Miracle-dz/UniFAR-FTUs huggingface dataset:
AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries

Edge computing offers the distinct advantage of harnessing compute capabilities on resources located at the edge of the network to run workloads of relatively weak user devices. This is achieved by offloading computationally intensive workloads, such as deep learning from user devices to the edge. Using the edge reduces the overall communication latency of applications as workloads can be processed closer to where data is generated on user devices rather than sending them to geographically distant clouds. Specialised hardware accelerators, such as Graphics Processing Units (GPUs) available in the cloud-edge network can enhance the performance of computationally intensive workloads that are offloaded from devices on to the edge. The underlying approach required to facilitate this is virtualization of GPUs. This paper therefore sets out to investigate the potential of GPU accelerator virtualization to improve the performance of deep learning workloads in a cloud-edge environment. The AVEC accelerator virtualization framework is proposed that incurs minimum overheads and requires no source-code modification of the workload. AVEC intercepts local calls to a GPU on a device and forwards them to an edge resource seamlessly. The feasibility of AVEC is demonstrated on a real-world application, namely OpenPose using the Caffe deep learning library. It is observed that on a lab-based experimental test-bed AVEC delivers up to 7.48x speedup despite communication overheads incurred due to data transfers.
A compact core-jet structure in the changing-look Seyfert NGC 2617

The nearby face-on spiral galaxy NGC 2617 underwent an unambiguous ‘inside–out’ multiwavelength outburst in Spring 2013, and a dramatic Seyfert-type change probably between 2010 and 2012, with the emergence of broad optical emission lines. To search for the jet activity associated with this variable accretion activity, we carried out multiresolution and multiwavelength radio observations. Using the very long baseline interferometric (VLBI) observations with the European VLBI Network at 1.7 and 5.0 GHz, we find that NGC 2617 shows a partially synchrotron self-absorbed compact radio core with a significant core shift, and an optically thin steep-spectrum jet extending towards the north up to about 2 pc in projection. We also observed NGC 2617 with the electronic Multi-Element Remotely Linked Interferometer Network at 1.5 and 5.5 GHz, and revisited the archival data of the Very Large Array (VLA) and the Very Long Baseline Array (VLBA). The radio core had a stable flux density of ∼1.4 mJy at 5.0 GHz between 2013 June and 2014 January, in agreement with the expectation of a supermassive black hole in the low accretion rate state. The northern jet component is unlikely to be associated with the ‘inside–out’ outburst of 2013. Moreover, we report that most optically selected changing-look active galactic nuclei (AGN) at z < 0.83 are sub-mJy radio sources in the existing VLA surveys at 1.4 GHz, and it is unlikely that they are more active than normal AGN at radio frequencies.
Learn to Differ: Sim2Real Small Defection Segmentation Network

Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the network inevitably learns the representation of the background of the training data before figuring out the defection. They underperform in the inference stage once the context changed and can only be solved by training in every new settings. This eventually leads to the limitation in practical robotic applications where contexts keep varying. To cope with this, instead of training a network context by context and hoping it to generalize, why not stop misleading it with any limited context and start training it with pure simulation? In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all. A small defection detection layer utilizing the pose sensitivity of phase correlation between images is introduced and is followed by an outlier masking layer. The network is trained on randomly generated simulated data with simple shapes and is generalized across the real world. Finally, SSDS is validated on real-world collected data and demonstrates the ability that even when trained in cheap simulation, SSDS can still find small defections in the real world showing the effectiveness and its potential for practical applications. Code is available here
THE FINITE-SAMPLE DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS AND UNIFORM VERSUS NONUNIFORM APPROXIMATIONS

In Pötscher (1991, Econometric Theory 7, 163–185) the asymptotic distribution of a post-model-selection estimator, both unconditional and conditional on selecting a correct model (minimal or not), has been derived. Limitations of these results are (i) that they do not provide information on the distribution of the post-model-selection estimator conditional on selecting an incorrect model and (ii) that the quality of this asymptotic approximation to the finite-sample distribution is not uniform with respect to the underlying parameters. In the present paper we first obtain the unconditional and also the conditional finite-sample distribution of the post-model-selection estimator, which turn out to be complicated and difficult to interpret. Second, we obtain approximations to the finite-sample distributions that are as simple and easy to interpret as the asymptotic distributions obtained in Pötscher (1991) but at the same time are close to the finite-sample distributions uniformly with respect to the underlying parameters. As a by-product, we also obtain the asymptotic distribution conditional on selecting an incorrect model.We thank the co-editor Richard Smith and the two referees for helpful comments on a previous version of this paper. Hannes Leeb's research was supported by the Austrian Science Foundation (FWF), project P13868-MAT.
Prevalent behavior of smooth strongly monotone discrete-time dynamical systems

