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The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to better understand and improve supply chains, and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture these hidden relationships between nodes' inputs and outputs. Here, we introduce a new class of models for this setting, by combining temporal GNNs with a novel inventory module, which learns production functions via attention weights and a special loss function. We evaluate our models extensively on real supply chains data, along with data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, with a 6-50% improvement over baselines, and forecast future transactions on real and synthetic data, outperforming baselines by 11-62%.

Merge trees are a valuable tool in the scientific visualization of scalar fields; however, current methods for merge tree comparisons are computationally expensive, primarily due to the exhaustive matching between tree nodes. To address this challenge, we introduce the Merge Tree Neural Network (MTNN), a learned neural network model designed for merge tree comparison. The MTNN enables rapid and high-quality similarity computation. We first demonstrate how to train graph neural networks, which emerged as effective encoders for graphs, in order to produce embeddings of merge trees in vector spaces for efficient similarity comparison. Next, we formulate the novel MTNN model that further improves the similarity comparisons by integrating the tree and node embeddings with a new topological attention mechanism. We demonstrate the effectiveness of our model on real-world data in different domains and examine our model's generalizability across various datasets. Our experimental analysis demonstrates our approach's superiority in accuracy and efficiency. In particular, we speed up the prior state-of-the-art by more than $100\times$ on the benchmark datasets while maintaining an error rate below $0.1\%$.

A tremendous range of design tasks in materials, physics, and biology can be formulated as finding the optimum of an objective function depending on many parameters without knowing its closed-form expression or the derivative. Traditional derivative-free optimization techniques often rely on strong assumptions about objective functions, thereby failing at optimizing non-convex systems beyond 100 dimensions. Here, we present a tree search method for derivative-free optimization that enables accelerated optimal design of high-dimensional complex systems. Specifically, we introduce stochastic tree expansion, dynamic upper confidence bound, and short-range backpropagation mechanism to evade local optimum, iteratively approximating the global optimum using machine learning models. This development effectively confronts the dimensionally challenging problems, achieving convergence to global optima across various benchmark functions up to 2,000 dimensions, surpassing the existing methods by 10- to 20-fold. Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms. This enables efficient autonomous knowledge discovery and facilitates self-driving virtual laboratories. Although we focus on problems within the realm of natural science, the advancements in optimization techniques achieved herein are applicable to a broader spectrum of challenges across all quantitative disciplines., Comment: 39 pages, 3 figures

In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ JS divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. A temporal graph integrating both prototype and context nodes helps incorporate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV., Comment: 9 pages, accepted by WWW 2024 Industry Track

High-cadence, multiwavelength observations have continuously revealed the diversity of tidal disruption events (TDEs), thus greatly advancing our knowledge and understanding of TDEs. In this work, we conducted an intensive optical-UV and X-ray follow-up campaign of TDE AT2023lli, and found a remarkable month-long bump in its UV/optical light curve nearly two months prior to maximum brightness. The bump represents the longest separation time from the main peak among known TDEs to date. The main UV/optical outburst declines as $t^{-4.10}$, making it one of the fastest decaying optically selected TDEs. Furthermore, we detected sporadic X-ray emission 30 days after the UV/optical peak, accompanied by a reduction in the period of inactivity. It is proposed that the UV/optical bump could be caused by the self-intersection of the stream debris, whereas the primary peak is generated by the reprocessed emission of the accretion process. In addition, our results suggest that episodic X-ray radiation during the initial phase of decline may be due to the patched obscurer surrounding the accretion disk, a phenomenon associated with the inhomogeneous reprocessing process. The double TDE scenario, in which two stars are disrupted in sequence, is also a possible explanation for producing the observed early bump and main peak. We anticipate that the multicolor light curves of TDEs, especially in the very early stages, and the underlying physics can be better understood in the near future with the assistance of dedicated surveys such as the deep high-cadence survey of the 2.5-meter Wide Field Survey Telescope (WFST)., Comment: 14 pages, 8 figures,accepted for publication by ApJL

Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions. To mitigate this deficiency, we propose a novel adversarial defense method called "Immunity" (Innovative MoE with MUtual information \& positioN stabilITY) based on a modified Mixture-of-Experts (MoE) architecture in this work. The key enhancements to the standard MoE are two-fold: 1) integrating of Random Switch Gates (RSGs) to obtain diverse network structures via random permutation of RSG parameters at evaluation time, despite of RSGs being determined after one-time training; 2) devising innovative Mutual Information (MI)-based and Position Stability-based loss functions by capitalizing on Grad-CAM's explanatory power to increase the diversity and the causality of expert networks. Notably, our MI-based loss operates directly on the heatmaps, thereby inducing subtler negative impacts on the classification performance when compared to other losses of the same type, theoretically. Extensive evaluation validates the efficacy of the proposed approach in improving adversarial robustness against a wide range of attacks.

Multi-peaked supernovae with precursors, dramatic light-curve rebrightenings, and spectral transformation are rare, but are being discovered in increasing numbers by modern night-sky transient surveys like the Zwicky Transient Facility (ZTF). Here, we present the observations and analysis of SN 2023aew, which showed a dramatic increase in brightness following an initial luminous (-17.4 mag) and long (~100 days) unusual first peak (possibly precursor). SN 2023aew was classified as a Type IIb supernova during the first peak but changed its type to resemble a stripped-envelope supernova (SESN) after the marked rebrightening. We present comparisons of SN 2023aew's spectral evolution with SESN subtypes and argue that it is similar to SNe Ibc during its main peak. P-Cygni Balmer lines are present during the first peak, but vanish during the second peak's photospheric phase, before H$\alpha$ resurfaces again during the nebular phase. The nebular lines ([O I], [Ca II], Mg I], H$\alpha$) exhibit a double-peaked structure which hints towards a clumpy or non-spherical ejecta. We analyze the second peak in the light curve of SN 2023aew and find it to be broader than normal SESNe as well as requiring a very high $^{56}$Ni mass to power the peak luminosity. We discuss the possible origins of SN 2023aew including an eruption scenario where a part of the envelope is ejected during the first peak which also powers the second peak of the light curve through SN-CSM interaction., Comment: 22 pages, 11 figures, 5 tables

The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ($m_\mathrm{peak}\,\leq\,18.5\,$mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection ("scanning") to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past $\sim5$ yr of ZTF operations. We present $\texttt{BTSbot}$, a multi-modal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. $\texttt{BTSbot}$ is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. $\texttt{BTSbot}$ recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, $\sim$1 hour quicker than scanners). We also find that $\texttt{BTSbot}$ is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. $\texttt{BTSbot}$ has been integrated into Fritz and $\texttt{Kowalski}$, ZTF's first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. During the month of October 2023, $\texttt{BTSbot}$ selected 296 sources in real-time, 93% of which were real extragalactic transients. With $\texttt{BTSbot}$ and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human-time needed to scan. Future development has tremendous potential for creating similar models to identify and request follow-up observations for specific types of transients., Comment: 26 pages, 12 figures; to be submitted to ApJ; comments welcome

Low-light image enhancement aims to improve the perception of images collected in dim environments and provide high-quality data support for image recognition tasks. When dealing with photos captured under non-uniform illumination, existing methods cannot adaptively extract the differentiated luminance information, which will easily cause over-exposure and under-exposure. From the perspective of unsupervised learning, we propose a multi-scale attention Transformer named MSATr, which sufficiently extracts local and global features for light balance to improve the visual quality. Specifically, we present a multi-scale window division scheme, which uses exponential sequences to adjust the window size of each layer. Within different-sized windows, the self-attention computation can be refined, ensuring the pixel-level feature processing capability of the model. For feature interaction across windows, a global transformer branch is constructed to provide comprehensive brightness perception and alleviate exposure problems. Furthermore, we propose a loop training strategy, using the diverse images generated by weighted mixing and a luminance consistency loss to improve the model's generalization ability effectively. Extensive experiments on several benchmark datasets quantitatively and qualitatively prove that our MSATr is superior to state-of-the-art low-light image enhancement methods, and the enhanced images have more natural brightness and outstanding details. The code is released at https://github.com/fang001021/MSATr.

