Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps

Binarizing historically degraded as-built drawing (HDAD) maps is a new challenging job, especially in terms of removing the three artifacts, namely noise, the yellowing areas, and the folded lines, while preserving the foreground components well. In this paper, we first propose a semi-automatic labeling method to create the HDAD-pair dataset of which each HDAD-pair consists of one HDAD map and its binarized HDAD map. Based on the created training HDAD-pair dataset, we propose a convolutional neural network-based (CNN-based) binarization method to produce high-quality binarized HDAD maps. Based on the testing HDAD maps, the thorough experimental data demonstrated that in terms of the accuracy, PSNR (peak-signal-to-noise-ratio), and the perceptual effect of the binarized HDAD maps, our method substantially outperforms the nine existing binarization methods. In addition, with similar accuracy, the experimental results demonstrated the significant execution-time reduction merit of our method relative to the retrained version of the state-of-the-art CNN-based binarization methods.
Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space

Both images and music can convey rich semantics and are widely used to induce specific emotions. Matching images and music with similar emotions might help to make emotion perceptions more vivid and stronger. Existing emotion-based image and music matching methods either employ limited categorical emotion states which cannot well reflect the complexity and subtlety of emotions, or train the matching model using an impractical multi-stage pipeline. In this paper, we study end-to-end matching between image and music based on emotions in the continuous valence-arousal (VA) space. First, we construct a large-scale dataset, termed Image-Music-Emotion-Matching-Net (IMEMNet), with over 140K image-music pairs. Second, we propose cross-modal deep continuous metric learning (CDCML) to learn a shared latent embedding space which preserves the cross-modal similarity relationship in the continuous matching space. Finally, we refine the embedding space by further preserving the single-modal emotion relationship in the VA spaces of both images and music. The metric learning in the embedding space and task regression in the label space are jointly optimized for both cross-modal matching and single-modal VA prediction. The extensive experiments conducted on IMEMNet demonstrate the superiority of CDCML for emotion-based image and music matching as compared to the state-of-the-art approaches.
Breeding for Trace Minerals in Wheat

In the search for genetic material with high iron and zinc concentration in wheat grain, a significant positive correlation has been found between iron and zinc concentrations, suggesting that these two traits may be combined relatively easily during breeding. In future research, the very high values of iron and zinc in the grain seen in wild types and landraces need to be confirmed in trials in which all the best material is planted in the same location and year. In addition, it is important to determine if these high levels of iron and zinc in the grain can be maintained in high-yielding material. The production of semi-dwarf wheat through the introduction of the rht genes has resulted in substantial yield increases. However, this is associated with a reduction in iron and zinc concentrations in some bread wheat genotypes, but not in durum wheat. The presence of the 1B/1R translocation in the wheat germplasm of the Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT) to increase leaf rust resistance may have had a positive effect on the concentration of iron and zinc, but a negative effect was ruled out. There is a strong positive correlation between grain yield and year of release in the CIMMYT wheat varieties. There is a small negative but statistically significant relationship between the time of release and the concentrations of iron, zinc, total phosphorus, and phytate. The positive effect of nitrogen applications on iron and zinc concentrations is more important than declines in these concentrations due to breeding during the last 42 years
Neutron stars with Bogoliubov quark-meson coupling model

A quark-meson coupling model based on the quark model proposed by Bogoliubov for the description of the quark dynamics is developed and applied to the description of neutron stars. Starting from a su(3) symmetry approach, it is shown that this symmetry has to be broken in order to satisfy the constraints set by the hypernuclei and by neutron stars. The model is able to describe observations such as two solar mass stars or the radius of canonical neutron stars within the uncertainties presently accepted. If the optical potentials for $\Lambda$ and $\Xi$ hyperons in symmetric nuclear matter at saturation obtained from laboratory measurements of hypernuclei properties are imposed the model predicts no strangeness inside neutron stars.
Influence of acceleration on multibody entangled quantum states

We study the influence of acceleration on the twin-Fock state which is a class of specific multibody entangled quantum state and was already realized experimentally with high precision and sensitivity. We show that the multi-body quantum entanglement can be increased with the acceleration, consistent with the "anti-Unruh effect" in reference to the counterintuitive cooling previously pointed out for an accelerated detector coupled to the vacuum. In particular, this kind of entanglement increase can lead to the improvement of the phase sensitivity, which provides a way to test the anti-Unruh effect in the future experiments.
RECOApy: Data recording, pre-processing and phonetic transcription for end-to-end speech-based applications

Deep learning enables the development of efficient end-to-end speech processing applications while bypassing the need for expert linguistic and signal processing features. Yet, recent studies show that good quality speech resources and phonetic transcription of the training data can enhance the results of these applications. In this paper, the RECOApy tool is introduced. RECOApy streamlines the steps of data recording and pre-processing required in end-to-end speech-based applications. The tool implements an easy-to-use interface for prompted speech recording, spectrogram and waveform analysis, utterance-level normalisation and silence trimming, as well grapheme-to-phoneme conversion of the prompts in eight languages: Czech, English, French, German, Italian, Polish, Romanian and Spanish. The grapheme-to-phoneme (G2P) converters are deep neural network (DNN) based architectures trained on lexicons extracted from the Wiktionary online collaborative resource. With the different degree of orthographic transparency, as well as the varying amount of phonetic entries across the languages, the DNN's hyperparameters are optimised with an evolution strategy. The phoneme and word error rates of the resulting G2P converters are presented and discussed. The tool, the processed phonetic lexicons and trained G2P models are made freely available.
Modeling networks of probabilistic memristors in SPICE

Efficient simulation of probabilistic memristors and their networks requires novel modeling approaches. One major departure from the conventional memristor modeling is based on a master equation for the occupation probabilities of network states [arXiv:2003.11011 (2020)]. In the present article, we show how to implement such master equations in SPICE - a general-purpose circuit simulation program. In the case studies, we simulate the dynamics of ac-driven probabilistic binary and multi-state memristors, and dc-driven networks of probabilistic binary and multi-state memristors. Our SPICE results are in perfect agreement with known analytical solutions. Examples of LTspice codes are included.
General Measurements with Limited Resources and Their Application to Quantum Unambiguous State Discrimination

In this report we present a framework for implementing arbitrary $n$-outcome quantum measurement as a sequence of two-outcome measurements requiring only single ancillary qubit. Our procedure is the same as one presented in [Phys. Rev. A 77, 052104 (2008)] but in addition offers particular construction for a two-outcome partial measurements. We exemplify this framework on the unambiguous state discrimination. In the simplest case it gives the same construction as is known, if we opt for performing conclusiveness measurement first. However, it also offers possibility of performing measurement for one of the state outcomes, which shows flexibility of presented framework.
Clinical significance and functional role of transmembrane protein 47 (TMEM47) in chemoresistance of hepatocellular carcinoma

Chemoresistance is the main cause of chemotherapy failure in patients with hepatocellular carcinoma (HCC). The gene encoding transmembrane protein 47 (TMEM47) was previously identified to be significantly upregulated in HCC cell lines with acquired chemoresistance. The aim of the present study was to characterize the clinical significance and function of TMEM47 in HCC chemoresistance. The results demonstrated that the TMEM47 expression levels in the tumors of patients not responding to cisplatin-based transarterial chemoembolization (TACE) treatment was significantly higher compared with those in patients who responded to TACE treatment. Moreover, analyses from clinical samples and HCC cell lines indicated that TMEM47 expression may be upregulated in HCC in response to cisplatin treatment. Furthermore, the TMEM47 mRNA expression levels were positively correlated with the degree of cisplatin resistance of HCC cells. Overexpression of TMEM47 in HCC cells significantly promoted cisplatin resistance. The present study also demonstrated that targeted inhibition of TMEM47 could significantly reduce cisplatin resistance of cisplatin-resistant HCC cells via enhancing caspase-mediated apoptosis. In addition, targeted inhibition of TMEM47 enhanced the sensitivity of cisplatin-resistant cells to cisplatin via suppressing cisplatin-induced activation of the genes involved in drug efflux and metabolism. The present study also validated that TMEM47 expression was significantly correlated with multi-drug resistance-associated protein 1 in patients with HCC who received TACE treatment. In conclusion, the findings of the present study demonstrated that TMEM47 may be a useful biomarker for predicting the response to chemotherapy and a potential therapeutic target for overcoming HCC chemoresistance.
SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks

Binary Convolutional Neural Networks (BCNNs) can significantly improve the efficiency of Deep Convolutional Neural Networks (DCNNs) for their deployment on resource-constrained platforms, such as mobile and embedded systems. However, the accuracy degradation of BCNNs is still considerable compared with their full precision counterpart, impeding their practical deployment. Because of the inevitable binarization error in the forward propagation and gradient mismatch problem in the backward propagation, it is nontrivial to train BCNNs to achieve satisfactory accuracy. To ease the difficulty of training, the shortcut-based BCNNs, such as residual connection-based Bi-real ResNet and dense connection-based BinaryDenseNet, introduce additional shortcuts in addition to the shortcuts already present in their full precision counterparts. Furthermore, fractal architectures have been also been used to improve the training process of full-precision DCNNs since the fractal structure triggers effects akin to deep supervision and lateral student-teacher information flow. Inspired by the shortcuts and fractal architectures, we propose two Shortcut-based Fractal Architectures (SoFAr) specifically designed for BCNNs: 1. residual connection-based fractal architectures for binary ResNet, and 2. dense connection-based fractal architectures for binary DenseNet. Our proposed SoFAr combines the adoption of shortcuts and the fractal architectures in one unified model, which is helpful in the training of BCNNs. Results show that our proposed SoFAr achieves better accuracy compared with shortcut-based BCNNs. Specifically, the Top-1 accuracy of our proposed RF-c4d8 ResNet37(41) and DRF-c2d2 DenseNet51(53) on ImageNet outperforms Bi-real ResNet18(64) and BinaryDenseNet51(32) by 3.29% and 1.41%, respectively, with the same computational complexity overhead.
CMZoom. II. Catalog of Compact Submillimeter Dust Continuum Sources in the Milky Way’s Central Molecular Zone

In this paper we present the CMZoom survey’s catalog of compact sources (<10″, ∼0.4 pc) within the central molecular zone (CMZ). CMZoom is a Submillimeter Array large program designed to provide a complete and unbiased map of all high column density gas (N(H2) ≥ 1023 cm−2) of the innermost 500 pc of the Galaxy in the 1.3 mm dust continuum. We generate both a robust catalog designed to reduce spurious source detections, and a second catalog with higher completeness, both generated using a pruned dendrogram. In the robust catalog, we report 285 compact sources, or 816 in the high-completeness catalog. These sources have effective radii between 0.04 and 0.4 pc, and are the potential progenitors of star clusters. The masses for both catalogs are dominated by the Sagittarius B2 cloud complex, where masses are likely unreliable due to free–free contamination, uncertain dust temperatures, and line-of-sight confusion. Given the survey selection and completeness, we predict that our robust catalog accounts for more than ∼99% of compact substructure capable of forming high-mass stars in the CMZ. This catalog provides a crucial foundation for future studies of high-mass star formation in the Galactic Center.
Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization

Thelimousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9,000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem for the hotel is introduced, having driven up to S$3.2 million of savings per year while improving the service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of the hotel’s fleet of limousines and drivers and to support decision making for planners and controllers to cultivate sustained business value.
OCR Graph Features for Manipulation Detection in Documents

Detecting manipulations in digital documents is becoming increasingly important for information verification purposes. Due to the proliferation of image editing software, altering key information in documents has become widely accessible. Nearly all approaches in this domain rely on a procedural approach, using carefully generated features and a hand-tuned scoring system, rather than a data-driven and generalizable approach. We frame this issue as a graph comparison problem using the character bounding boxes, and propose a model that leverages graph features using OCR (Optical Character Recognition). Our model relies on a data-driven approach to detect alterations by training a random forest classifier on the graph-based OCR features. We evaluate our algorithm's forgery detection performance on dataset constructed from real business documents with slight forgery imperfections. Our proposed model dramatically outperforms the most closely-related document manipulation detection model on this task.
ASYMPTOTIC DEGREE OF RANDOM MONOMIAL IDEALS

One of the fundamental invariants connecting algebra and geometry is the degree of an ideal. In this paper we derive the probabilistic behavior of degree with respect to the versatile Erdős-Renyi-type model for random monomial ideals defined in \cite{rmi}. We study the staircase structure associated to a monomial ideal, and show that in the random case the shape of the staircase diagram is approximately hyperbolic, and this behavior is robust across several random models. Since the discrete volume under this staircase is related to the summatory higher-order divisor function studied in number theory, we use this connection and our control over the shape of the staircase diagram to derive the asymptotic degree of a random monomial ideal. Another way to compute the degree of a monomial ideal is with a standard pair decomposition. This paper derives bounds on the number of standard pairs of a random monomial ideal indexed by any subset of the ring variables. The standard pairs indexed by maximal subsets give a count of degree, as well as being a more nuanced invariant of the random monomial ideal.
CHILES VERDES: Radio Variability at an Unprecedented Depth and Cadence in the COSMOS Field

