likelihood-based ABC methods have been suggested in the literature. All of these methods rely on the availability of several suitable analytically tractable estimating equations, and this is sometimes problematic. We propose an easy-to-use empirical likelihood ABC method in this article. First, by using a variational approximation argument as a motivation, we show that the target log-posterior can be approximated as a sum of an expected joint log-likelihood and the differential entropy of the data generating density. The expected log-likelihood is then estimated by an empirical likelihood where the only inputs required are a choice of summary statistic, it's observed value, and the ability to simulate the chosen summary statistics for any parameter value under the model. The differential entropy is estimated from the simulated summaries using traditional methods. Posterior consistency is established for the method, and we discuss the bounds for the required number of simulated summaries in detail. The performance of the proposed method is explored in various examples.
Stylized Neural Painting

This paper proposes an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering. Since a typical vector render is not differentiable, we design a novel neural renderer which imitates the behavior of the vector renderer and then frame the stroke prediction as a parameter searching process that maximizes the similarity between the input and the rendering output. We explored the zero-gradient problem on parameter searching and propose to solve this problem from an optimal transportation perspective. We also show that previous neural renderers have a parameter coupling problem and we re-design the rendering network with a rasterization network and a shading network that better handles the disentanglement of shape and color. Experiments show that the paintings generated by our method have a high degree of fidelity in both global appearance and local textures. Our method can be also jointly optimized with neural style transfer that further transfers visual style from other images. Our code and animated results are available at https://jiupinjia.github. io/neuralpainter/.
EVALUATION OF AN AUTOMATED COAGULATION ANALYZER

The ACL (IL-Automated Coagulation Laboratory from the Fisher Scientific Group) is the first microcentrifugal analyzer incorporating 2 reading channels, a coagulometric channel consisting of a laser light source and a chromogenic channel consisting of a halogen light source. We have evaluated the instrument for precision and accuracy using different reagents for both clotting and the chromogenic assays. Replicate samples were run in both the PT and APTT modes using 4 different reagents. The reagent with the least particles had the greatest precision. The mean values for APTTs and PTs using a particle free reagent were 68.7 ± . 83“ for the APTT (C.V. 1.2%), and 30.2 ± . 33” (C.V. 1.1%) for the PT. A fibrinogen assay measured on the ACL (delta light scatter of the prothrombin time) was compared to the Clause fibrinogen assay in three population groups: the adult, the newborn and patients receiving thrombolytic therapy. The correlation for the adult and newborn was good with r values of . 911 (n = 51) and . 96 (n = 36) respectively. The thrombolytic therapy group had poor correlation r = . 32. Antithrombin III (ATIII) and a2 antiplasmin (AP) assays were measured on the ACL using IIs chromogenic method. These were compared to an ATIII chromogenic method (Kahle) using a Gilford SBA 300 analyzer and α2 AP assay using a Protopath (Dade). The correlations were ATIII, r=. 95 and α2 AP, r=. 82. The plasminogen method of Friberger was adapted to the ACL giving us comparable results to those read off the Gilford SBA 300 (r=.93). With the introduction of the ACL we have been able to: 1) reduce the technical time required for assays by one half; 2) reduce reagent costs by one half to three-quarters; 3) reduce the amount of plasma required for screening tests by half the volume, which has greatly facilitated neonatal coagulation testing
Star Formation Efficiency and Dispersal of Giant Molecular Clouds with UV Radiation Feedback: Dependence on Gravitational Boundedness and Magnetic Fields