For C-smooth strongly monotone discrete-time dynamical systems, it is shown that “convergence to linearly stable cycles” is a prevalent asymptotic behavior in the measuretheoretic sense. The results are then applied to classes of time-periodic parabolic equations and give new results on prevalence of convergence to periodic solutions. In particular, for equations with Neumann boundary conditions on convex domains, we show the prevalence of the set of initial conditions corresponding to the solutions that converge to spatiallyhomogeneous periodic solutions. While, for equations on radially symmetric domains, we obtain the prevalence of the set of initial values corresponding to solutions that are asymptotic to radially symmetric periodic solutions.
Changes in electric field noise due to thermal transformation of a surface ion trap

We aim to illuminate how the microscopic properties of a metal surface map to its electric-ﬁeld noise characteristics. In our system, prolonged heat treatments of a metal ﬁlm can induce a rise in the magnitude of the electric-ﬁeld noise generated by the surface of that ﬁlm. We refer to this heat-induced rise in noise magnitude as a thermal transformation. The underlying physics of this thermal transformation process is explored through a series of heating, milling, and electron treatments performed on a single surface ion trap. Between these treatments, 40 Ca + ions trapped 70 µ m above the surface of the metal are used as detectors to monitor the electric-ﬁeld noise at frequencies close to 1 MHz. An Auger spectrometer is used to track changes in the composition of the contaminated metal surface. With these tools we investigate contaminant deposition, chemical reactions, and atomic restructuring as possible drivers of thermal transformations.
A coordination-free, convergent, and safe replicated tree

The tree is an essential data structure in many applications. In a distributed application, such as a distributed file system, the tree is replicated.To improve performance and availability, different clients should be able to update their replicas concurrently and without coordination. Such concurrent updates converge if the effects commute, but nonetheless, concurrent moves can lead to incorrect states and even data loss. Such a severe issue cannot be ignored; ultimately, only one of the conflicting moves may be allowed to take effect. However, as it is rare, a solution should be lightweight. Previous approaches would require preventative cross-replica coordination, or totally order move operations after-the-fact, requiring roll-back and compensation operations. In this paper, we present a novel replicated tree that supports coordination-free concurrent atomic moves, and provably maintains the tree invariant. Our analysis identifies cases where concurrent moves are inherently safe, and we devise a lightweight, coordination-free, rollback-free algorithm for the remaining cases, such that a maximal safe subset of moves takes effect. We present a detailed analysis of the concurrency issues with trees, justifying our replicated tree data structure. We provide mechanized proof that the data structure is convergent and maintains the tree invariant. Finally, we compare the response time and availability of our design against the literature.
Weather Analogs with a Machine Learning Similarity Metric for Renewable Resource Forecasting

The Analog Ensemble (AnEn) technique is a technique that has been shown effective on several weather problems. Unlike previous weather analogs that are sought within a large spatial domain and an extended temporal window, AnEn strictly confines space and time, and independently generates results at each grid point within a short time window. AnEn can find similar forecasts that lead to accurate and calibrated ensemble forecasts. The central core of the AnEn technique is a similarity metric that sorts historical forecasts with respect to a new target prediction. A commonly used metric is a Euclidean distance function which computes the distance between a target and a historical set of forecasts using a weighted difference of all multivariate parameters, normalized by the standard deviation of the historical set. A significant difficulty using this metric is the definition of the weights for all the parameters. Generally, the AnEn methodology starts with a feature selection task, where a subset of parameters are selected, and then weights are identified using heuristics or an optimization process. While this method has been proven effective, it is expedient and error inducing. AnEn is, in fact, characterized by a systematic bias when applied to extreme events, and this bias can potentially be hard to compensate if only a limited set of historical forecasts is available. This paper proposes a novel definition of weather analogs through a Machine Learning (ML) based similarity metric. The similarity metric uses neural networks that are trained and instantiated to search for weather analogs. This new metric allows incorporating all variables without requiring a prior feature selection and weight optimization. Experiments are presented on the application of this new metric to forecast wind speed and solar irradiance. Results show that the ML metric generally outperforms the original metric. The ML metric has a better capability to correct for larger errors and to take advantage of a larger search repository. Spatial predictions using a learned metric also show the ability to define effective latent features that are transferable to other locations. ∗Corresponding author. weiming@psu.edu. 205 Walker Building, University Park, PA, 16802. http:
On the light front surfaces in the phase space of hadrons in heavy-ion collisions

