Miracle-dz/UniFAR-FTUs huggingface dataset:
The Statistics of Extended Debris Disks Measured with Gaia and Planck

Thermal emission from debris disks around stars has been measured using targeted and resolved observations. We present an alternative likelihood-based approach in which temperature maps from the Planck cosmic microwave background (CMB) survey at 857 and 545 GHz are analyzed in conjunction with stellar positions from Gaia to estimate the fraction of stars hosting disks and the thermal emission from the disks. The debris disks are not resolved (or even necessarily detected individually), but their statistical properties and the correlations with stellar properties are measured for several thousand stars. We compare our findings with higher sensitivity surveys of smaller samples of stars. For dimmer stars, in particular K and M dwarfs, we find that about 10% of stars within 80 pc have emission consistent with debris disks. We also report on 80 candidate disks, the majority of which are not previously identified. We have previously constrained the properties of Exo-Oort clouds using Planck data—with future CMB surveys, both components can be measured for different stellar types, providing a new avenue to study the outer parts of planetary systems.
BackPACK: Packing more into backprop

Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these quantities are of great interest to researchers and practitioners, current deep learning software does not support their automatic calculation. Manually implementing them is burdensome, inefficient if done naively, and the resulting code is rarely shared. This hampers progress in deep learning, and unnecessarily narrows research to focus on gradient descent and its variants; it also complicates replication studies and comparisons between newly developed methods that require those quantities, to the point of impossibility. To address this problem, we introduce BackPACK, an efficient framework built on top of PyTorch, that extends the backpropagation algorithm to extract additional information from first-and second-order derivatives. Its capabilities are illustrated by benchmark reports for computing additional quantities on deep neural networks, and an example application by testing several recent curvature approximations for optimization.
ZTF18aaqjovh (SN 2018bvw): A Radio-emitting Type Ic-BL Supernova Discovered by the Zwicky Transient Facility

We present ZTF18aaqjovh (SN 2018bvw), a high-velocity ("broad-lined") stripped-envelope (Type Ic) supernova (Ic-BL SN) discovered in the Zwicky Transient Facility one-day cadence survey. The shape of the r-band light curve and the evolution of the photospheric velocity are most similar to that of the Type Ic-BL SN 1998bw, which was associated with a low-luminosity gamma-ray burst (GRB). However, the radio luminosity of ZTF18aaqjovh is almost two orders of magnitude fainter than that of SN 1998bw at the same velocity phase, and is more similar to that of iPTF17cw. No X-ray emission was detected, setting a limit that is fainter than the X-ray emission from SN 1998bw, SN 2010bh, and SN 2006aj but consistent with the X-ray emission from SN 2009bb. From our radio observations we estimate a shock velocity of $v\sim0.06$-0.4$c$ and a mass-loss rate of $0.1$-3$\times 10^{-4} (v_\mathrm{wind}/1000 $km/sec) solar masses per year. A search of high-energy catalogs reveals no GRB temporally and spatially coincident with ZTF18aaqjovh. The lack of detection by the interplanetary network (IPN) sets a limit on the prompt gamma-ray luminosity of $L_\mathrm{iso} \approx 1.6 \times 10^{48}$ erg/sec, ruling out a classical GRB but not a low-luminosity GRB. ZTF18aaqjovh shares a number of features with engine-driven explosions: the high photospheric velocity (SN 1998bw), the associated radio emission (SN 2006aj, iPTF17cw), and the optical light curve (SN 1998bw). However, the velocity of the radio-emitting ejecta is similar to that seen in interacting SNe with no evidence for an engine. Therefore, ZTF18aaqjovh represents another transition event between GRB-SNe and "ordinary" Ic-BL SNe.
Microchain: a Light Hierarchical Consensus Protocol for IoT System

While the large-scale Internet of Things (IoT) makes many new applications feasible, like Smart Cities, IoT also brings new concerns on data reliability, security, and privacy. The rapid evolution in blockchain technologies, which relied on a decentralized, immutable and distributed ledger system for transaction data auditing, provides a prospective solution to address the issues in IoT. The blockchain and smart contract enabled security mechanism for IoT applications have attracted increasing interests from both academia and industry. However, integrating cryptocurrency-oriented blockchain technologies into IoT systems meets tremendous challenges on scalability, storage capacity, security, and privacy. Particularly, the performance of blockchain networks significantly relies on the performance of consensus mechanisms, e.g., in terms of data confidentiality, transaction throughput, and network scalability. In this chapter, given an in-depth review of state-of-the-art blockchain networks, the key matrix of designing consensus mechanism for IoT networks are identified in terms of throughput, scalability, and security. To demonstrate a case study on designing scalable, lightweight blockchain protocols for IoT systems, a Microchain framework is introduced and a proof-of-concept prototype is implemented in a physical network environment. The experimental results verify the feasibility of integrating the Microchain into IoT systems.
Applying the Quantum Approximate Optimization Algorithm to the Tail-Assignment Problem

