Miracle-dz/UniFAR-FTUs huggingface dataset:
CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context

Learning accurate models of the physical world is required for a lot of robotic manipulation tasks. However, during manipulation, robots are expected to interact with un-known workpieces so that building predictive models which can generalize over a number of these objects is highly desirable. In this paper, we study the problem of designing deep learning agents which can generalize their models of the physical world by building context-aware learning models. The purpose of these agents is to quickly adapt and/or generalize their notion of physics of interaction in the real world based on certain features about the interacting objects that provide different contexts to the predictive models. With this motivation, we present context-aware zero shot learning (CAZSL, pronounced as casual) models, an approach utilizing a Siamese network architecture, embedding space masking and regularization based on context variables which allows us to learn a model that can generalize to different parameters or features of the interacting objects. We test our proposed learning algorithm on the recently released Omnipush datatset that allows testing of meta-learning capabilities using low-dimensional data. Codes for CAZSL are available at https://www.merl. com/research/license/CAZSL.
Applying Bayesian Hierarchical Probit Model to Interview Grade Evaluation

Job interviews are a fundamental activity for most corporations to acquire potential candidates, and for job seekers to get well-rewarded and fulfilling career opportunities. In many cases, interviews are conducted in multiple processes such as telephone interviews and several face-to-face interviews. At each stage, candidates are evaluated in various aspects. Among them, grade evaluation, such as a rating on a 1-4 scale, might be used as a reasonable method to evaluate candidates. However, because each evaluation is based on a subjective judgment of interviewers, the aggregated evaluations can be biased because the difference in toughness of interviewers is not examined. Additionally, it is noteworthy that the toughness of interviewers might vary depending on the interview round. As described herein, we propose an analytical framework of simultaneous estimation for both the true potential of candidates and toughness of interviewers' judgment considering job interview rounds, with algorithms to extract unseen knowledge of the true potential of candidates and toughness of interviewers as latent variables through analyzing grade data of job interviews. We apply a Bayesian Hierarchical Ordered Probit Model to the grade data from HRMOS, a cloud-based Applicant Tracking System (ATS) operated by BizReach, Inc., an IT start-up particularly addressing human-resource needs in Japan. Our model successfully quantifies the candidate potential and the interviewers' toughness. An interpretation and applications of the model are given along with a discussion of its place within hiring processes in real-world settings. The parameters are estimated by Markov Chain Monte Carlo (MCMC). A discussion of uncertainty, which is given by the posterior distribution of the parameters, is also provided along with the analysis.
Extracting non-Fermi liquid fermionic self-energy at $T=0$ from quantum Monte Carlo data

Quantum Monte Carlo (QMC) simulations of correlated electron systems provide unbiased information about system behavior at a quantum critical point (QCP) and can verify or disprove the existing theories of non-Fermi liquid (NFL) behavior at a QCP. However, simulations are carried out at a finite temperature, where quantum-critical features are masked by finite temperature effects. Here we present a method to rigorously separate thermal and quantum effects and extract the information about NFL physics at $T=0$. We demonstrate our method for a specific example of 2D fermions near a Ising-ferromagnetic QCP. We show that one can accurately extract from QMC data the zero-temperature form of fermionic self-energy $\Sigma (\omega)$ even though the leading contribution to the self-energy comes from thermal effects. We find that the frequency dependence of $\Sigma (\omega)$ agrees well with the analytic form obtained within the Eliashberg theory of dynamical quantum criticality, and obeys $\omega^{2/3}$ scaling at low frequencies. Our results open up a new avenue for QMC studies of quantum-critical metals.
Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality

Abstract Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence rates for regression and classification problems. However, the sparsity constraint requires knowing certain properties of the true model, which are not available in practice. Moreover, computation is difficult due to the discrete nature of the sparsity constraint. In this letter, we propose a novel penalized estimation method for sparse DNNs that resolves the problems existing in the sparsity constraint. We establish an oracle inequality for the excess risk of the proposed sparse-penalized DNN estimator and derive convergence rates for several learning tasks. In particular, we prove that the sparse-penalized estimator can adaptively attain minimax convergence rates for various nonparametric regression problems. For computation, we develop an efficient gradient-based optimization algorithm that guarantees the monotonic reduction of the objective function.
Triad State Space Construction for Chaotic Signal Classification with Deep Learning