Molecular clouds are supported by turbulence and magnetic fields, but quantifying their influence on cloud life cycle and star formation efficiency (SFE) remains an open question. We perform radiation magnetohydrodynamic simulations of star-forming giant molecular clouds (GMCs) with UV radiation feedback, in which the propagation of UV radiation via ray tracing is coupled to hydrogen photochemistry. We consider 10 GMC models that vary in either initial virial parameter (1 ≤ α vir,0 ≤ 5) or dimensionless mass-to-magnetic flux ratio (0.5 ≤ μ Φ,0 ≤ 8 and ∞ ); the initial mass 105 M ⊙ and radius 20 pc are fixed. Each model is run with five different initial turbulence realizations. In most models, the duration of star formation and the timescale for molecular gas removal (primarily by photoevaporation) are 4–8 Myr. Both the final SFE (ε *) and time-averaged SFE per freefall time (ε ff) are reduced by strong turbulence and magnetic fields. The median ε * ranges between 2.1% and 9.5%. The median ε ff ranges between 1.0% and 8.0%, and anticorrelates with α vir,0, in qualitative agreement with previous analytic theory and simulations. However, the time-dependent α vir(t) and ε ff,obs(t) based on instantaneous gas properties and cluster luminosity are positively correlated due to rapid evolution, making observational validation of star formation theory difficult. Our median ε ff,obs(t) ≈ 2% is similar to observed values. We show that the traditional virial parameter estimates the true gravitational boundedness within a factor of 2 on average, but neglect of magnetic support and velocity anisotropy can sometimes produce large departures from traditional virial parameter estimates. Magnetically subcritical GMCs are unlikely to represent sites of massive star formation given their unrealistic columnar outflows, prolonged lifetime, and low escape fraction of radiation.
Audio-Visual Multi-Channel Integration and Recognition of Overlapped Speech

Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging task to date. To this end, multi-channel microphone array data are widely used in current ASR systems. Motivated by the invariance of visual modality to acoustic signal corruption and the additional cues they provide to separate the target speaker from the interfering sound sources, this paper presents an audio-visual multi-channel based recognition system for overlapped speech. It benefits from a tight integration between a speech separation front-end and recognition back-end, both of which incorporate additional video input. A series of audio-visual multi-channel speech separation front-end components based on TF masking, Filter&Sum and mask-based MVDR neural channel integration approaches are developed. To reduce the error cost mismatch between the separation and the recognition components, the entire system is jointly fine-tuned using a multi-task criterion interpolation of the scale-invariant signal to noise ratio (Si-SNR) with either the connectionist temporal classification (CTC), or lattice-free maximum mutual information (LF-MMI) loss function. Experiments suggest that: the proposed audio-visual multi-channel recognition system outperforms the baseline audio-only multi-channel ASR system by up to 8.04% (31.68% relative) and 22.86% (58.51% relative) absolute WER reduction on overlapped speech constructed using either simulation or replaying of the LRS2 dataset respectively. Consistent performance improvements are also obtained using the proposed audio-visual multi-channel recognition system when using occluded video input with the lip region randomly covered up to 60%.
Pneumothorax and chest tube classification on chest x-rays for detection of missed pneumothorax

Chest x-ray imaging is widely used for the diagnosis of pneumothorax and there has been significant interest in developing automated methods to assist in image interpretation. We present an image classification pipeline which detects pneumothorax as well as the various types of chest tubes that are commonly used to treat pneumothorax. Our multi-stage algorithm is based on lung segmentation followed by pneumothorax classification, including classification of patches that are most likely to contain pneumothorax. This algorithm achieves state of the art performance for pneumothorax classification on an open-source benchmark dataset. Unlike previous work, this algorithm shows comparable performance on data with and without chest tubes and thus has an improved clinical utility. To evaluate these algorithms in a realistic clinical scenario, we demonstrate the ability to identify real cases of missed pneumothorax in a large dataset of chest x-ray studies.
Selection rules for the S-Matrix bootstrap

We examine the space of allowed S-matrices on the Adler zeros' plane using the recently resurrected (numerical) S-matrix bootstrap program for pion scattering. Two physical quantities, an averaged total scattering cross-section, and an averaged entanglement power for the boundary S-matrices, are studied. Emerging linearity in the leading Regge trajectory is correlated with a reduction in both these quantities. We identify two potentially viable regions where the S-matrices give decent agreement with low energy S- and P-wave scattering lengths and have leading Regge trajectory compatible with experiments. The Lovelace-Shapiro model, which was a precursor to modern string theory, is given by a straight line in the Adler zeros' plane and, quite remarkably, we find that this line intersects the space of allowed S-matrices near both these regions.
Audio-Visual Event Recognition Through the Lens of Adversary

