Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Color Attenuation Priori Dehazing Algorithm and Its Hardware Implementation

An improved color attenuation priori dehazing algorithm is proposed in this paper. The performance of the improved algorithm is tested with MATALAB program. The hardware architecture for the improved algorithm is designed, the RTL codes of the architecture are programed, simulated, and synthesized to Altera’s FPGA. Experiment results show that the improved algorithm is efficient for image dehazing and the hardware architecture designed in this paper is correct.
RAG: Facial Attribute Editing by Learning Residual Attributes

Facial attribute editing aims to modify face images in the desired manner, such as changing hair color, gender, and age, adding or removing eyeglasses, and so on. Recent researches on this topic largely leverage the adversarial loss so that the generated faces are not only realistic but also well correspond to the target attributes. In this paper, we propose Residual Attribute Generative Adversarial Network (RAG), a novel model to achieve unpaired editing for multiple facial attributes. Instead of directly learning the target attributes, we propose to learn the residual attributes, a more intuitive and understandable representation to convert the original task as a problem of arithmetic addition or subtraction for different attributes. Furthermore, we propose the identity preservation loss, which proves to facilitate convergence and provide better results. At last, we leverage effective visual attention to localize the related regions and preserve the unrelated content during transformation. The extensive experiments on two facial attribute datasets demonstrate the superiority of our approach to generate realistic and high-quality faces for multiple attributes. Visualization of the residual image, which is defined as the difference between the original image and the generated result, better explains which regions RAG focuses on when editing different attributes.
Effective Activating Compensation Logic for Drams in 3D-ICs

3D-IC is a promising solution to the “Memory Wall”. The transmission issue of data between processor and memory will be migrated in this way, and the high bandwidth can achieve further performance improvement. However, high operation temperature and long-term stress condition induce more severe Bias Temperature Instability (BTI) effect in 3D-IC. This paper explores the activation failure of DRAM caused by BTI effect in 3D stacked system which degrades circuit functionality over time. Meanwhile, additional compensation logic is proposed to compensate the sensing failure. The charge saving in memory storage cell when being activated can obtain about 15% improvement in the scenario of heavy unbalanced workload.
Toward a Feedback-Insensitive Semiconductor Laser

We present a route toward design and fabrication of a conceptually new type of optical feedback insensitive integrated semiconductor ring laser with a weak optical isolator included in the ring. Although the approach has not resulted in the clear advantages that were aimed at, this detailed account of the process we believe is valuable to readers active in the field. After a summary of optical-feedback sensitivity in semiconductor lasers, we review various attempts toward reducing this sensitivity and discuss the feasibility of the concept of weak intra-cavity isolation. The design, model and theory of the novel integrated ring laser device are presented. The observed 5 dB of isolation and 3 dB increase of tolerance for external optical feedback demonstrate the feasibility of the intra-cavity isolation concept. However, the observed output performance of the single-mode laser, both with and without activated intra-cavity isolation, suggests the presence of a spurious intra-cavity reflection, strongly coupling the directional modes. Due to this coupling, the sensitivity to external optical feedback is comparable to that of a Fabry–Pérot type laser. Therefore, an alternate route is indicated toward a less complex laser cavity with potentially less intra-cavity reflection and better tolerance for external optical feedback.
Joint Integer-Forcing Precoder Design for MIMO Multiuser Relay System

In this paper, we study a relay-aided multiuser amplify-and-forward relaying system where a multi-antenna base station (BS) transmits downlink to multiple single-antenna mobile stations (MSs) via a multi-antenna relay station (RS). We propose a novel precoder design scheme by using the joint integer-forcing method to achieve high rate with the algebraic structure of lattice codes. Inspired by the concept of decoding the linear equations in compute-and-forward relay systems, the linear precoding matrix is generalized as an integer matrix that is approximate to the channel matrix, thereby mitigating the multiuser interference (MUI) at the MSs. The power allocation optimization problem at the BS is taken into account to improve the performance of the scheme. The simulation results show the outstanding performance of the proposed scheme.
Programmed to succeed?: A multi-national, multi-institutional study of introductory programming courses

This paper describes a multi-national, multi-institutional study that investigated introductory programming courses. Student participants were drawn from eleven institutions, mainly in Australasia, during the academic year of 2004. A number of diagnostic tasks were used to explore cognitive, behavioural, and attitudinal factors such as spatial visualisation and reasoning, the ability to articulate strategies for common-place search and design tasks, and attitudes to studying. The results indicate that: a deep approach to learning was positively correlated with mark for the course, while a surface approach was negatively correlated; spatial visualisation skills are correlated with success; a progression of map drawing styles identified in the literature has a significant effect with marks; and increasing measures of richness of articulation of a search strategy are also associated with higher marks. Finally, a qualitative analysis of short interviews identified the qualities that students themselves regarded as important to learn programming well.
Aplicaciones del diálogo humano de tutoría al autotutor: un sistema inteligente de tutoría

At the University of Memphis we have created an intelligent tutoring system, called AutoTutor, that helps students learn by holding a conversation in natural language. Decades of research on human tutoring have guided our creation of AutoTutor, which implements effective tutoring strategies. Several studies have shown that AutoTutor promotes significant learning gains. The current research examines which features of the dialog can account for the learning gains, and assesses AutoTutor? s appropriate use of dialog. Specifically, we explored the dialog patterns from natural tutoring interactions with AutoTutor and analyzed how short pedagogical feedback is related to learning. We found that AutoTutor creates an appropriate model of student knowledge and responds to the students in a manner consistent with their overall performance. These results together with previous findings support the conclusion that AutoTutor is an effective intelligent tutoring system that uses pedagogical strategies that are appropriate for individual learners.
Multi-objective Container Deployment on Heterogeneous Clusters

Operating system (OS) containers are becoming increasingly popular in cloud computing for improving productivity and code portability. However, existing deployment scheduling solutions mainly treat each container deployment as an independent request, and focus on the single aspect of resource utilization or load balancing, or work on homogeneous clusters. In this paper, we propose a new container deployment algorithm to satisfy multiple objectives on heterogeneous clusters. We analyze the deployment requirements of container-based infrastructure and formulate the deployment problem as a vector bin packing problem with heterogeneous bins. We focus on three objectives: multi-resource guarantee, load balancing, and dependency awareness. The goal of the proposed algorithm is to improve the tradeoff between load balancing and dependency awareness with multi-resource guarantees. Based on the algorithm, we implement a prototype scheduler to deploy containers on heterogeneous clusters. We evaluate our scheduler over a wide range of workload scenarios by simulation, which shows that our scheduler significantly outperforms existing schedulers of the container orchestration platforms.
Dual-Band MIMO Antenna With Compact Self-Decoupled Antenna Pairs for 5G Mobile Applications

In this paper, a dual-band four-element multi-input and multi-output (MIMO) antenna system based on compact self-decoupled antenna pairs is proposed for the fifth-generation (5G) operation in mobile terminals. By sharing one common grounding branch for the two adjacent antenna units, dual-band antenna pairs with high isolation can be obtained. In particular, the two compact antenna pairs are placed perpendicularly on both sides of the system ground plane. The MIMO antenna system is optimized to operate in both 3.5 GHz (3.4–3.6 GHz) and 4.9 GHz (4.8–5.0 GHz) bands with isolation better than −17.5 dB for the low band and −20 dB for the high band. The proposed dual-band four-antenna MIMO system is fabricated and measured, and a good agreement between the simulation and measurement is obtained. Moreover, the influences of the phantom hand and display panel on the performance of the MIMO antenna system are also studied and discussed.
A Snapback Suppressed Base Resistance Controlled Thyristor with Double N-type Buried Layer

A new base resistance controlled thyristor with double N-type buried layer (DNBL-BRT) is proposed in this paper. In the new structure, the left N-buried layer introduces an electron potential trap to extract electron current into thyristor, then effective thyristor trigger current is enhanced. Meanwhile, the right N-buried layer acts as a hole potential barrier to push hole current into P-base region, then parasitic PNP is suppressed and hole current density in P-base region is improved. Snapback phenomenon is significantly suppressed. Numerical simulation results show that, snapback-free can be realized when the doping level of N-buried layers is $1.0 \times 10^{15}$ $\mathrm{c m}^{-3}$and the distance between the two N-buried layers is 1.5 $\mu$m, meanwhile high blocking capability is maintained.
Podcasts as a supplement in tertiary education: an experiment with two Computer Science courses

The current generation of undergraduate students are enthusiastic adopters of mobile technologies, and some of these technologies offers significant opportunities for broadening the times, places and manner that students study and learn. One of these is podcasting, which greatly eases distribution of content to portable devices, giving the students opportunity for anytime, anywhere learning. However, most educational podcasts do not take advantage of the potential of podcasting. In this paper we discuss the issues surrounding the use of podcasting for higher education. We report our experiences using podcasts to supplement two undergraduate Computer Science courses, and we provide tips for others considering the use of podcasts in education.
Fast and Provable ADMM for Learning with Generative Priors