Surfaces corresponding to the constant values of the light front variable forms paraboloids in the phase space of the inclusively produced hadrons in heavy-ion collisions. In this paper, it is demonstrated with the simulated Au-Au collisions at √ s = 200 GeV using the UrQMD event generator that the paraboloidal surface defined by a certain constant value of the light front variable divides the particles in the phase space into two groups such that the distributions of the square of the transverse momentum, the polar angle and the light front variable of particles belonging to one among them can be described with the Boltzmann distribution parameterised by the mass-dependent temperature.
Observation of inverse magnetocaloric effect in magnetic-field-induced austenite phase of Heusler alloys 
Ni50−xCoxMn31.5Ga18.5
 
(x=9 and 9.7)

Observation of inverse magnetocaloric effect in magnetic-field-induced austenite phase of Heusler Alloys Ni50-xCoxMn31.5Ga18. 5 (x = 9 and 9.7) T. Kihara*, T. Roy, X. Xu, A. Miyake, M. Tsujikawa, H. Mitamura M. Tokunaga, Y. Adachi, T. Eto, and T. Kanomata 1 Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan 2 Research Institute of Electrical Communication, Tohoku University, Sendai, Miyagi, Japan 3 Department of Materials Science, Tohoku University, Sendai, Miyagi, Japan 4 The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Chiba, Japan 5 Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, Japan 6 Kurume Institute of Technology, Kurume, Fukuoka, Japan 7 Research Institute for Engineering and Technology, Tohoku Gakuin University, Tagajo, Miyagi, Japan
CUHK-EE voice cloning system for ICASSP 2021 M2VoC challenge

This paper presents the CUHK-EE voice cloning system for ICASSP 2021 M2VoC challenge. The challenge provides two Mandarin speech corpora: the AIShell-3 corpus of 218 speakers with noise and reverberation and the MST corpus including high-quality speech of one male and one female speakers. 100 and 5 utterances of 3 target speakers in different voice and style are provided in track 1 and 2 respectively, and the participants are required to synthesize speech in target speaker's voice and style. We take part in the track 1 and carry out voice cloning based on 100 utterances of target speakers. An end-to-end voicing cloning system is developed to accomplish the task, which includes: 1. a text and speech front-end module with the help of forced alignment, 2. an acoustic model combining Tacotron2 and DurIAN to predict melspectrogram, 3. a Hifigan vocoder for waveform generation. Our system comprises three stages: multi-speaker training stage, target speaker adaption stage and target speaker synthesis stage. Our team is identified as T17. The subjective evaluation results provided by the challenge organizer demonstrate the effectiveness of our system. Audio samples are available at our demo page: https://daxintan-cuhk.github. io/CUHK-EE-system-M2VoC-challenge/ .
Reconfigurable Intelligent Surfaces for Rich Scattering Wireless Communications: Recent Experiments, Challenges, and Opportunities

Recent advances in the fabrication and experimentation of reconfigurable intelligent surfaces (RISs) have motivated the concept of the smart radio environment, according to which the propagation of information-bearing waveforms in the wireless medium is amenable to programmability. Although the vast majority of recent experimental research on RIS-empowered wireless communications gravitates around narrowband beamforming in quasi-free space, RISs are foreseen to revolutionize wideband wireless connectivity in dense urban as well as indoor scenarios, which are usually characterized as strongly reverberant environments exhibiting severe multipath conditions. In this article, capitalizing on recent physics-driven experimental explorations of RIS-empowered wave propagation control in complex scattering cavities, we identify the potential of the spatio-temporal control offered by RISs to boost wireless communications in rich scattering channels via two case studies. First, an RIS is deployed to shape the multipath channel impulse response, which is shown to enable higher achievable communication rates. Second, the RIS-tunable propagation environment is leveraged as an analog multiplexer to localize non-cooperative objects using wave fingerprints, even when they are outside the line of sight. Future research challenges and opportunities in the algorithmic design and experimentation of smart rich scattering wireless environments enabled by RISs for sixth generation wireless communications are discussed.
Auto-tuning of Deep Neural Networks by Conflicting Layer Removal

Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this paper, we introduce a novel methodology to identify layers that decrease the test accuracy of trained models. Conflicting layers are detected as early as the beginning of training. In the worst-case scenario, we prove that such a layer could lead to a network that cannot be trained at all. A theoretical analysis is provided on what is the origin of those layers that result in a lower overall network performance, which is complemented by our extensive empirical evaluation. More precisely, we identified those layers that worsen the performance because they would produce what we name conflicting training bundles. We will show that around 60% of the layers of trained residual networks can be completely removed from the architecture with no significant increase in the test-error. We will further present a novel neural-architecture-search (NAS) algorithm that identifies conflicting layers at the beginning of the training. Architectures found by our auto-tuning algorithm achieve competitive accuracy values when compared against more complex state-of-the-art architectures, while drastically reducing memory consumption and inference time for different computer vision tasks. The source code is available on https://github.com/peerdavid/conflicting-bundles
Uplink Power Control in Massive MIMO With Double Scattering Channels

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion.
FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation

Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical flow estimation. However, current networks often consist of a large number of parameters and require heavy computation costs, largely hindering its application on low power-consumption devices such as mobile phones. In this paper, we tackle this challenge and design a lightweight model for fast and accurate optical flow prediction. Our proposed FastFlowNet follows the widely-used coarse-to-fine paradigm with following innovations. First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed to intensify high-resolution pyramid features while reducing parameters. Second, we introduce a new center dense dilated correlation (CDDC) layer for constructing compact cost volume that can keep large search radius with reduced computation burden. Third, an efficient shuffle block decoder (SBD) is implanted into each pyramid level to accelerate flow estimation with marginal drops in accuracy. Experiments on both synthetic Sintel data and real-world KITTI datasets demonstrate the effectiveness of the proposed approach, which needs only 1/10 computation of comparable networks to achieve on par accuracy. In particular, FastFlowNet only contains 1.37M parameters; and can execute at 90 FPS (with a single GTX 1080Ti) or 5.7 FPS (embedded Jetson TX2 GPU) on a pair of Sintel images of resolution 1024 × 436.Code is available at: https://git.io/fastflow
Mapping Milky Way Halo Substructure Using Stars in the Extended Blue Tail of the Horizontal Branch

Although blue horizontal branch (BHB) stars are commonly used to trace halo substructure, the stars bluer than (g − r) < −0.3 are ignored owing to the difficulty in determining their absolute magnitudes. The blue extension of the horizontal branch (HBX) includes BHB tail stars and extreme horizontal branch (EHB) stars. We present a method for identifying HBX stars in the field, using spectra and photometry from the Sloan Digital Sky Survey Data Release 14 (SDSS DR14). We derive an estimate for the absolute magnitudes of BHB tail and EHB stars as a function of color and use this relationship to calculate distances. We identify an overdensity of HBX stars that appears to trace the northern end of the Hercules-Aquila Cloud. We identify three stars that are likely part of a tidal stream, but this is not enough stars to explain the observed overdensity. Combining SDSS data with Gaia DR2 proper motions allows us to show that the majority of the HBX stars in the overdensity are on high-eccentricity orbits similar to those in the Virgo Radial Merger/Gaia–Enceladus/Gaia Sausage structure and that the overdensity of high-eccentricity orbits extends all the way to the Virgo Overdensity. We use stellar kinematics to separate the HBX stars into disk stars and halo stars. The halo stars are primarily on highly eccentric (radial) orbits. The fraction of HBX stars that are EHBs is highest in the disk population and lowest in the low-eccentricity halo stars.
Learning to Predict Vehicle Trajectories with Model-based Planning

Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this paper, we introduce a novel prediction framework called PRIME, which stands for Prediction with Model-based Planning. Unlike recent prediction works that utilize neural networks to model scene context and produce unconstrained trajectories, PRIME is designed to generate accurate and feasibility-guaranteed future trajectory predictions. PRIME guarantees the trajectory feasibility by exploiting a model-based generator to produce future trajectories under explicit constraints and enables accurate multimodal prediction by utilizing a learning-based evaluator to select future trajectories. We conduct experiments on the large-scale Argoverse Motion Forecasting Benchmark, where PRIME outperforms the state-of-the-art methods in prediction accuracy, feasibility, and robustness under imperfect tracking.
SYNTHESIS AND CHARACTERIZATION OF 2,5-DIHYDROXY SUBSTITUTED CHALCONES USING SOCL 2/ETOH

A novel method for the synthesis of 2,5-dihydroxy substituted chalcones via Aldol condensation is introduced. The HCl is generated in situ by the reaction of SoCl 2 with absolute ethanol. In the presence of SOCl 2 / EtOH as a catalyst, The structures of the synthesized compounds were confirmed by IR, mass spectroscopy and elemental analysis.