Miracle-dz/UniFAR-FTUs huggingface dataset:
The deformation space of geodesic triangulations
and generalized Tutte’s embedding theorem

We proved the contractibility of the deformation space of the geodesic triangulations on a closed surface of negative curvature. This solves an open problem proposed by Connelly et al. in 1983, in the case of hyperbolic surfaces. The main part of the proof is a generalization of Tutte's embedding theorem for closed surfaces of negative curvature.
The Nyquist soliton Kerr comb

Dissipative Kerr cavity solitons (DKSs) are localized particle-like wave packets that have attracted people’s great interests in the past decades. Besides being an excellent candidate for studying nonlinear physics, DKSs can also enable the generation of broadband frequency combs which have revolutionized a wide range of applications. The formation of DKSs are generally explained by a double balance mechanism. The group velocity dispersion is balanced by the Kerr effect; and the cavity loss is compensated by the parametric gain. Here, we show that DKSs can emerge through the interplay between dispersive loss and Kerr gain, without the participation of group velocity dispersion. By incorporating rectangular gate spectral filtering in a zero-dispersion coherently driven Kerr cavity, we demonstrate the generation of Nyquistpulse-like solitons with unprecedented ultra-flat spectra in the frequency domain. The discovery of pure dissipation enabled solitons reveals new insights into the cavity soliton dynamics, and provides a useful tool for spectral tailoring of Kerr frequency combs.
Lux: Always-on Visualization Recommendations for Exploratory Data Science

Exploratory data science largely happens in computational note-books with dataframe API, such as pandas , that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in data science workflows. When users print a dataframe in their notebooks, Lux recommends visualizations to provide a quick overview of the patterns and trends and suggests promising analysis directions. Lux features a high-level language for generating visualizations on-demand to encourage rapid visual experimentation with data. We demonstrate that through the use of a careful design and three system optimizations, Lux adds no more than two seconds of overhead on top of pandas for over 98% of datasets in the UCI repository. We evaluate Lux in terms of usability via a controlled first-use study and interviews with early adopters, finding that Lux helps fulfill the needs of data scientists for visualization support within their dataframe work-flows. Lux has already been embraced by data science practitioners, with over 1.9k stars on Github within its first 15 months.
Russian News Clustering and Headline Selection Shared Task

This paper presents the results of the Russian News Clustering and Headline Selection shared task. As a part of it, we propose the tasks of Russian news event detection, headline selection, and headline generation. These tasks are accompanied by datasets and baselines. The presented datasets for event detection and headline selection are the first public Russian datasets for their tasks. The headline generation dataset is based on clustering and provides multiple reference headlines for every cluster, unlike the previous datasets. Finally, the approaches proposed by the shared task participants are reported and analyzed.
SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera

Event cameras are novel neuromorphic vision sensors with ultrahigh temporal resolution and low latency, both in the order of microseconds. Instead of image frames, event cameras generate an asynchronous event stream of per-pixel intensity changes with precise timestamps. The resulting sparse data structure impedes applying many conventional computer vision techniques to event streams, and specific algorithms should be designed to leverage the information provided by event cameras. We propose a corner detection algorithm, eSUSAN, inspired by the conventional SUSAN (smallest univalue segment assimilating nucleus) algorithm for corner detection. The proposed eSUSAN extracts the univalue segment assimilating nucleus from the circle kernel based on the similarity across timestamps and distinguishes corner events by the number of pixels in the nucleus area. Moreover, eSUSAN is fast enough to be applied to CeleX-V, the event camera with the highest resolution available. Based on eSUSAN, we also propose the SE-Harris corner detector, which uses adaptive normalization based on exponential decay to quickly construct a local surface of active events and the event-based Harris detector to refine the corners identified by eSUSAN. We evaluated the proposed algorithms on a public dataset and CeleX-V data. Both eSUSAN and SE-Harris exhibit higher real-time performance than existing algorithms while maintaining high accuracy and tracking performance.
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a robust choice for optimizing relevance and fairness metrics. Unlike their deterministic counterparts that require heuristic optimization algorithms, PL models are fully differentiable. Theoretically, they can be used to optimize ranking metrics via stochastic gradient descent. However, in practice, the computation of the gradient is infeasible because it requires one to iterate over all possible permutations of items. Consequently, actual applications rely on approximating the gradient via sampling techniques. In this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model w.r. t. both relevance and fairness metrics. Unlike existing approaches that are based on policy gradients, PL-Rank makes use of the specific structure of PL models and ranking metrics. Our experimental analysis shows that PL-Rank has a greater sample-efficiency and is computationally less costly than existing policy gradients, resulting in faster convergence at higher performance. PL-Rank further enables the industry to apply PL models for more relevant and fairer real-world ranking systems.
Analysis of Screening Effects on Terahertz Photoconductive Devices Using a Fully-Coupled Multiphysics Approach