In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on the special case where such constraint arises from the specification that a variable should lie in the range of a neural network. This is motivated by recent successful applications of Generative Adversarial Networks (GANs) in tasks like compressive sensing, denoising and robustness against adversarial examples. The derived rates for our algorithm are characterized in terms of certain geometric properties of the generator network, which we show hold for feedforward architectures, under mild assumptions. Unlike gradient descent (GD), it can efficiently handle non-smooth objectives as well as exploit efficient partial minimization procedures, thus being faster in many practical scenarios.
The Hamburg meteorite fall: Fireball trajectory, orbit, and dynamics

The Hamburg (H4) meteorite fell on 17 January 2018 at 01:08 UT approximately 10 km north of Ann Arbor, Michigan. More than two dozen fragments totaling under 1 kg were recovered, primarily from frozen lake surfaces. The fireball initial velocity was 15.83 ± 0.05 km s−1, based on four independent records showing the fireball above 50 km altitude. The radiant had a zenith angle of 66.14 ± 0.29° and an azimuth of 121.56 ± 1.2°. The resulting low inclination (<1°) Apollo‐type orbit has a large aphelion distance and Tisserand value relative to Jupiter (Tj) of ~3. Two major flares dominate the energy deposition profile, centered at 24.1 and 21.7 km altitude, respectively, under dynamic pressures of 5–7 MPa. The Geostationary Lightning Mapper on the Geostationary Operational Environmental Satellite‐16 also detected the two main flares and their relative timing and peak flux agree with the video‐derived brightness profile. Our preferred total energy for the Hamburg fireball is 2–7 T TNT (8.4–28 × 109 J), which corresponds to a likely initial mass in the range of 60–225 kg or diameter between 0.3 and 0.5 m. Based on the model of Granvik et al. (2018), the meteorite originated in an escape route from the mid to outer asteroid belt. Hamburg is the 14th known H chondrite with an instrumentally derived preatmospheric orbit, half of which have small (<5°) inclinations making connection with (6) Hebe problematic. A definitive parent body consistent with all 14 known H chondrite orbits remains elusive.
Evolutionary Algorithm for Sinhala to English Translation

Machine Translation (MT) is an area in natural language processing, which focuses on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules, and therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it into English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.
Star formation in low-redshift cluster dwarf galaxies

Evolution of galaxies in dense environments can be affected by close encounters with neighbouring galaxies and interactions with the intracluster medium. Dwarf galaxies (dGs) are important as their low mass makes them more susceptible to these effects than giant systems. Combined luminosity functions (LFs) in the r and u band of 15 galaxy clusters were constructed using archival data from the Canada–France–Hawaii Telescope. LFs were measured as a function of clustercentric radius from stacked cluster data. Marginal evidence was found for an increase in the faint-end slope of the u-band LF relative to the r-band with increasing clustercentric radius. The dwarf-to-giant ratio (DGR) was found to increase toward the cluster outskirts, with the u-band DGR increasing faster with clustercentric radius compared to the r-band. The dG blue fraction was found to be ∼2 times larger than the giant galaxy blue fraction over all clustercentric distance (∼5σ level). The central concentration (C) was used as a proxy to distinguish nucleated versus non-nucleated dGs. The ratio of high-C to low-C dGs was found to be ∼2 times greater in the inner cluster region compared to the outskirts (2.8σ level). The faint-end slope of the r-band LF for the cluster outskirts (0.6 ≤ r/r200 < 1.0) is steeper than the Sloan Digital Sky Survey field LF, while the u-band LF is marginally steeper at the 2. 5σ level. Decrease in the faint-end slope of the r- and u-band cluster LFs towards the cluster centre is consistent with quenching of star formation via ram pressure stripping and galaxy–galaxy interactions.
Most Black Holes Are Born Very Slowly Rotating