Neutron stars and stellar-mass black holes are the remnants of massive star explosions. Most massive stars reside in close binary systems, and the interplay between the companion star and the newly formed compact object has been theoretically explored, but signatures for binarity or evidence for the formation of a compact object during a supernova explosion are still lacking. Here we report a stripped-envelope supernova, SN 2022jli, which shows 12.4-day periodic undulations during the declining light curve. Narrow H$\alpha$ emission is detected in late-time spectra with concordant periodic velocity shifts, likely arising from hydrogen gas stripped from a companion and accreted onto the compact remnant. A new Fermi/LAT $\gamma$-ray source is temporally and positionally consistent with SN 2022jli. The observed properties of SN 2022jli, including periodic undulations in the optical light curve, coherent H$\alpha$ emission shifting, and evidence for association with a $\gamma$-ray source, point to the explosion of a massive star in a binary system leaving behind a bound compact remnant. Mass accretion from the companion star onto the compact object powers the light curve of the supernova and generates the $\gamma$-ray emission., Comment: Accepted for publication in Nature

This paper presents the first approach to visualize the importance of topological features that define classes of data. Topological features, with their ability to abstract the fundamental structure of complex data, are an integral component of visualization and analysis pipelines. Although not all topological features present in data are of equal importance. To date, the default definition of feature importance is often assumed and fixed. This work shows how proven explainable deep learning approaches can be adapted for use in topological classification. In doing so, it provides the first technique that illuminates what topological structures are important in each dataset in regards to their class label. In particular, the approach uses a learned metric classifier with a density estimator of the points of a persistence diagram as input. This metric learns how to reweigh this density such that classification accuracy is high. By extracting this weight, an importance field on persistent point density can be created. This provides an intuitive representation of persistence point importance that can be used to drive new visualizations. This work provides two examples: Visualization on each diagram directly and, in the case of sublevel set filtrations on images, directly on the images themselves. This work highlights real-world examples of this approach visualizing the important topological features in graph, 3D shape, and medical image data., Comment: 11 pages, 11 figures

We identify the progenitor star of SN 2023ixf in the nearby galaxy Messier 101 using Keck/NIRC2 adaptive optics imaging and pre-explosion HST/ACS images. The supernova position, localized with diffraction-spike pattern and high precision relative astrometry, unambiguously coincides with a single progenitor candidate of m_F814W=24.96(-0.04)(+0.05). Forced photometry further recovers 2-sigma detections in the F673N and F675W bands and imposes robust flux limits on the bluer bands. Given the reported infrared excess and semi-regular variability of the progenitor, we fit a time-dependent spectral energy distribution (SED) model of a dusty red supergiant (RSG) to a combined dataset of HST photometry, as well as ground-based near-infrared and Spitzer/IRAC [3.6], [4.5] photometry from the literature. The progenitor closely resembles a RSG of T_eff=3343+/-27 K and logL=5.10+/-0.02, with a 0.11+/-0.01 dex (25.2+/-1.7 per cent) variation over the mean luminosity at a period of P=1128.3+/-6.5 days, heavily obscured by a dust envelope with an optical depth of tau=2.83+/-0.03 at 1 micron (or A_V=10.28+/-0.11 mag). Such observed signatures match a post-main sequence star of 18.1(-1.2)(+0.7) Msun, close to the most massive SN II progenitor, with a pulsation-enhanced mass-loss rate of M_dot=(3.58+/-0.15) x 10^(-4) Msun/yr. The dense and confined circumstellar material is likely ejected during the last episode of radial pulsation before the explosion. Notably, we find strong evidence for periodic variation of tau (or both T_eff and tau) along with luminosity, a necessary assumption to reproduce the wavelength dependence of the variability, which implies dust sublimation and condensation during radial pulsations. Given the observed SED, partial dust obscuration remains a possible scenario, but any unobstructed binary companion over 7.1 Msun can be ruled out., Comment: submitted

The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance., Comment: Accepted at RecSys 2023

In this paper, we propose a dynamic grouping negotiation model for climate mitigation based on real-world business and political negotiation protocols. Within the AI4GCC competition framework, we develop a three-stage process: group formation and updates, intra-group negotiation, and inter-group negotiation. Our model promotes efficient and effective cooperation between various stakeholders to achieve global climate change objectives. By implementing a group-forming method and group updating strategy, we address the complexities and imbalances in multi-region climate negotiations. Intra-group negotiations ensure that all members contribute to mitigation efforts, while inter-group negotiations use the proposal-evaluation framework to set mitigation and savings rates. We demonstrate our negotiation model within the RICE-N framework, illustrating a promising approach for facilitating international cooperation on climate change mitigation., Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951)