The terahertz current generated by a photoconductive device (PCD) saturates as the power of the input optical pump is increased. This behavior is induced by various screening effects that stem from the interactions between electromagnetic (EM) fields and semiconductor carriers. In this work, these screening effects are numerically analyzed for the first time using a fully-coupled multiphysics approach. Unlike the previously developed simulation frameworks, this approach rigorously models the nonlinear coupling between the EM fields and the carriers and therefore is capable of accounting for the screening effects. It is demonstrated that the results obtained using this multiphysics approach and actual experiments are in excellent agreement. The optical- and radiation-field screening effects are identified in the simulation results and the optical-field screening is found to play a more dominant role in the saturation of the PCD output under high optical pump power levels.
S3Net: A Single Stream Structure for Depth Guided Image Relighting

Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map. To the best of our knowledge, this task is a new challenge that has not been addressed in the previous literature. To address this issue, we propose a deep learning-based neural Single Stream Structure network called S3Net for depth guided image relighting. This network is an encoder-decoder model. We concatenate all images and corresponding depth maps as the input and feed them into the model. The decoder part contains the attention module and the enhanced module to focus on the relighting-related regions in the guided images. Experiments performed on challenging benchmark show that the proposed model achieves the 3rd highest SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.
Embracing Data Incompleteness for Better Earthquake Forecasting

We propose two methods to calibrate the parameters of the epidemic‐type aftershock sequence (ETAS) model based on expectation maximization (EM) while accounting for temporal variation of catalog completeness. The first method allows for model calibration on long‐term earthquake catalogs with temporal variation of the completeness magnitude, mc. This calibration technique is beneficial for long‐term probabilistic seismic hazard assessment (PSHA), which is often based on a mixture of instrumental and historical catalogs. The second method generalizes the concept of mc, considering rate‐ and magnitude‐dependent detection probability, and allows for self‐consistent estimation of ETAS parameters and high‐frequency detection incompleteness. With this approach, we aim to address the potential biases in parameter calibration due to short‐term aftershock incompleteness, embracing incompleteness instead of avoiding it. Using synthetic tests, we show that both methods can accurately invert the parameters of simulated catalogs. We then use them to estimate ETAS parameters for California using the earthquake catalog since 1932. To explore how model calibration, inclusion of small events, and accounting for short‐term incompleteness affect earthquakes' predictability, we systematically compare variants of ETAS models based on the second approach in pseudo‐prospective forecasting experiments for California. Our proposed model significantly outperforms the ETAS null model, with decreasing information gain for increasing target magnitude threshold. We find that the ability to include small earthquakes for simulation of future scenarios is the primary driver of the improvement and that accounting for incompleteness is necessary. Our results have significant implications for our understanding of earthquake interaction mechanisms and the future of seismicity forecasting.
Interacting electron spins in 
κ−(BEDT-TTF)2Cu[N(CN)2]I
 investigated by ESR spectroscopy

Lena Nadine Majer, Björn Miksch, Olga Iakutkina, Takuya Kobayashi, Atsushi Kawamoto, and Martin Dressel 1. Physikalisches Institut, Universität Stuttgart, Pfaffenwaldring 57, 70569 Stuttgart, Germany Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan Department of Condensed Matter Physics, Graduate School of Science, Hokkaido University, Sapporo 060-0810, Japan (Dated: May 4, 2021)
A Brief Survey And Investigation Of Hybrid Beamforming For Millimeter Waves In 5G Massive MIMO Systems