Inspired by the well-known permutation entropy (PE), an effective image encoding scheme for chaotic time series, Triad State Space Construction (TSSC), is proposed. The TSSC image can recognize higher-order temporal patterns and identify new forbidden regions in time series motifs beyond the Bandt-Pompe probabilities. The Convolutional Neural Network (ConvNet) is widely used in image classification. The ConvNet classifier based on TSSC images (TSSC-ConvNet) are highly accurate and very robust in the chaotic signal classification.
C-transfinite diameter

We give a general formula for the $C-$transfinite diameter $\delta_C(K)$ of a compact set $K\subset \mathbb{C}^2$ which is a product of univariate compacta where $C\subset (\mathbb{R}^+)^2$ is a convex body. Along the way we prove a Rumely type formula relating $\delta_C(K)$ and the $C-$Robin function $\rho_{V_{C,K}}$ of the $C-$extremal plurisubharmonic function $V_{C,K}$ for $C \subset (\mathbb{R}^+)^2$ a triangle $T_{a,b}$ with vertices $(0,0), (b,0), (0,a)$. Finally, we show how the definition of $\delta_C(K)$ can be extended to include many nonconvex bodies $C\subset \mathbb{R}^d$ for $d-$circled sets $K\subset \mathbb{C}^d$, and we prove an integral formula for $\delta_C(K)$ which we use to compute a formula for the $C-$transfinite diameter of the Euclidean unit ball $\mathbb{B}\subset \mathbb{C}^2$.
Topological superconductor from superconducting topological surface states and fault-tolerant quantum computing

The chiral $p$-wave superconductor/superfluid in two dimensions (2D) is the simplest and most robust system for topological quantum computation . Candidates for such topological superconductors/superfluids in nature are very rare. A widely believed chiral $p$-wave superfluid is the Moore-Read state in the $\nu=\frac{5}2$ fractional quantum Hall effect, although experimental evidence are not yet conclusive. Experimental realizations of chiral $p$-wave superconductors using quantum anomalous Hall insulator-superconductor hybrid structures have been controversial. Here we report a new mechanism for realizing 2D chiral $p$-wave superconductors on the surface of 3D $s$-wave superconductors that have a topological band structure and support superconducting topological surface states (SC-TSS), such as the iron-based superconductor Fe(Te,Se). We find that tunneling and pairing between the SC-TSS on the top and bottom surfaces in a thin film or between two opposing surfaces of two such superconductors can produce an emergent 2D time-reversal symmetry breaking chiral topological superconductor. The topologically protected anyonic vortices with Majorana zero modes as well as the chiral Majorana fermion edge modes can be used as a platform for more advantageous non-abelian braiding operations. We propose a novel device for the CNOT gate with six chiral Majorana fermion edge modes, which paves the way for fault-tolerant universal quantum computing.
Single-shot 3D wide-field fluorescence imaging with a Computational Miniature Mesoscope

Computational Miniature Mesoscope enables single-shot large-scale 3D fluorescence imaging with single-cell level resolution. Fluorescence microscopes are indispensable to biology and neuroscience. The need for recording in freely behaving animals has further driven the development in miniaturized microscopes (miniscopes). However, conventional microscopes/miniscopes are inherently constrained by their limited space-bandwidth product, shallow depth of field (DOF), and inability to resolve three-dimensional (3D) distributed emitters. Here, we present a Computational Miniature Mesoscope (CM2) that overcomes these bottlenecks and enables single-shot 3D imaging across an 8 mm by 7 mm field of view and 2.5-mm DOF, achieving 7-μm lateral resolution and better than 200-μm axial resolution. The CM2 features a compact lightweight design that integrates a microlens array for imaging and a light-emitting diode array for excitation. Its expanded imaging capability is enabled by computational imaging that augments the optics by algorithms. We experimentally validate the mesoscopic imaging capability on 3D fluorescent samples. We further quantify the effects of scattering and background fluorescence on phantom experiments.
Square Function Estimates for Dunkl Operators