Airlines today are faced with a number of large scale scheduling problems. One such problem is the tail assignment problem, which is the task of assigning individual aircraft to a given set of flights, minimizing the overall cost. Each aircraft is identified by the registration number on its tail fin. In this article, we simulate the Quantum Approximate Optimization Algorithm (QAOA) applied to instances of this problem derived from real world data. The QAOA is a variational hybrid quantum-classical algorithm recently introduced and likely to run on near-term quantum devices. The instances are reduced to fit on quantum devices with 8, 15 and 25 qubits. The reduction procedure leaves only one feasible solution per instance, which allows us to map the tail assignment problem onto the Exact Cover problem. We find that repeated runs of the QAOA identify the feasible solution with close to unit probability for all instances. Furthermore, we observe patterns in the variational parameters such that an interpolation strategy can be employed which significantly simplifies the classical optimization part of the QAOA. Finally, we empirically find a relation between the connectivity of the problem graph and the single-shot success probability of the algorithm.
Congruence RFRS towers

We describe a criterion for a real or complex hyperbolic lattice to admit a RFRS tower that consists entirely of congruence subgroups. We use this to show that certain Bianchi groups $\mathrm{PSL}(\mathcal{O}_d)$ are virtually fibered on congruence subgroups, and also exhibit the first examples of RFRS Kahler groups that are not a subgroup of a product of surface groups and abelian groups.
PSNet: Parametric Sigmoid Norm Based CNN for Face Recognition

The Convolutional Neural Networks (CNN) have become very popular recently due to its outstanding performance in various computer vision applications. It is also used over widely studied face recognition problem. However, the existing layers of CNN are unable to cope with the problem of hard examples which generally produce lower class scores. Thus, the existing methods become biased towards the easy examples. In this paper, we resolve this problem by incorporating a Parametric Sigmoid Norm (PSN) layer just before the final fully-connected layer. We propose a PSNet CNN model by using the PSN layer. The PSN layer facilitates high gradient flow for harder examples as compared to easy examples. Thus, it forces the network to learn the visual features of difficult examples. We conduct the face recognition experiments to test the performance of PSN layer. The suitability of the PSN layer with different loss functions is also experimented. The widely used YouTube Faces (YTF) and Labeled Faces in the Wild (LFW) datasets are used in the experiments. The experimental results confirm the relevance of the proposed PSN laver.
Closed Form Variances for Variational Auto-Encoders

We propose a reformulation of Variational Auto-Encoders eliminating half of the network outputs (the variances) in a deep network setting. While it is well known that the posterior is in general intractable, we show that the variances of Gaussian posteriors and likelihoods may be solved in closed form, producing improved variational lower bounds over their learned counterparts in experiments. The closed forms reduce to remarkably simple expressions -- in particular, one optimal choice for the posterior variance is simply the identity matrix. We arrive at these conclusions by analyzing the variational lower bound objective irrespective of any particular network architecture, deriving its partial derivatives and closed form solutions for all parameters but the posterior means. In deriving the closed form likelihood variance, we show that the objective is underdetermined, which we resolve by constraining the presumed information content of the data examples. Any of these modifications may be applied to simplify, and perhaps improve, any Variational Auto-Encoder.
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for practitioners. Traditionally, randomized controlled trials (A/B tests) are performed to evaluate treatment assignment functions. However, such trials can be time-consuming, expensive, and even unethical in some cases. Therefore, offline counterfactual evaluation of treatment assignment functions becomes a pressing issue because a massive amount of observational data is available in today's big data era. Counterfactual evaluation requires handling the hidden confounders -- the unmeasured features which causally influence both the treatment assignment and the outcome. To deal with the hidden confounders, most of the existing methods rely on the assumption of no hidden confounders. However, this assumption can be untenable in the context of massive observational data. When such data comes with network information, the later can be potentially useful to correct hidden confounding bias. As such, we first formulate a novel problem, counterfactual evaluation of treatment assignment functions with networked observational data. Then, we investigate the following research questions: How can we utilize network information in counterfactual evaluation? Can network information improve the estimates in counterfactual evaluation? Toward answering these questions, first, we propose a novel framework, \emph{Counterfactual Network Evaluator} (CONE), which (1) learns partial representations of latent confounders under the supervision of observed treatments and outcomes; and (2) combines them for counterfactual evaluation. Then through extensive experiments, we corroborate the effectiveness of CONE. The results imply that incorporating network information mitigates hidden confounding bias in counterfactual evaluation.
Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions

Building a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms. The models should be robust enough to handle various user distractions during the interaction flow and should steer the user back into an engaging interaction for successful completion of the interaction. In this work, we have designed a goal-oriented interaction system where children can engage with agents for a series of interactions involving `Meet \& Greet' and `Simon Says' game play. We have explored various feature extractors and models for improved intent recognition and looked at leveraging previous user and system interactions in novel ways with attention models. We have also looked at dialog adaptation methods for entrained response selection. Our bootstrapped models from limited training data perform better than many baseline approaches we have looked at for intent recognition and dialog action prediction.
Latent Variables on Spheres for Sampling and Spherical Inference

Variational inference is a fundamental problem in Variational Auto-Encoder (VAE). By virtue of high-dimensional geometry, we propose a very simple algorithm completely different from existing ones to solve the inference problem in VAE. We analyze the unique characteristics of random variables on spheres in high dimensions and prove that the Wasserstein distance between two arbitrary datasets randomly drawn from a sphere are nearly identical when the dimension is sufficiently large. Based on our theory, a novel algorithm for distribution-robust sampling is devised. Moreover, we reform the latent space of VAE by constraining latent random variables on the sphere, thus freeing VAE from the approximate optimization pertaining to the variational posterior probability. The new algorithm is named as Spherical Auto-Encoder (SAE), which is in essence the vanilla autoencoder with the spherical constraint on the latent space. The associated inference is called the spherical inference, which is geometrically deterministic but is much more robust to various probabilistic priors than the variational inference in VAE for sampling. The experiments on sampling and inference validate our theoretical analysis and the superiority of SAE.
Image Denoising by Random Interpolation Average with Low-Rank Matrix Approximation

With the wide deployment of digital image capturing equipment, the need of denoising to produce a crystal clear image from noisy capture environment has become indispensable. This work presents a novel image denoising method that can tackle both impulsive noise, such as salt and pepper noise (SAPN), and additive white Gaussian noise (AWGN), such as hot carrier noise from CMOS sensor, at the same time. We propose to use low-rank matrix approximation to form the basic denoising framework, as it has the advantage of preserving the spatial integrity of the image. To mitigate the SAPN, the original noise corrupted image is randomly sampled to produce sampled image sets. Low-rank matrix factorization method (LRMF) via alternating minimization denoising method is applied to all sampled images, and the resultant images are fused together via a wavelet fusion with hard threshold denoising. Since the sampled image sets have independent but identical noise property, the wavelet fusion serves as the effective mean to remove the AWGN, while the LRMF method suppress the SAPN. Simulation results are presented which vividly show the denoised images obtained by the proposed method can achieve crystal clear image with strong structural integrity and showing good performance in both subjective and objective metrics.
Hunt Protagonist of Sentiment: Sentiment Analysis via Capsule Network with Sentiment-Aspect Reconstruction

Aspect-term level sentiment analysis (ATSA) is a fine-grained task in sentiment classification. It aims at extracting and summarizing the sentiment polarity towards a given aspect phrase from a sentence. Most existing studies combined various neural network models with a delicately carved attention mechanism to generate refined representations of sentences for better predictions. However, they were inadequate to capture correlations between aspects and sentiments. Moreover, the annotated aspect term might be unavailable in real-world scenarios which may challenge the existing methods to give correct forecasting. In this paper, we propose a capsule network based model named CAPSAR (CAPsule network with Sentiment-Aspect Reconstruction) to improve aspect-term level sentiment analysis. CAPSAR adopts a hierarchical structure of capsules and learns interactive patterns between aspects and sentiments through packaged sentiment-aspect reconstruction. Capsules in CAPSAR are capable of communicating with other capsules through a sharing-weight routing algorithm. Experiments on three ATSA benchmarks demonstrate the superiority of our model, and CAPSAR can detect the potential aspect terms from sentences by de-capsulizing the vectors in capsules when aspect terms are unknown.
ARM Pointer Authentication based Forward-Edge and Backward-Edge Control Flow Integrity for Kernels