Millimeter-wave (mm-wave) is a promising technique to enhance the network capacity and coverage of next-generation (5G) based on utilizing a great number of available spectrum resources in mobile communication. Improving the 5G network requires enhancing and employing mm-wave beamforming channel propagation characteristics. To achieve high data rates, system performance remains a challenge given the impact of propagation channels in mm-wave that is insufficient in both path loss, delay spread, and penetration loss. Additional challenges arise due to high cost and energy consumption, which require combining both analog and digital beamforming (hybrid beamforming) to reduce the number of radio frequency (RF) chains. In this paper, the distributed powers in the small cell to suppress path loss by specifying a considerable power and controlling the distributed power to reduce the high cost and energy consumption was proposed. The hybrid beamforming in mm-wave exploits a large bandwidth which reduces the large path loss in Rayleigh fading channel. Also, the trade-off between the energy consumption of RF chains and cost efficiency depends on reducing the number of RF chains and the distributed number of users. This paper finds that hybrid beamforming for massive multiple? input multiple-output (MIMO) systems constitute a promising platform for advancing and capitalizing on 5G networks
APEX: A High-Performance Learned Index on Persistent Memory

The recently released persistent memory (PM) offers high performance, persistence, and is cheaper than DRAM. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. However, none support persistence or instant recovery, and existing PM-based indexes typically evolve B+-trees without considering learned indexes. This paper proposes APEX, a new PM-optimized learned index that offers high performance, persistence, concurrency, and instant recovery. APEX is based on ALEX, a state-of-the-art updatable learned index, to combine and adapt the best of past PM optimizations and learned indexes, allowing it to reduce PM accesses while still exploiting machine learning. Our evaluation on Intel DCPMM shows that APEX can perform up to ~15× better than existing PM indexes and can recover from failures in ~42ms.
DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems

It is important for sociable recommendation dialog systems to perform as both on-task content and social content to engage users and gain their favor. In addition to understand the user preferences and provide a satisfying recommendation, such systems must be able to generate coherent and natural social conversations to the user. Traditional dialog state tracking cannot be applied to such systems because it does not track the attributes in the social content. To address this challenge, we propose DEUX, a novel attribute-guided framework to create better user experiences while accomplishing a movie recommendation task. DEUX has a module that keeps track of the movie attributes (e.g., favorite genres, actors,etc.) in both user utterances and system responses. This allows the system to introduce new movie attributes in its social content. Then, DEUX has multiple values for the same attribute type which suits the recommendation task since a user may like multiple genres, for instance. Experiments suggest that DEUX outperforms all the baselines on being more consistent, fitting the user preferences better, and providing a more engaging chat experience. Our approach can be used for any similar problems of sociable task-oriented dialog system.
Sharpening quasar absorption lines with ESPRESSO. Temperature of warm gas at z ~ 2, constraints on the Mg isotopic ratio, and structure of cold gas at z ~ 0.5

We present a high-resolution (R=140,000) spectrum of the bright quasar HE0001-2340 (z=2.26), obtained with ESPRESSO at the Very Large Telescope. We analyse three systems at z=0.45, z=1.65, and z=2.19 using multiple-component Voigt-profile fitting. We also compare our spectrum with those obtained with VLT/UVES, covering a total period of 17 years. We disentangle turbulent and thermal broadening in many components spread over about 400 km/s in the z~2.19 sub-DLA system. We derive an average temperature of 16000+/-1300 K, i.e., about twice the canonical value of the warm neutral medium in the Galactic interstellar medium. A comparison with other high-z, low-metallicity absorbers reveals an anti-correlation between gas temperature and total HI column density. Although requiring confirmation, this could be the first observational evidence of a thermal decrease with galacto-centric distance, i.e., we may be witnessing a thermal transition between the circum-galactic medium and the cooler ISM. We revisit the Mg isotopic ratios at z=0.45 and z=1.65 and constrain them to be xi = (26Mg+25Mg)/24Mg <0.6 and <1.4 in these two systems, respectively. These values are consistent with the standard Solar ratio, i.e., we do not confirm strong enhancement of heavy isotopes previously inferred from UVES data. Finally, we confirm the partial coverage of the quasar emission-line region by a FeI-bearing cloud in the z=0.45 system and present evidence for velocity sub-structure of the gas that has Doppler parameters of the order of only ~0.3 km/s. This work demonstrates the uniqueness of high-fidelity, high-resolution optical spectrographs on large telescopes as tools to investigate the thermal state of the gas in and around galaxies as well as its spatial and velocity structure on small scales, and to constrain the associated stellar nucleosynthetic history. [abridged]
Cybersecurity in Power Grids: Challenges and Opportunities