The age of gravitational-wave astronomy has begun, and black hole (BH) mergers detected by the Laser Interferometer Gravitational-Wave Observatory (LIGO) are providing novel constraints on massive star evolution. A major uncertainty in stellar theory is the angular momentum (AM) transport within the star that determines its core rotation rate and the resulting BH’s spin. Internal rotation rates of low-mass stars measured from asteroseismology prove that AM transport is efficient, suggesting that massive stellar cores may rotate slower than prior expectations. We investigate AM transport via the magnetic Tayler instability, which can largely explain the rotation rates of low-mass stars and white dwarfs. Implementing an updated AM transport prescription into models of high-mass stars, we compute the spins of their BH remnants. We predict that BHs born from single stars rotate very slowly, with a ∼ 10−2, regardless of initial rotation rate, possibly explaining the low χeff of most BH binaries detected by LIGO thus far. A limited set of binary models suggests slow rotation for many binary scenarios as well, although homogeneous evolution and tidal spin-up of post-common-envelope helium stars can create moderate or high BH spins. We make predictions for the values of χeff in future LIGO events, and we discuss implications for engine-powered transients.
Hot neutron stars with microscopic equations of state

We study the properties of hot $\ensuremath{\beta}$-stable nuclear matter using equations of state derived within the Brueckner-Hartree-Fock approach at finite temperature including consistent three-body forces. Simple and accurate parametrizations of the finite-temperature equations of state are provided. The adiabatic index is analyzed in some detail. The properties of hot neutron stars are then investigated within this framework, in particular, the temperature dependence of the maximum mass. We find very small temperature effects and analyze the interplay of the different contributions.
Observational constraints on warm inflation in loop quantum cosmology

By incorporating quantum aspects of gravity, Loop Quantum Cosmology (LQC) provides a self-consistent extension of the inflationary scenario, allowing for modifications in the primordial inflationary power spectrum with respect to the standard General Relativity one. We investigate such modifications and explore the constraints imposed by the Cosmic Microwave Background (CMB) Planck Collaboration data on the Warm Inflation (WI) scenario in the LQC context. We obtain useful relations between the dissipative parameter of WI and the bounce scale parameter of LQC. We also find that the number of required e-folds of expansion from the bounce instant till the moment the observable scales crossed the Hubble radius during inflation can be smaller in WI than in CI. In particular, we find that this depends on how large is the dissipation in WI, with the amount of required e-folds decreasing with the increasing of the dissipation value. {}Furthermore, by performing a Monte Carlo Markov Chain analysis for the considered WI models, we find good agreement of the model with the data. This shows that the WI models studied here can explain the current observations also in the context of LQC.
Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network’s generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of $0.1dB$ , whether trained on or not.
Gapless Surface Dirac Cone in Antiferromagnetic Topological Insulator 
MnBi2Te4

The recent discovered antiferromagnetic topological insulators in Mn-Bi-Te family with intrinsic magnetic ordering have rapidly drawn broad interest since its cleaved surface state is believed to be gapped, hosting the unprecedented axion states with half-integer quantum Hall effect. Here, however, we show unambiguously by using high-resolution angle-resolved photoemission spectroscopy that a gapless Dirac cone at the (0001) surface of MnBi$_2$Te$_4$ exists between the bulk band gap. Such unexpected surface state remains unchanged across the bulk Neel temperature, and is even robust against severe surface degradation, indicating additional topological protection. Through symmetry analysis and $\textit{ab}$-$\textit{initio}$ calculations we consider different types of surface reconstruction of the magnetic moments as possible origins giving rise to such linear dispersion. Our results reveal that the intrinsic magnetic topological insulator hosts a rich platform to realize various topological phases such as topological crystalline insulator and time-reversal-preserved topological insulator, by tuning the magnetic configurations.
Momentum and energy injection by a supernova remnant into an inhomogeneous medium

We investigate the effect of mass-loading from embedded clouds on the evolution of supernova remnants and on the energy and momentum that they inject into an inhomogeneous interstellar medium. We use 1D hydrodynamical calculations and assume that the clouds are numerous enough that they can be treated in the continuous limit. The destruction of embedded clouds adds mass into the remnant, increasing its density and pressure, and decreasing its temperature. The remnant cools more quickly, is less able to do PdV work on the swept-up gas, and ultimately attains a lower final momentum (by up to a factor of two or more). We thus find that the injection of momentum is more sensitive to an inhomogeneous environment than previous work has suggested, and we provide fits to our results for the situation where the cloud mass is not limited. The behaviour of the remnant is more complex in situations where the cloud mass is finite and locally runs out. In the case of multiple supernovae in a clustered environment, later supernova explosions may encounter higher densities than previous explosions due to the prior liberation of mass from engulfed clouds. If the cloud mass is finite, later explosions may be able to create a sustained hot phase when earlier explosions have not been able to.