The current framework for climate change negotiation models presents several limitations that warrant further research and development. In this track, we discuss mainly two key areas for improvement, focusing on the geographical impacts and utility framework. In the aspects of geographical impacts, We explore five critical aspects: (1) the shift from local to global impact, (2) variability in climate change effects across regions, (3) heterogeneity in geographical location and political structures, and (4) collaborations between adjacent nations, (5) the importance of including historical and cultural factors influencing climate negotiations. Furthermore, we emphasize the need to refine the utility and rewards framework to reduce the homogeneity and the level of overestimating the climate mitigation by integrating the positive effects of saving rates into the reward function and heterogeneity among all regions. By addressing these limitations, we hope to enhance the accuracy and effectiveness of climate change negotiation models, enabling policymakers and stakeholders to devise targeted and appropriate strategies to tackle climate change at both regional and global levels., Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951)

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to prioritize observational follow-ups before events fade away. We use host galaxy features to classify transients, thereby providing classification upon discovery. In contrast to past work that focused on distinguishing Type Ia and core-collapse supernovae (SNe) using host galaxy features that are not always accessible (e.g., morphology), we determine the relative likelihood across $12$ transient classes based on only 19 host apparent magnitudes and colors from $10$ optical and IR photometric bands. We develop both binary and multiclass classifiers, using kernel density estimation to estimate the underlying distribution of host galaxy properties for each transient class. Even in this pilot study, and ignoring relative differences in transient class frequencies, we distinguish eight transient classes at purities significantly above the 8.3% baseline (based on a classifier that assigns labels uniformly and at random): tidal disruption events ($48\%\pm27\%$, where $\pm$ indicates the 95% confidence limit), SNe Ia-91bg ($32\%\pm18\%$), SNe Ia-91T ($23\%\pm11\%$), SNe Ib ($23\%\pm13\%$), SNe II ($17\%\pm2\%$), SNe IIn ($17\%\pm6\%$), SNe II P ($16\%\pm4\%$), and SNe Ia ($10\%\pm1\%$). We demonstrate that our model is applicable to LSST and estimate that our approach may accurately classify 59% of LSST alerts expected each year for SNe Ia, Ia-91bg, II, Ibc, SLSN-I, and tidal disruption events. Our code and dataset are publically available., Comment: 25 pages, 12 figures, accepted for publication in ApJ

In the brain, cross-frequency coupling has been hypothesized to result from the activity of specialized microcircuits. For example, theta-gamma coupling is assumed to be generated by specialized cell pairs (PING and ING mechanisms), or special cells (e.g., fast bursting neurons). However, this implies that the generating mechanisms is uniquely specific to the brain. In fact, cross-scale coupling is a phenomenon encountered in the physics of all large, multi-scale systems: phase and amplitude correlations between components of different scales arise as a result of nonlinear interaction. Because the brain is a multi-scale system too, a similar mechanism must be active in the brain. Here, we represent brain activity as a superposition of nonlinearly interacting patterns of spatio-temporal activity (collective activity), supported by populations of neurons. Cross-frequency coupling is a direct consequence of the nonlinear interactions, and does not require specialized cells or cell pairs. It is therefore universal, and must be active in neural fields of any composition. To emphasize this, we demonstrate the phenomenon in excitatory fields. While there is no doubt that specialized cells play a role in theta-gamma coupling, our results suggest that the coupling mechanism is at the same time simpler and richer: simpler because it involves the universal principle of nonlinearity; richer, because nonlinearity of collective activity is likely modulated by specialized-cell populations in ways to be yet understood., Comment: 24 pages, 10 figures