As audio/visual classification models are widely deployed for sensitive tasks like content filtering at scale, it is critical to understand their robustness along with improving the accuracy. This work aims to study several key questions related to multimodal learning through the lens of adversarial noises: 1) The trade-off between early/middle/late fusion affecting its robustness and accuracy 2) How does different frequency/time domain features contribute to the robustness? 3) How does different neural modules contribute against the adversarial noise? In our experiment, we construct adversarial examples to attack state-of-the-art neural models trained on Google AudioSet. [1]1 We compare how much attack potency in terms of adversarial perturbation of size using different Lp norms we would need to "deactivate" the victim model. Using adversarial noise to ablate multimodal models, we are able to provide insights into what is the best potential fusion strategy to balance the model parameters/accuracy and robustness trade-off, and distinguish the robust features versus the non-robust features that various neural networks model tend to learn.
Metabolic and Nutritional Characteristics in Middle-Aged and Elderly Sarcopenia Patients with Type 2 Diabetes

Sarcopenia is considered to be a new complication of type 2 diabetes (T2DM) leading to increased risk of adverse outcome. We performed a survey to evaluate glucose metabolism and nutritional status in sarcopenia patients with T2DM. Diabetic participants aged ≥50 years were grouped into a probable sarcopenia group with low muscle strength (n = 405) and a nonsarcopenia group with normal muscle strength (n = 720) according to the revised recommendations from EWGSOP2 (2018). Compared to the controls, the probable sarcopenia participants were older and had lower waist-to-hip ratio and BMI, longer diabetes duration, higher fasting plasma glucose level and glycosylated hemoglobin (HbA1c), decreased estimated glomerular filtration rate and lower bone mineral content, lower fatless upper arm circumference, lower appendicular skeletal muscle mass index (ASMI), and muscle quality in both genders. Multivariable logistic regression analysis showed increased age, male, low BMI, and increased HbA1c, combined with diabetic nephropathy and decreased serum albumin levels, were risk factors associated with low muscle strength in diabetes patients. In conclusion, diabetic patients with sarcopenia had worse glucose metabolism and nutritional status, decreased renal function and reduced muscle quality ,and muscle mass with a greater likelihood of osteoporosis, who need an overall health management to improve outcomes. This clinical trial registration is registered with the Chinese Clinical Trial Registry, ChiCTR-EOC-15006901.
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning

The principle of Reward-Biased Maximum Likelihood Estimate Based Adaptive Control (RBMLE) that was proposed in Kumar and Becker (1982) is an alternative approach to the Upper Confidence Bound Based (UCB) Approach (Lai and Robbins, 1985) for employing the principle now known as "optimism in the face of uncertainty" (Auer et al., 2002). It utilizes a modified maximum likelihood estimate, with a bias towards those Markov Decision Process (MDP) models that yield a higher average reward. However, its regret performance has never been analyzed earlier for reinforcement learning (RL (Sutton et al., 1998)) tasks that involve the optimal control of unknown MDPs. We show that it has a learning regret of $O(\log T )$ where $T$ is the time-horizon, similar to the state-of-art algorithms. It provides an alternative general purpose method for solving RL problems.
Continuous-time Quantum Walks on Cayley Graphs of Extraspecial Groups

We study continuous-time quantum walks on normal Cayley graphs of certain non-abelian $p$-groups, called extraspecial $2$-groups. By applying general results for graphs in association schemes we determine the precise conditions for perfect state transfer and fractional revival, and use partial spreads to construct graphs admitting these various phenomena. Lastly, we use a result of Ada Chan to show that there is no normal Cayley graph of an extraspecial $p$-group that admits instantaneous uniform mixing.