Miracle-dz/UniFAR-FTUs huggingface dataset:
New Classes of Tolerant Mutants

Several hundred independent bacteriocin-tolerant mutants have been isolated without mutagenesis from three strains of Escherichia coli. On the basis of pattems of sensitivity to eight different colicins, over 85% of these mutants could be grouped into four classes. Two classes of mutants, class A and class B, are equivalent to tolA and tolB type mutants. We found tolA and tolB mutants were sensitive to the antibiotic bacitracin. The other two classes of bacteriocin-tolerant mutants, class F and class G, are distinguished from other types of colicin-tolerant mutants on the basis of sensitivity to colicins, dyes, detergents, antibiotics, and chelating agents. The mutation in class F and class G mutants is located between 21 to 23 min on the E. coli chromosome. We propose to designate the loci of these mutations as tolF and to1G, respectively.
University students' conceptions of bonding in melting and dissolving phenomena

Undergraduate and graduate students' predictions and submicroscopic level explanations for the melting of four materials (salt, chalk, sugar, and butter), and for the mixing of these solutes in two solvents (water and cooking oil) were collected. Twenty-three undergraduate students and seven graduate students participated in the study, and data were collected through individual semi-structured interviews. The purpose of the study was to investigate students' conceptions of how bonding is involved in both melting and dissolving processes, as well as to investigate students' ideas of specific bonds and forces involved in melting and dissolving. Three types of misconceptions about bond breaking in the process of melting were revealed, as well as three types of misconceptions about bond breaking and three types of misconceptions about bond forming in the process of dissolving. Analysis indicated that students viewed the dissolving process as occurring via a single-step mechanism or via a multi-step mechanism. Analysis also revealed that students viewed bond breaking similarly in melting and dissolving processes, likely resulting in confusion between these two processes.
UNDIFFERENTIATED NASOPHARYNGEAL CARCINOMA IN CHILDREN AND ADOLESCENTS: A REPORT OF 68 CASES IN A MOROCCAN CENTER

This study aimed to demonstrate the clinical and therapeutic features of nasopharyngeal carcinoma (NPC) in children and adolescents in Morocco, an intermediate risk area. Sixty-eight newly diagnosed undifferentiated NPC patients younger than 19 years old treated at the National Institute of Oncology Sidi Mohamed Ben Abdellah in Rabat, Morocco, between 2010 and 2016. Patients had a median age of 16 years (range 8-19), with a sex ratio of 2.2. The mean time to presentation was 5.9 months. The main presenting symptom was cervical lymphadenopathy in 77% of cases. Patients with stages I, II, III, and IVa accounted respectively for 1.5%, 10.3%, 44.1%, and 39.7%. Three patients had bone metastases at the time of diagnosis. Forty-four patients received induction chemotherapy before concurrent chemo radiation, 23 patients received concurrent chemo radiotherapy alone with weekly cisplatin, and one patient underwent exclusive radiotherapy. After a mean follow-up of 38 months (range: 14.5 to 65), loco-regional control (LRC) and distant metastasis-free survival (DMFS) were 94.1% and 76.5%, respectively. The 5and 10year overall survival (OS) was 87% and 80.1%, respectively. The 5and 10-year disease-free survival (DFS) was 75.3% and 66%, respectively. At the end of follow-up period, 13 patients (19.1%) were lost to follow-up. Seven patients (10.3%) had died, and 48 patients (70.6%) were alive and without disease. Most common late complications were xerostomia, hearing loss, skin fibrosis, trismus and dental caries. In spite of the majority of children and adolescents with NPC diagnosed at the advanced stage, treatment combining chemotherapy and radiotherapy achieves an excellent local control rate. A higher interval time between symptom onset and consultation (> 6 months), and T stage (T3, T4) were the most relevant prognostic factors in this study.
Neural 3D Clothes Retargeting from a Single Image

In this paper, we present a method of clothes retargeting; generating the potential poses and deformations of a given 3D clothing template model to fit onto a person in a single RGB image. The problem is fundamentally ill-posed as attaining the ground truth data is impossible, i.e., images of people wearing the different 3D clothing template model at exact same pose. We address this challenge by utilizing large-scale synthetic data generated from physical simulation, allowing us to map 2D dense body pose to 3D clothing deformation. With the simulated data, we propose a semi-supervised learning framework that validates the physical plausibility of the 3D deformation by matching with the prescribed body-to-cloth contact points and clothing silhouette to fit onto the unlabeled real images. A new neural clothes retargeting network (CRNet) is designed to integrate the semi-supervised retargeting task in an end-to-end fashion. In our evaluation, we show that our method can predict the realistic 3D pose and deformation field needed for retargeting clothes models in real-world examples.
A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?