In this study we derive the governing equations for mesoscopic collective activity in the cortex, starting from the generic Hodgkin-Huxley equations for microscopic cell dynamics. For simplicity, and to maintain focus on the essential elements of the derivation, the discussion is confined to excitatory neural fields. The fundamental assumption of the procedure is that mesoscale processes are macroscopic with respect to cell-scale activity, and emerge as the average behavior of a large population of cells. Because of their duration, action-potential details are assumed not observable at mesoscale; the essential mesoscopic function of action potentials is to redistribute energy in the neural field. The Hodgkin-Huxley dynamical model is first reduced to a set of equations that describe subthreshold dynamics. An ensemble average over a cell population then produces a closed system of equations involving two mesoscopic state variables: the density of kinetic energy J, carried by sodium ionic currents, and the excitability H of the neural field, which could be described as the average state of gating variable h. The resulting model is represented as essentially a subthreshold process; and the dynamical role of the firing rate is naturally reassessed as describing energy transfers. The linear properties of the equations are consistent with expectations for the dynamics of excitatory neural fields: the system supports oscillations of progressive waves, with shorter waves typically having higher frequencies, propagating slower, and decaying faster. Extending the derivation to include more complex cell dynamics (e.g., including other ionic channels, e.g., calcium channels) and multiple-type, excitatory-inhibitory, neural fields is straightforward, and will be presented elsewhere., Comment: 27 pages, 7 figures

We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example, the grounding process is usually a trivial language-location association without visual reasoning, e.g., grounding any language query containing sheep to the nearly central regions, due to that most queries about sheep have ground-truth locations at the image center. First, we frame the visual grounding pipeline into a causal graph, which shows the causalities among image, query, target location and underlying confounder. Through the causal graph, we know how to break the grounding bottleneck: deconfounded visual grounding. Second, to tackle the challenge that the confounder is unobserved in general, we propose a confounder-agnostic approach called: Referring Expression Deconfounder (RED), to remove the confounding bias. Third, we implement RED as a simple language attention, which can be applied in any grounding method. On popular benchmarks, RED improves various state-of-the-art grounding methods by a significant margin. Code will soon be available at: https://github.com/JianqiangH/Deconfounded_VG., Comment: AAAI 2022 Accepted

Understanding the preferences of transient types for host galaxies with certain characteristics is key to studies of transient physics and galaxy evolution, as well as to transient identification and classification in the LSST era. Here we describe a value-added database of extragalactic transients--supernovae, tidal disruption events, gamma-ray bursts, and other rare events--and their host galaxy properties. Based on reported coordinates, redshifts, and host galaxies (if known) of events, we cross-identify their host galaxies or most likely host candidates in various value-added or survey catalogs, and compile the existing photometric, spectroscopic, and derived physical properties of host galaxies in these catalogs. This new database covers photometric measurements from the far-ultraviolet to mid-infrared. Spectroscopic measurements and derived physical properties are also available for a smaller subset of hosts. For our 36333 unique events, we have cross-identified 13753 host galaxies using host names, plus 4480 using host coordinates. Besides those with known hosts, there are 18100 transients with newly identified host candidates. This large database will allow explorations of the connections of transients to their hosts, including a path toward transient alert filtering and probabilistic classification based on host properties., Comment: Accepted for publication in ApJS. 79 pages, 24 figures, 10 tables, 12 appendices

Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e.g. persons) in images. The perspective effect, which significantly influences the distribution of data points, plays an important role in crowd counting. In this paper, we propose a novel perspective-aware approach called PANet to address the perspective problem. Based on the observation that the size of the objects varies greatly in one image due to the perspective effect, we propose the dynamic receptive fields (DRF) framework. The framework is able to adjust the receptive field by the dilated convolution parameters according to the input image, which helps the model to extract more discriminative features for each local region. Different from most previous works which use Gaussian kernels to generate the density map as the supervised information, we propose the self-distilling supervision (SDS) training method. The ground-truth density maps are refined from the first training stage and the perspective information is distilled to the model in the second stage. The experimental results on ShanghaiTech Part_A and Part_B, UCF_QNRF, and UCF_CC_50 datasets demonstrate that our proposed PANet outperforms the state-of-the-art methods by a large margin., Comment: The paper is under consideration at Computer Vision and Image Understanding

We identify an effective proxy for the analytically-unknown second integral of motion (I_2) for rotating barred or tri-axial potentials. Planar orbits of a given energy follow a tight sequence in the space of the time-averaged angular momentum and its amplitude of fluctuation. The sequence monotonically traces the main orbital families in the Poincare map, even in the presence of resonant and chaotic orbits. This behavior allows us to define the "Calibrated Angular Momentum," the average angular momentum normalized by the amplitude of its fluctuation, as a numerical proxy for I_2. It also implies that the amplitude of fluctuation in L_z, previously under-appreciated, contains valuable information. This new proxy allows one to classify orbital families easily and accurately, even for real orbits in N-body simulations of barred galaxies. It is a good diagnostic tool of dynamical systems, and may facilitate the construction of equilibrium models., Comment: 7 pages, 4 figure. Published on ApJL

Persistence diagrams have been widely used to quantify the underlying features of filtered topological spaces in data visualization. In many applications, computing distances between diagrams is essential; however, computing these distances has been challenging due to the computational cost. In this paper, we propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances. This framework is built upon a generative adversarial network (GAN) with a diagram distance loss function to steer the learning process. Instead of using standard representations, we hash diagrams into binary codes, which have natural advantages in large-scale tasks. The training of this model is domain-oblivious in that it can be computed purely from synthetic, randomly created diagrams. As a consequence, our proposed method is directly applicable to various datasets without the need for retraining the model. These binary codes, when compared using fast Hamming distance, better maintain topological similarity properties between datasets than other vectorized representations. To evaluate this method, we apply our framework to the problem of diagram clustering and we compare the quality and performance of our approach to the state-of-the-art. In addition, we show the scalability of our approach on a dataset with 10k persistence diagrams, which is not possible with current techniques. Moreover, our experimental results demonstrate that our method is significantly faster with the potential of less memory usage, while retaining comparable or better quality comparisons., Comment: 11 pages

The Compact Spectrometer for Heavy Ion Experiment (CSHINE) is under construction for the study of isospin chronology via the Hanbury Brown$-$Twiss (HBT) particle correlation function and the nuclear equation of state of asymmetrical nuclear matter. The CSHINE consists of silicon strip detector (SSD) telescopes and large-area parallel plate avalanche counters, which measure the light charged particles and fission fragments, respectively. In phase I, two SSD telescopes were used to observe 30 MeV/u $^{40}$Ar +$^{197}$Au reactions. The results presented here demonstrate that hydrogen and helium were observed with high isotopic resolution, and the HBT correlation functions of light charged particles could be constructed from the obtained data.

Learning-based approaches, especially those based on deep networks, have enabled high-quality estimation of tissue microstructure from low-quality diffusion magnetic resonance imaging (dMRI) scans, which are acquired with a limited number of diffusion gradients and a relatively poor spatial resolution. These learning-based approaches to tissue microstructure estimation require acquisitions of training dMRI scans with high-quality diffusion signals, which are densely sampled in the q-space and have a high spatial resolution. However, the acquisition of training scans may not be available for all datasets. Therefore, we explore knowledge transfer between different dMRI datasets so that learning-based tissue microstructure estimation can be applied for datasets where training scans are not acquired. Specifically, for a target dataset of interest, where only low-quality diffusion signals are acquired without training scans, we exploit the information in a source dMRI dataset acquired with high-quality diffusion signals. We interpolate the diffusion signals in the source dataset in the q-space using a dictionary-based signal representation, so that the interpolated signals match the acquisition scheme of the target dataset. Then, the interpolated signals are used together with the high-quality tissue microstructure computed from the source dataset to train deep networks that perform tissue microstructure estimation for the target dataset. Experiments were performed on brain dMRI scans with low-quality diffusion signals, where the benefit of the proposed strategy is demonstrated., Comment: 4 pages, 2 figures

Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times can adjust the attention position, find the missing information back and avoid generating the wrong word. In this paper, we show that attending more times per word can gain improvements in the image captioning task, without increasing the number of parameters. We propose a flexible two-LSTM merge model to make it convenient to encode more attentions than words. Our captioning model uses two LSTMs to encode the word sequence and the attention sequence respectively. The information of the two LSTMs and the image feature are combined to predict the next word. Experiments on the MSCOCO caption dataset show that our method outperforms the state-of-the-art. Using bottom up features and self-critical training method, our method gets BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE scores of 0.381, 0.283, 0.580, 1.261 and 0.220 on the Karpathy test split.