Dunkl operators may be regarded as differential-difference operators parameterized by finite reflection groups. In this paper, the Littlewood--Paley square function for Dunkl heat flows in $\mathbb{R}^d$ is introduced by employing the full "gradient" induced by the corresponding carre du champ operator and then the $L^p$ boundedness is studied for all $p\in(1,\infty)$. For $p\in(1,2]$, we successfully adapt Stein's heat flows approach to overcome the difficult caused by the non-local difference part of the Dunkl operator and establish the $L^p$ boundedness, while for $p\in[2,\infty)$, we restrict to a particular case when the corresponding Coxeter group is isomorphic to $\mathbb{Z}_2^d$ and apply a probabilistic method to prove the $L^p$ boundedness. In the latter case, the curvature-dimension condition for Dunkl operators in the sense of Bakry--Emery, which may be of independent interest, plays a crucial role.
The Great Beauty of VEMs

In this paper I review the m ain features of the (newborn) Virtual Ele m ent Method, and of its application to the approxi m ation of boundary value proble m s for Partial Di ﬀ erential Equations of particular relevance for applications. I will m ostly concentrate on the deﬁnition of the Virtual Ele m ent spaces, that, roughly, consist of (vector valued) functions that are solution of (syste m s of) partial differential equations in each subdo m ain of a deco m position of the co m putational do m ain into polygons or polyhedra of quite general shape. Then I will give so m e hint on the use of these spaces for the discretization of so m e classical toy-proble m s like Heat conduction, Darcy ﬂows, and Magnetostatic proble m s.
An overview of the current status of engineered therapeutic monoclonal antibodies

Since the commercialization of the first therapeutic monoclonal antibody (mAb) product in 1986, this class of biopharmaceutical products have grown significantly. Due to the enhanced antigen binding and reduced cellular toxicity, they result in more efficacy in treatment of variety of diseases. The global sales of mAbs which was 95.1 b$ in 2017 have grown annually due to the dramatic increase in cancer and severe diseases rates and are estimated to reach 131.33 b$ by 2023, this represents a clear accelerating trend with more than 5.53% growth. In this review, we discuss some of these mAbs which have been approved by the FDA as well as others that are experiencing or being evaluated in clinical phases. Global sales of some monoclonal antibodies in 2016 are also considered, suggesting a significant increase in sales of mAbs over the years ahead. Keywords: Monoclonal Antibody; Market; Phage display; ScFv Antibodies
Enhancing palatal wound healing by using platelet rich fibrin membrane as fibrin bandage

Aim: Free gingival graft procedure is a frequently used mucogingival procedure for gingival augmentation. One of the major drawback associated with technique is healing of donor site by secondary intention which takes more time to heal and causes patient discomfort. PRF is a novel biomaterial which has shown to accelerate wound healing process. The article describes the healing of donor site with and without placement of PRF membrane in 2 patients. Case presentation: Two patients requiring gingival augmentation were enrolled for the study. The palatal donor site of one patient was covered with PRF membrane as palatal bandage. The donor site of the other patient was allowed to heal in a conventional way without PRF membrane to evaluate the difference in healing. The healing was evaluated visually by using healing index and by hydrogen peroxide test at 12th, 13th, 18th, 19th, 24th, 25th, 30th and 31st days. Palatal donor site covered with PRF membrane demonstrated considerably faster healing and higher healing score compared to site not covered by PRF membrane. Conclusion: The superior healing observed at the PRF membrane site supports its use in accelerating the soft tissue healing. PRF membrane as a bandage is an efficacious approach to protect the open wound in palate to reduce healing time and patient discomfort.
RELATIONSHIP BETWEEN THREE COTTON TRASH MEASUREMENTS: HVI, SA, AND AFIS

Presence of non-lint materials (trashes) in commercial cotton bales at various amounts degrades the market values and further influences the end-use qualities. In order to ensure a fair trading, the USDA’s AMS has introduced the high volume instrument (HVI) measurement as a universal standard index. Trash contents are generated by one of three HVI modules and represent the trash portion only detectable on samples’ surfaces. In additional to HVI’s geometric method, gravimetric-based Shirley analyzer (SA) and advanced fiber information system (AFIS) have also been utilized to yield the trash contents. With the increasing acceptance of HVI readings in the domestic and international trading, there is a continued interest in the relationship between HVI trash and SA trash from cotton fiber customers and regulators. Due to the complexity of not only trash type, size, and its weight distribution but also the nature of HVI and SA tests, it is understandable that there are few studies available trying to bridge two types of trash readings and, apparently, this is a challenge. This study first investigated the correlations between two HVI trash readings, and revealed a general conversion of HVIcount=104.5*HVIarea among low trash samples (HVIarea ≤ 0.40). Then, correlations between the HVI and SA trash and also against AFIS trash were examined, and a stronger relationship between HVI and SA trash than between HVI and AFIS trash was observed. Next, the samples were sub-grouped subjectively according to the ratios of HVIarea/SAvisible (or HVIcount/SAvisible), and from the plots with the least intercepts, it was proposed two general conversions of SAvisible = 6.82*HVIarea and SAvisible = 0.069*HVIcount. In order to verify the likely conversion, NIR spectra were correlated with HVIarea readings. Considering the heterogeneous distribution of trashes in fibers and different sampling specimens between NIR spectral and HVI reference measurement, a 90% confidence interval was applied to exclude outlier samples from the calibration and validation sets. The recalibrated models revealed different response to validation samples in various subsets. Remarkably, the model from samples with the HVIarea/SAvisible ratio of 0.12-0.18 suggested the highest r, RPD, and means/SEP, indicating the most appropriate references for the samples in this subset and echoing the earlier finding from simple descriptive approach of least intercept. Unquestionably, conversion constant might change with relative amount of trash size and type and also their weight distribution.
Foreign Aid, Checks and Balances and Resource Rent

We study the impact of checks and balances on the volume of foreign aid inflows to recipient countries. We hypothesize that higher checks and balances lead to lower volumes of attracting aid because of less rent seeking at the top of the government. However, this impact of checks and balances disappears in the presence of windfalls like natural resource rent because in resource rich countries foreign aid is no longer an important source of nontax revenue and/or rent seeking remains high in spite of constitutional constraints. Our empirical results confirm that in the case of lower natural resource rent, an improvement in checks and balances does reduce the volume of aid. Yet, in the face of increasing natural resource rent, the beneficial impact of checks and balances on aid inflows disappears.
HIV-positive women have higher risk of HPV infection, precancerous lesions, and cervical cancer: A systematic review and meta-analysis

Objectives: HIV-positive women have higher human papillomavirus (HPV) prevalence and cervical cancer (CC) incidence than HIV-negative women, partly due to HIV’s modifying effect on HPV pathogenesis. We synthesized the literature on the impact of HIV on HPV natural history. Design: Systematic review and meta-analysis Methods: We searched the literature for studies evaluating HPV acquisition and persistence or precancer progression by HIV status. Data on HPV natural history by HIV status, CD4+ cell counts, viral load, and antiretroviral therapy (ART) were summarized using fixed effect models. Results: Overall, 38 of 1845 abstracts identified met inclusion criteria. HIV-positive women had higher HPV acquisition (relative risk [RRpooled]=2.64, 95% confidence interval [CI] 2.04-3.42) and lower HPV clearance (hazard ratio [HRpooled]=0.72, 95% CI 0.62-0.84) than HIV-negative women. HPV acquisition was higher with declining CD4 and was lower in those virally suppressed on ART. HIV was associated with higher incidence of low-grade squamous intraepithelial lesions (LSIL) (RRpooled=3.73, 95% CI 2.62-5.32) and high-grade squamous intraepithelial lesions (HSIL) (HRpooled=1.32, 95% CI 1.10-1.58), largely due to increased HPV persistence. ART lowered progression from normal cytology to LSIL (HRpooled=0.65, 95% CI 0.52-0.82), but not HSIL. CC incidence wasassociated with HIV positivity (RR=4.1, 95% CI 2.3-6.6), but not with ART. Copyright © 2018 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. Conclusions: HIV-positive women have higher risk of acquiring HPV, with risk inversely associated with CD4 count. ART lowered HPV acquisition, increased clearance, and reduced precancer progression, likely via immune reconstitution. While some of our results are limited by small number of studies, our study can inform screening guidelines and mathematical modeling for CC prevention.
FishBase, A DATABASE WITH KEY INFORMATION ON CORAL REEF FISHES