Increasing volatilities within power transmission and distribution force power grid operators to amplify their use of communication infrastructure to monitor and control their grid. The resulting increase in communication creates a larger attack surface for malicious actors. Indeed, cyber attacks on power grids have already succeeded in causing temporary, large-scale blackouts in the recent past. In this paper, we analyze the communication infrastructure of power grids to derive resulting fundamental challenges of power grids with respect to cybersecurity. Based on these challenges, we identify a broad set of resulting attack vectors and attack scenarios that threaten the security of power grids. To address these challenges, we propose to rely on a defense-in-depth strategy, which encompasses measures for (i) device and application security, (ii) network security, and (iii) physical security, as well as (iv) policies, procedures, and awareness. For each of these categories, we distill and discuss a comprehensive set of state-of-the art approaches, as well as identify further opportunities to strengthen cybersecurity in interconnected power grids.
Switching between relaxation hotspots and coldspots in disordered spin qubits

We develop a valley-dependent envelope function theory that can describe the effects of arbitrary configurations of interface steps and miscuts on the qubit relaxation time. For a given interface roughness, we show how our theory can be used to find the valley-dependent dipole matrix elements, the valley splitting, and the spin-valley coupling as a function of the electromagnetic fields in a Si/SiGe quantum dot spin qubit. We demonstrate that our theory can quantitatively reproduce and explain the result of experimental measurements for the spin relaxation time with only a minimal set of free parameters. Investigating the sample dependence of spin relaxation, we find that at certain conditions for a disordered quantum dot, the spin-valley coupling vanishes. This, in turn, completely blocks the valley-induced qubit decay. We show that the presence of interface steps can in general give rise to a strongly anisotropic behavior of the spin relaxation time. Remarkably, by properly tuning the gate-induced out-of-plane electric field, it is possible to turn the spin-valley hotspot into a “coldspot” at which the relaxation time is significantly prolonged and where the spin relaxation time is additionally first-order insensitive to the fluctuations of the magnetic field. This electrical tunability enables on-demand fast qubit reset and initialization that is critical for many quantum algorithms and error correction schemes. We therefore argue that the valley degree of freedom can be used as an advantage for Si spin qubits.
Deep Insights of Deepfake Technology : A Review

: Under the aegis ofof computer vision and deep learning technology, a new emerging techniques has introduced that anyone can make highly realistic but fake videos, images even can manipulates the voices. And this technology is widely known as Deepfake Technology. Although it seems interesting techniques to make fake videos or image of something or some individuals but it could spread as misinformation via internet. Deepfake contents could be dangerous for individuals as well as for our communities, organizations, countries religions etc. As Deepfake content creation involve a high level expertise with combination of several algorithms of deep learning, it seems almost real and genuine and difficult to differentiate. In this paper, a wide range of articles have been examined to understand Deepfake technology more extensively. We have examined several articles to find some insights such as what is Deepfake, who are the responsible for this, is there any benefits of Deepfake and what are the challenges of this technology. We have also examined several creation and detection techniques. Our results found that although Deepfake is threat to our societies, proper measures and strict regulations could prevent this.
A Geo-Gender Study of Indexed Computer Science Research Publications

This paper presents a study that analyzes and gives quantitative means for measuring the gender gap in computing research publications. The data set built for this study is a geo-gender tagged authorship database named authorships that integrates data from computing journals indexed in the Journal Citation Reports (JCR) and the Microsoft Academic Graph (MAG). We propose a gender gap index to analyze female and male authors' participation gap in JCR publications in Computer Science. Tagging publications with this index, we can classify papers according to the degree of participation of both women and men in different domains. Given that working contexts vary for female scientists depending on the country, our study groups analytics results according to the country of authors affiliation institutions. The paper details the method used to obtain, clean and validate the data, and then it states the hypothesis adopted for defining our index and classifications. Our study results have led to enlightening conclusions concerning various aspects of female authorship's geographical distribution in computing JCR publications.
Waypoint Planning Networks

With the recent advances in machine learning, path planning algorithms are also evolving; however, the learned path planning algorithms often have difficulty competing with success rates of classic algorithms. We propose waypoint planning networks (WPN), a hybrid algorithm based on LSTMs with a local kernel—a classic algorithm such as A*, and a global kernel using a learned algorithm. WPN produces a more computationally efficient and robust solution. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value iteration networks (VIN). In this paper, the design and experiments have been conducted for 2D environments. Experimental results outline the benefits of WPN, both in efficiency and generalization. It is shown that WPN’s search space is considerably less than A*, while being able to generate near optimal results. Additionally, WPN works on partial maps, unlike A* which needs the full map in advance. The code is available online 1.