The widespread use of mobile electronic devices increases the complexities of mobile security. This paper aims to provide a secure communication environment for smartphone users. Some research proves that the one-time pad is one of the securest encryption methods, and the key distribution problem can be solved by using the QKD (quantum key distribution). The objective of this project is to design an Android APP (application) to exchange several random keys between mobile phones. Inspired by QKD, the developed APP uses the quick response (QR) code as a carrier to dispatch large amounts of one-time keys. After evaluating the performance of APP, it allows the mobile phone to capture and decode 1800 bytes of random data in 600ms. The continuous scanning mode of APP is designed to improve the overall transmission performance and user experience, and the maximum transmission rate of this mode is around 2200 bytes/s. The omnidirectional readability and error correction capability of QR code gives it better real-life application, and the features of adequate storage capacity and quick response optimize overall transmission efficiency. The security of this APP is guaranteed since QR code is exchanged face-to-face, eliminating the risk of being eavesdropped. Also, the id of QR code is the only message that would be transmitted through the whole communication. The experimental results show this project can achieve superior transmission performance, and the correlation between the transmission rate of the system and several parameters, such as the QR code size, has been analyzed. In addition, some existing technologies and the main findings in the context of the project are summarized and critically compared in detail., Comment: The conclusion of this paper I recalculated and found to be wrong. Since I am afraid of causing future problems, please let me withdraw it

Using the LAMOST-Gaia common stars, we demonstrate that the in-plane velocity field for the nearby young stars are significantly different from that for the old ones. For the young stars, the probably perturbed velocities similar to the old population are mostly removed from the velocity maps in the $X$--$Y$ plane. The residual velocity field shows that the young stars consistently move along $Y$ with faster $v_\phi$ at the trailing side of the local arm, while at the leading side, they move slower in azimuth direction. At both sides, the young stars averagely move inward with $v_R$ of $-5\sim-3$ km s$^{-1}$. The divergence of the velocity in $Y$ direction implies that the young stars are associated with a density wave nearby the local arm. We therefore suggest that the young stars may reflect the formation of the local spiral arm by correlating themselves with a density wave. The range of the age for the young stars is around 2 Gyr, which is sensible since the transient spiral arm can sustain that long. We also point out that alternative explanations of the peculiar velocity field for the young population cannot be ruled out only from this observed data., Comment: 7 pages, 5 figures, accepted for publication in the ApJL

Recently, Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) detected gravitational wave (GW) transients from mergers of binary black holes (BHs). The system may also produce a wide-angle, relativistic outflow if the claimed short GRB detected by GBM is in real association with GW 150914. It was suggested that mergers of double neutron stars (or neutron star-black hole binaries), another promising source of GW transients, also produce fast, wide-angle outflows. In this paper, we calculate the high-energy gamma-ray emission arising from the blast waves driven by these wide-angle outflows. We find that TeV emission arising from the inverse-Compton process in the relativistic outflow resulted from mergers of binary BHs similar to those in GW 150914 could be detectable by ground-based IACT telescopes such as Cherenkov Telescope Array (CTA) if the sources occur in { a dense medium with density $n > 0.3 cm^{-3}$}. For neutron star-neutron star (NS-NS) and NS-BH mergers, TeV emission from the wide-angle, mildly-relativistic outflow could be detected as well if they occur in a dense medium {with $n > 10-100 cm^{-3}$.} Thus TeV afterglow emission would be a useful probe of the environment of the GW transients, which could shed light on the evolution channels of the progenitors of GW transients., Comment: 6 pages, 4 figures, accepted by ApJL

In recent years, many researches have been done to establish symbolic models of stateful protocols. Two works among them, the SAPIC tool and StatVerif tool, provide a high-level specification language and an automated analysis. Their language, the stateful applied \pi-calculus, is extended from the applied \pi-calculus by defining explicit state constructs. Symbolic abstractions of cryptography used in it make the analysis amenable to automation. However, this might overlook the attacks based on the algebraic properties of the cryptographic algorithms. In our paper, we establish the computational soundness results for stateful applied \pi-calculus used in SAPIC tool and StatVerif tool. In our approach, we build our results on the CoSP framework. For SAPIC, we embed the non-monotonic protocol states into the CoSP protocols, and prove that the resulting CoSP protocols are efficient. Through the embedding, we provide the computational soundness result for SAPIC (by Theorem 1). For StatVerif, we encode the StatVerif process into a subset of SAPIC process, and obtain the computational soundness result for StatVerif (by Theorem 2). Our encoding shows the differences between the semantics of the two languages. Our work inherits the modularity of CoSP, which allows for easily extending the proofs to specific cryptographic primitives. Thus we establish a computationally sound automated verification result for the input languages of SAPIC and StatVerif that use public-key encryption and signatures (by Theorem 3)., Comment: to appear in POST 2016

Although recent studies have found that the long-term correlations relating to the fat-tailed distribution of inter-event times exist in human activity, and that these correlations indicate the presence of fractality, the property of fractality and its origin have not been analyzed. We use both DFA and MFDFA to analyze the time series in online viewing activity separating from Movielens and Netflix. We find long-term correlations at both the individual and communal level, and that the extent of correlation at the individual level is determined by the activity level. These long-term correlations also indicate that there is fractality in the pattern of online viewing. And, we firstly find a multifractality that results from the combined effect of the fat-tailed distribution of inter-event times (i.e., the times between successive viewing actions of individual) and the long-term correlations in online viewing activity and verify this finding using three synthesized series. Therefore, it can be concluded that the multifractality in online viewing activity is caused by both the fat-tailed distribution of inter-event times and the long-term correlations, and that this enlarges the generic property of human activity to include not just physical space, but also cyberspace., Comment: 10 pages, 13 figures

Wavelength stability of free-electron lasers (FELs) is one of the important parameters for various applications. In this paper we describe two different methods to diagnose the wavelength stability of a mid-infrared (MIR) FEL. The first one is based on autocorrelation which is usually used to measure the pulse duration, and the second one is based on frequency upconversion through sum-frequency mixing (SFM).

We explore a second order Hamiltonian vertical resonance model for X-shaped or peanut-shaped galactic bulges. The X-shape is caused by the 2:1 vertical Lindblad resonance with the bar, with two vertical oscillation periods per orbital period in the bar frame. We examine N-body simulations and find that due to the bar slowing down and disk thickening during bar buckling, the resonance and associated peanut-shape moves outward. The peanut-shape is consistent with the location of the vertical resonance, independent of whether the bar buckled or not. We estimate the resonance width from the potential m=4 Fourier component and find that the resonance is narrow, affecting orbits over a narrow range in the angular momentum distribution, dL/L ~ 0.05. As the resonance moves outward, stars originally in the mid plane are forced out of the mid plane into orbits just within the resonance separatrix. The height of the separatrix orbits, estimated from the Hamiltonian model, is approximately consistent with the peanut-shape height. The X-shape is comprised of stars in the vicinity of the resonance separatrix. The velocity distributions from the simulations illustrate that low inclination orbits are depleted within resonance. Within resonance, the vertical velocity distribution is broad, consistent with resonant heating caused by the passage of the resonance through the disk. In the Milky Way bulge we relate the azimuthally averaged mid-plane mass density near the vertical resonance to the rotation curve and bar pattern speed. At an estimated vertical resonance galactocentric radius of ~1.3 kpc, we confirm a mid-plane density of ~5x10^8 Msol/kpc^3, consistent with recently estimated mass distributions. We find that the rotation curve, bar pattern speed, 2:1 vertical resonance location, X-shape tips, and mid-plane mass density, are all self-consistent in the Milky Way galaxy bulge., Comment: accepted for publication in MNRAS

The development of a one-dimensional, time-dependent model to study the removal of soluble gases from a warm, precipitating stratiform cloud is reported. The model calculates the distributions of water vapor and condensed water, in the form of cloud drops and raindrops, as well as the in-cloud concentration of a soluble species in the gas and aqueous phases for a specified profile of pressure and temperature and an assumed updraft velocity. Highly soluble gases are found to be rapidly dissolved into cloud droplets and then slowly incorporated into raindrops as cloudwater is converted to rainwater. The rainout rate for highly soluble species is found to be ultimately limited by the rate at which new gaseous material can be transferred from the cloud-free air into the cloud. The model calculations indicate that turbulence represents an important mechanism by which highly soluble gases are transported into stratiform clouds.