Although it is well established that some extragalactic radio sources are time-variable, the properties of this radio variability, and its connection with host galaxy properties, remain to be explored—particularly for faint sources. Here we present an analysis of radio variable sources from the CHILES Variable and Explosive Radio Dynamic Evolution Survey (CHILES VERDES)—a partner project of the 1.4 GHz COSMOS H i Large Extragalactic Survey. CHILES VERDES provides an unprecedented combination of survey depth, duration, and cadence, with 960 hr of 1–2 GHz continuum VLA data obtained over 209 epochs between 2013 and 2019 in a 0.44 deg2 section of the well-studied extragalactic deep field, COSMOS. We identified 18 moderate-variability sources (showing 10%–30% flux density variation) and 40 lower-variability sources (2%–10% flux density variation). They are mainly active galactic nuclei (AGNs) with radio luminosities in the range of 1022–1027 W Hz−1, based on cross-matching with COSMOS multiwavelength catalogs. The moderate-variability sources span redshifts z = 0.22–1.56, have mostly flat radio spectra (α > −0.5), and vary on timescales ranging from days to years. The lower-variability sources have similar properties, but generally have higher radio luminosities than the moderate-variability sources, extending to z = 2.8, and have steeper radio spectra (α < −0.5). No star-forming galaxy showed statistically significant variability in our analysis. The observed variability likely originates from scintillation on short (∼week) timescales, and Doppler-boosted intrinsic AGN variability on long (month–year) timescales.
A family of mixture models for biclustering

Biclustering is used for simultaneous clustering of the observations and variables when there is no group structure known a priori. It is being increasingly used in bioinformatics, text analytics, and so on. Previously, biclustering has been introduced in a model‐based clustering framework by utilizing a structure similar to a mixture of factor analyzers. In such models, observed variables X are modeled using a latent variable U that is assumed to be from N(0,I) . Clustering of variables are introduced by imposing constraints on the entries of the factor loading matrix to be 0 and 1 that results in block diagonal covariance matrices. However, this approach is overly restrictive as off‐diagonal elements in the blocks of the covariance matrices can only be 1 which can lead to unsatisfactory model fit on complex data. Here, the latent variable U is assumed to be from a N(0,T) where T is a diagonal matrix. This ensures that the off‐diagonal terms in the block matrices within the covariance matrices are non‐zero and not restricted to be 1. This leads to a superior model fit on complex data. A family of models is developed by imposing constraints on the components of the covariance matrix. For parameter estimation, an alternating expectation conditional maximization (AECM) algorithm is used. Finally, the proposed method is illustrated using simulated and real datasets.
Tuning of ‘Constant in Gain Lead in Phase (CgLp)’ Reset Controller using Higher-orDer Sinusoidal Input Describing Function (HOSIDF)

Due to development of technology, linear controllers cannot satisfy requirements of high-tech industry. One solution is using nonlinear controllers such as reset elements to overcome this big barrier. In literature, the Constant in gain Lead in phase (CgLp) compensator is a novel reset element developed to overcome the inherent linear controller limitations. However, a tuning guideline for these controllers has not been proposed so far. In this paper, a recently developed method named higher-order sinusoidal input describing function (HOSIDF), which gives deeper insight into the frequency behaviour of non-linear controllers compared to sinusoidal input describing function (DF), is used to obtain a straight-forward tuning method for CgLp compensators. In this respect, comparative analyses on tracking performance of these compensators are carried out. Based on these analyses, tuning guidelines for CgLp compensators are developed and validated on a high-tech precision positioning stage. The results show the effectiveness of the developed tuning method.
Can microtask programming work in industry?

A critical issue in software development projects in IT service companies is finding the right people at the right time. By enabling assignments of tasks to people to be more fluid, the use of crowdsourcing approaches within a company offers a potential solution to this challenge. Inside a company, as multiple system development projects are ongoing separately, developers with slack time on one project might use this time to contribute to other projects. In this paper, we report on a case study of the application of crowdsourcing within an industrial web application system development project in a large telecommunications company. Developers worked with system specifications which were organized into a set of microtasks, offering a set of short and self-contained descriptions. When crowd workers in other projects had slack time, they fetched and completed microtasks. Our results offer initial evidence for the potential value of microtask programming in increasing the fluidity of team assignments within a company. Crowd contributors to the project were able to onboard and contribute to a new project in less than 2 hours. After onboarding, the crowd workers were together able to successfully implement a small program which contained only a small number of defects. Interview and survey data gathered from project participants revealed that crowd workers reported that they perceived onboarding costs to be reduced and did not experience issues with the reduced face to face communication, but experienced challenges with motivation.