—When trained as generative models, Deep Learn- ing algorithms have shown exceptional performance on tasks involving high dimensional data such as image denoising and super-resolution. In an increasingly connected world dominated by mobile and edge devices, there is surging demand for these al- gorithms to run locally on embedded platforms. FPGAs, by virtue of their reprogrammability and low-power characteristics, are ideal candidates for these edge computing applications. As such, we design a spatio-temporally parallelized hardware architecture capable of accelerating a deconvolution algorithm optimized for power-efﬁcient inference on a resource-limited FPGA. We pro- pose this FPGA-based accelerator to be used for Deconvolutional Neural Network (DCNN) inference in low-power edge computing applications. To this end, we develop methods that systematically exploit micro-architectural innovations, design space exploration, and statistical analysis. Using a Xilinx PYNQ-Z2 FPGA, we leverage our architecture to accelerate inference for two DCNNs trained on the MNIST and CelebA datasets using the Wasserstein GAN framework. On these networks, our FPGA design achieves a higher throughput to power ratio with lower run-to-run variation when compared to the NVIDIA Jetson TX1 edge computing GPU.
Riemannian Perspective on Matrix Factorization

We study the non-convex matrix factorization approach to matrix completion via Riemannian geometry. Based on an optimization formulation over a Grassmannian manifold, we characterize the landscape based on the notion of principal angles between subspaces. For the fully observed case, our results show that there is a region in which the cost is geodesically convex, and outside of which all critical points are strictly saddle. We empirically study the partially observed case based on our findings.
Spin-spin interactions in solids from mixed all-electron and pseudopotential calculations $-$ a path to screening materials for spin qubits

Krishnendu Ghosh1,∗ He Ma, Mykyta Onizhuk, Vikram Gavini1,4,† Giulia Galli2,3,5‡ Department of Mechanical Engineering, University of Michigan, Ann Arbor MI 48109 Pritzker School of Molecular Engineering, University of Chicago, Chicago IL 60637 Department of Chemistry, University of Chicago, Chicago IL 60637 Department of Materials Science and Engineering, University of Michigan, Ann Arbor MI 48109 Materials Science Division, Argonne National Laboratory, Lemont IL 60439 (Dated: February 2, 2021)
Adversarially learning disentangled speech representations for robust multi-factor voice conversion

Factorizing speech as disentangled speech representations is vital to achieve highly controllable style transfer in voice conversion (VC). Conventional speech representation learning methods in VC only factorize speech as speaker and content, lacking controllability on other prosody-related factors. State-of-the-art speech representation learning methods for more speechfactors are using primary disentangle algorithms such as random resampling and ad-hoc bottleneck layer size adjustment,which however is hard to ensure robust speech representationdisentanglement. To increase the robustness of highly controllable style transfer on multiple factors in VC, we propose a disentangled speech representation learning framework based on adversarial learning. Four speech representations characterizing content, timbre, rhythm and pitch are extracted, and further disentangled by an adversarial Mask-And-Predict (MAP)network inspired by BERT. The adversarial network is used tominimize the correlations between the speech representations,by randomly masking and predicting one of the representationsfrom the others. Experimental results show that the proposedframework significantly improves the robustness of VC on multiple factors by increasing the speech quality MOS from 2.79 to3.30 and decreasing the MCD from 3.89 to 3.58.
Taxonomic survey of Hindi Language NLP systems

Natural Language processing (NLP) represents the task of automatic handling of natural human language by machines.There is large spectrum of possible applications of NLP which help in automating tasks like translating text from one language to other, retrieving and summarizing data from very huge repositories, spam email filtering, identifying fake news in digital media, find sentiment and feedback of people, find political opinions and views of people on various government policies, provide effective medical assistance based on past history records of patient etc. Hindi is the official language of India with nearly 691 million users in India and 366 million in rest of world. At present, a number of government and private sector projects and researchers in India and abroad, are working towards developing NLP applications and resources for Indian languages. This survey gives a report of the resources and applications available for Hindi language NLP.
Prospects of Gravitational Wave Detections from Common Envelope Evolution with LISA

Understanding common envelope (CE) evolution is an outstanding problem in binary evolution. Although the CE phase is not driven by gravitational wave (GW) emission, the inspiraling binary emits GWs that passively trace CE dynamics. Detecting this GW signal would provide direct insight into gas-driven physics. Even a non-detection might offer invaluable constraints. We investigate the prospects of detection of a Galactic CE by LISA. While the dynamical phase of the CE is likely sufficiently loud for detection, it is short and thus rare. We focus instead on the self-regulated phase that proceeds on a thermal timescale. Based on population-synthesis calculations and the (unknown) signal duration in the LISA band, we expect ∼0.1–100 sources in the Galaxy during the mission duration. We map the GW observable parameter space of frequency f GW and its derivative ḟGW , remaining agnostic on the specifics of the inspiral and find that signals with signal-to-noise ratios > 10 are possible if the CE stalls at separations such that f GW ≳ 2 × 10−3 Hz. We investigate the possibility of misidentifying the signal with other known sources. If the second derivative f̈GW can also be measured, the signal can be distinguished from other sources using a GW braking index. Alternatively, coupling LISA with electromagnetic observations of peculiar red giant stars and/or infrared and optical transients, might allow for the disentangling of a Galactic CE from other Galactic and extragalactic GW sources.
Semi-discrete and fully discrete HDG methods for Burgers' equation

<p style='text-indent:20px;'>This paper proposes semi-discrete and fully discrete hybridizable discontinuous Galerkin (HDG) methods for the Burgers' equation in two and three dimensions. In the spatial discretization, we use piecewise polynomials of degrees <inline-formula><tex-math id="M1">\begin{document}$ k \ (k \geq 1), k-1 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M2">\begin{document}$ l \ (l = k-1; k) $\end{document}</tex-math></inline-formula> to approximate the scalar function, flux variable and the interface trace of scalar function, respectively. In the full discretization method, we apply a backward Euler scheme for the temporal discretization. Optimal a priori error estimates are derived. Numerical experiments are presented to support the theoretical results. </p>
Neutral Metals in the Atmosphere of HD 149026b

Recent progress in high-dispersion spectroscopy has revealed the presence of vaporized heavy metals and ions in the atmosphere of hot Jupiters whose dayside temperature is larger than 2000 K, categorized as ultrahot Jupiters (UHJs). Using the archival data of high-resolution transmission spectroscopy obtained with the Subaru telescope, we searched for neutral metals in HD 149026b, a hot Jupiter cooler than UHJs. By removing stellar and telluric absorption and using a cross-correlation technique, we report a tentative detection of neutral titanium with 4. 4σ and a marginal signal of neutral iron with 2. 8σ in the atmosphere. This is the first detection of neutral titanium in an exoplanetary atmosphere. In this temperature range, titanium tends to form titanium oxide (TiO). The fact that we did not detect any signal from TiO suggests that the C/O ratio in the atmosphere is higher than the solar value. The detection of metals in the atmosphere of hot Jupiters cooler than UHJs will be useful for understanding the atmospheric structure and formation history of hot Jupiters.
Deciphering the 3D Orion Nebula-IV: The HH 269 Flow Emerges from the Orion-S Embedded Molecular Cloud

We have extended the membership and determined the 3D structure of the large (0.19 pc) HH 269 sequence of shocks in the Orion Nebula. All of the components lie along a track that is highly tilted to the plane of the sky and emerge from within the Orion-S embedded molecular cloud. Their source is probably either the highly obscured mm 9 source associated with a high N2H+ density core (more likely) or the more distant star COUP 632 (less likely). The former must be located in the photon dominated region (PDR) underlying the ionized surface of the Orion-S Cloud, while the latter would be embedded within the Orion-S Cloud. The flows seem to be episodic, with intervals of 1900–2600 yr or 700–2600 yr if COUP 632 is the source.
Near-field Image Transmission and EVM Measurements in Rich Scattering Environment

In this work we present near-field image transmission and error vector magnitude measurement in rich scattering environment in metal enclosure. We check the effect of loading metal enclosure on the performance of SDR based near-field communication link. We focus on the key communication receiver parameters to observe the effect of near-field link in presence of rich-scattering and in presence of loading with RF absorber cones. The near-field performance is measured by transmitting wideband OFDM-modulated packets containing image information. Our finding suggest that the performance of OFDM based wideband near-field communication improves when metal enclosure is loaded with RF absorbers. Near-field EVM improves when the enclosure is loaded with RF absorber cones. Loading of the metal enclosure has effect of increased coherence bandwidth. Frequency selectivity was observed in an empty enclosure which suggests coherence bandwidth less than the signal bandwidth.
$C^\infty$ partial regularity of the singular set in the obstacle problem

We show that the singular set Σ in the classical obstacle problem can be locally covered by a C∞ hypersurface, up to an “exceptional” set E, which has Hausdorff dimension at most n− 2 (countable, in the n = 2 case). Outside this exceptional set, the solution admits a polynomial expansion of arbitrarily large order. We also prove that Σ \ E is extremely unstable with respect to monotone perturbations of the boundary datum. We apply this result to the planar Hele-Shaw flow, showing that the free boundary can have singular points for at most countable many times.
PAVEL: Decorative Patterns with Packed Volumetric Elements

Many real-world hand-crafted objects are decorated with elements that are packed onto the object’s surface and deformed to cover it as much as possible. Examples are artisanal ceramics and metal jewelry. Inspired by these objects, we present a method to enrich surfaces with packed volumetric decorations. Our algorithm works by first determining the locations in which to add the decorative elements and then removing the non-physical overlap between them while preserving the decoration volume. For the placement, we support several strategies depending on the desired overall motif. To remove the overlap, we use an approach based on implicit deformable models creating the qualitative effect of plastic warping while avoiding expensive and hard-to-control physical simulations. Our decorative elements can be used to enhance virtual surfaces, as well as 3D-printed pieces, by assembling the decorations onto real surfaces to obtain tangible reproductions.
Understanding Cache Boundness of ML Operators on ARM Processors

Machine Learning compilers like TVM allow a fast and flexible deployment on embedded CPUs. This enables the use of non-standard operators, which are common in ML compression techniques. However, it is necessary to understand the limitations of typical compute-intense operators in ML workloads to design a proper solution. This is the first in-detail analysis of dense and convolution operators, generated with TVM, that compares to the fundamental hardware limits of embedded ARM processors. Thereby it explains the gap between computational peak performance, theoretical and measured, and real-world state-of-the-art results, created with TVM and openBLAS. Instead, one can see that single-precision general matrix multiply (GEMM) and convolutions are bound by L1-cache-read bandwidth. Explorations of 8-bit and bit-serial quantized operators show that quantization can be used to achieve relevant speedups compared to cache-bound floating-point operators. However, the performance of quantized operators highly depends on the interaction between data layout and bit packing.
Fabrication of High Aspect Ratio Poly(ethylene glycol)-Containing Microstructures by UV Embossing

The fabrication of high aspect ratio (5 and above) microstructures based upon UV embossing of mixtures containing poly(ethylene glycol) diacrylate (PEGDA) is described. UV embossing is a quick and convenient replication technique using low pressure and room temperature. The biocompatibility and cell- and protein-resistance of PEGDA make deep three-dimensional (3-D) micropatterned PEGDA films potentially useful for many biological applications such as protein delivery, tissue engineering, drug delivery, and biosensors. Microarrays of deep microchannels and microcups separated by PEGDA walls with aspect ratios of 7 and 5, respectively, were successfully embossed. UV embossing was found to faithfully replicate the lateral periodicity and height of the mold. We also successfully UV embossed a mixture having equal weight proportions of hydrophilic PEGDA and hydrophobic poly(propylene glycol) diacrylate and demonstrated the use of this microarray of microcups for encapsulation of a model protein (bovine serum a.
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach

It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache video contents on edge servers based on predicted video popularity. Traditional caching algorithms (e.g., LRU, LFU) are too simple to capture the dynamics of video popularity, especially long-tailed videos. Recent learning-driven caching algorithms (e.g., DeepCache) show promising performance, however, such black-box approaches are lack of explainability and interpretability. Moreover, the parameter tuning requires a large number of historical records, which are difficult to obtain for videos with low popularity. In this paper, we optimize video caching at the edge using a white-box approach, which is highly efficient and also completely explainable. To accurately capture the evolution of video popularity, we develop a mathematical model called HRS model, which is the combination of multiple point processes, including Hawkes’ self-exciting, reactive and self-correcting processes. The key advantage of the HRS model is its explainability, and much less number of model parameters. In addition, all its model parameters can be learned automatically through maximizing the Log-likelihood function constructed by past video request events. Next, we further design an online HRS-based video caching algorithm. To verify its effectiveness, we conduct a series of experiments using real video traces collected from Tencent Video, one of the largest online video providers in China. Experiment results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 15.5% improvement on average in terms of cache hit rate under realistic settings.
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat their illness. When making a decision, just the expected return -- known in reinforcement learning as the value -- cannot account for the potential range of adverse or positive outcomes a decision may have. Our key insight is that we should use the distribution over expected future returns differently to represent the critical information that the agent requires at decision time. In this paper, we propose Distributional Monte Carlo Tree Search, an algorithm that learns a posterior distribution over the utility of the different possible returns attainable from individual policy executions, resulting in good policies for both risk-aware and multi-objective settings. Moreover, our algorithm outperforms the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns.
MRSaiFE: Tissue Heating Prediction for MRI: a Feasibility Study

A to-date unsolved and highly limiting safety concern for Ultra High-Field (UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF) power in the body, quantified by the specific absorption rate (SAR), leading to dangerous tissue heating/damage in the form of local SAR hotspots that cannot currently be measured/monitored, thereby severely limiting the applicability of the technology for clinical practice and in regulatory approval. The goal of this study has been to show proof of concept of an artificial intelligence (AI) based exam-integrated real-time MRI safety prediction software (MRSaiFE) facilitating the safe generation of 3T and 7T images by means of accurate local SAR-monitoring at sub-W/kg levels. We trained the software with a small database of image as a feasibility study and achieved successful proof of concept for both field strengths. SAR patterns were predicted with a residual root mean squared error (RSME) of < 11{\%}$ along with a structural similarity (SSIM) level of > 84{\%}$ for both field strengths (3T and 7T).