FishBase is a biological database with key informationon more than 15,000 finfishes, including most coral reef species. In addition to current nomenclature, FishBase contains information on, e.g., maximum size and age, growth, length-weight relationship, reproduction, diet, predators, occurrence, preferred habitats and depth ranges. Most reef fishes are represented by color photos, and a Quick Identification routine based on dorsal and anal fin ray counts helps with identification. A FishWatcher module lets users create their personal or institutional database on fish encounters while maintaining a link to the full information in FishBase. A National Checklist module lets users create a fish biodiversity database for their country, through which they can keep track of distributional ranges within the country, existing regulations, common uses, etc.; again, all related information in FishBase is just a mouse-click away. The FishWatcher and the National Checklist modules can also be used to contribute information to the global version of the database. FishBase is developed and maintained by the International Center for Living Aquatic Resources Management (ICLARM}, in collaboration with the Food and Agriculture Organization of the United Nations (FAO) and many other partners, with support from the European Commission (EC). FishBase is available on CD-ROM for US$95. It is based on the Microsoft Access relational database and runs under windows on personal computers with at least 8 megabytes of RAM.
EEG Based Attention Tracking During Distracted Driving

Driving is a skill that requires drivers to direct their full attention to control the cars. Distracted driving might lead to many catastrophic consequences. The operation of automotive electronic devices and mobile devices has been greatly augmented during driving. Some of these technological devices increase safety and reduce the drivers’ attention load. However, operating the in-vehicle systems sometimes impairs driver’s attention that is taken away from the primary driving tasks. In particular, conversing on the phone while driving is easily distracting, even with handsfree systems. Also drowsiness is becoming a severe issue in case of traffic accidents. Normally, Sleeping can be identified from several factors like eye blink level, yawning, gripping force on the wheel and so on. But all these measuring techniques will check only the physical activities of the human. In some cases, people will mentally sleep with eyes open for a few seconds. This will make very big accidents in driving. In our proposed project work we are analyzing the mental activities of brain using EEG signals based on BrainComputer Interface (BCI) Technology. Brain-computer interface (BCI), an actively progressing field in brain engineering, refers to a platform that measures the specific intent of the user and issues commands to the computer by using EEG. This kind of interface can be used on various applications.
NCACMP 18 THE EFFECTIVENESS OF NORMALIZED MOLECULAR DESCRIPTORS IN PREDICTING THE DRUGLIKENESS OF MOLECULES

: Among the various routines adopted in the drug development process, computer based screening of potential candidate drug molecules is an important processing stage. The goal of screening is to identify suitable lead molecules and to subject them into the further stages of drug development. The screening could be made fast and reliable by making use of numerical data derived from the candidate molecules and by employing machine learning algorithms like support vector machine. The aim of the work is to map the structure based molecular descriptors in to a high dimensional feature space and to subsequently carry out a linear regression in the feature space. Normalized data sets, in the scaled range between zero and one, are quite effective in the pre-processing stages of machine learning algorithms. Hence, in this work such normalized molecular structure descriptors are used to standardize the independent descriptor data sets of candidate molecules. The molecules belong to anticonvulsant category and the efficiency of anova, polynomial and radial kernels are studied. The results of the prediction are compared with the predictions made by the use of directly derived descriptors, normalized ones and with the predictions made by drug mint, a webserver tool.