Code reuse attacks are still big threats to software and system security. Control flow integrity is a promising technique to defend against such attacks. However, its effectiveness has been weakened due to the inaccurate control flow graph and practical strategy to trade security for performance. In recent years, CPU vendors have integrated hardware features as countermeasures. For instance, ARM Pointer Authentication (PA in short) was introduced in ARMV8-A architecture. It can efficiently generate an authentication code for an address, which is encoded in the unused bits of the address. When the address is de-referenced, the authentication code is checked to ensure its integrity. Though there exist systems that adopt PA to harden user programs, how to effectively use PA to protect OS kernels is still an open research question. In this paper, we shed lights on how to leverage PA to protect control flows, including function pointers and return addresses, of Linux kernel. Specifically, to protect function pointers, we embed authentication code into them, track their propagation and verify their values when loading from memory or branching to targets. To further defend against the pointer substitution attack, we use the function pointer address as its context, and take a clean design to propagate the address by piggybacking it into the pointer value. We have implemented a prototype system with LLVM to identify function pointers, add authentication code and verify function pointers by emitting new machine instructions. We applied this system to Linux kernel, and solved numerous practical issues, e.g., function pointer comparison and arithmetic operations. The security analysis shows that our system can protect all function pointers and return addresses in Linux kernel.
Optimally handling commitment issues in online throughput maximization

We consider a fundamental online scheduling problem in which jobs with processing times and deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on a single machine that maximizes the number of jobs that complete before their deadline. Due to strong impossibility results for competitive analysis, it is commonly required that jobs contain some slack $\varepsilon>0$, which means that the feasible time window for scheduling a job is at least $1+\varepsilon$ times its processing time. In this paper, we resolve the question on how to handle commitment requirements which enforce that a scheduler has to guarantee at a certain point in time the completion of admitted jobs. This is very relevant, e.g., in providing cloud-computing services and disallows last-minute rejections of critical tasks. We give an algorithm with an optimal competitive ratio of $\Theta(1/\varepsilon)$ for the online throughput maximization problem when a scheduler must commit upon starting a job. Somewhat surprisingly, this is the same optimal performance bound (up to constants) as for scheduling without commitment. If commitment decisions must be made before a job's slack becomes less than a $\delta$-fraction of its size, we prove a competitive ratio of $\mathcal{O}(\varepsilon/((\varepsilon - \delta)\delta))$ for $0 < \delta < \varepsilon$. This result interpolates between commitment upon starting a job and commitment upon arrival. For the latter commitment model, it is known that no (randomized) online algorithms does admit any bounded competitive ratio.
Axial anomaly generation by domain wall motion in Weyl semimetals

A space-time dependent node separation in Weyl semimetals acts as an axial vector field. Coupled with domain wall motion in magnetic Weyl semimetals, this induces axial electric and magnetic fields localized at the domain wall. We show how these fields can activate the axial (chiral) anomaly and provide a direct experimental signature of it. Specifically, a domain wall provides a spatially dependent Weyl node separation and an axial magnetic field $\textbf{B}_5$, and domain wall movement, driven by an external magnetic field, gives the Weyl node separation a time dependence, inducing an axial electric field $\textbf{E}_5$. At magnetic fields beyond the Walker breakdown, $\textbf{E}_5\cdot\textbf{B}_5$ becomes nonzero and activates the axial anomaly that induces a finite axial charge density---imbalance in the number of left- and right-handed fermions---moving with the domain wall. This axial density, in turn, produces, via the chiral magnetic effect, an oscillating current flowing along the domain wall plane, resulting in a characteristic radiation of electromagnetic waves emanating from the domain wall. A detection of this radiation would constitute a direct measurement of the axial anomaly induced by axial electromagnetic fields.
UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing

In real-world underwater environment, exploration of seabed resources, underwater archaeology, and underwater fishing rely on a variety of sensors, vision sensor is the most important one due to its high information content, non-intrusive, and passive nature. However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images. To address this problem, firstly, we proposed an unsupervised generative adversarial network (GAN) for generating realistic underwater images (color distortion and haze effect) from in-air image and depth map pairs based on improved underwater imaging model. Secondly, U-Net, which is trained efficiently using synthetic underwater dataset, is adopted for color restoration and dehazing. Our model directly reconstructs underwater clear images using end-to-end autoencoder networks, while maintaining scene content structural similarity. The results obtained by our method were compared with existing methods qualitatively and quantitatively. Experimental results obtained by the proposed model demonstrate well performance on open real-world underwater datasets, and the processing speed can reach up to 125FPS running on one NVIDIA 1060 GPU. Source code, sample datasets are made publicly available at this https URL.
Does Symbolic Knowledge Prevent Adversarial Fooling?

Arguments in favor of injecting symbolic knowledge into neural architectures abound. When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. Focusing on deep probabilistic (logical) graphical models -- i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs -- we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples.