Miracle-dz/UniFAR-FTUs huggingface dataset:
Elevated Pre-Treatment Systemic Immuno-Inflammatory Indices, Triple-Negative Breast Cancer, and p53 Mutation are Associated with Early-Onset Breast Cancer in Southern Nigeria

Background: In West Africa, breast cancer (BC) patients have a mortality rate that is three times higher than those in North America and Northwestern Europe. This study aimed to identify high-risk patients by evaluating the pre-treatment systemic inflammatory indices, p53, and BRCA2 expressions in molecular sub-types of BC in West Africa. Methods: This retrospective cohort study included 152 BC tissues, diagnosed between January 2017 and December 2022. The tissue sections were immunohistochemically stained for p53, BRCA2, hormone receptors, and human epidermal growth factor receptor 2 (HER2), scored, and analyzed accordingly. Statistical significance was set at p[≤] 0.05. Results: The frequency of early-onset BC (49 years old or younger) was 58.6% while the frequency of early-onset BC among patients with a family history of cancer was 76.5%. The frequency of late-stage BC was 84.9%. The frequency of luminal A and triple-negative BC (TNBC) was 1.7 times higher in early-onset BC. In comparison, the frequency of Luminal B/B-like and HER2-enriched BC was 1.9 times higher in late-onset BC (p= 0.022). The frequency of p53 and BRCA2 mutation was 1.6 times and 1.2 times higher in early-onset BC than in late-onset BC (p= 0.003 and p= 0.843, respectively). Significant differences in pre-treatment systemic inflammatory index were observed between patients with early-onset and late-onset BC, and less than 6 months survival and > 12 months survival (p< 0.05). Conclusion: This study found a high incidence of early-onset BC, p53 mutation, and TNBC. Additionally, it suggests that pre-treatment systemic inflammatory indices can identify high-mortality-risk patients and early-onset BC.
Automated identification and characterization method of turbulent bursting from single-point records of the velocity field

A new automated method capable of accurately identifying bursting periods in single-point turbulent velocity field records is presented. Manual selection of the method sensitivity τ∗ and threshold εT are necessary for effective discrimination between burst periods and the background turbulent flow fluctuations (burst-free periods). The flow characteristic used for identification is the normalized ‘instantaneous’ TKE dissipation rate levels, calculated using sliding window averaging. Use of the record root mean square and average values for normalization eliminates the need for definition of a physics-based flow-specific threshold. Instead, the suitable sensitivity range and the threshold parameters are selected based on preliminary examination of the velocity records. This, potentially, makes the method applicable for use across various flow fields, especially as it does not require resolving the burst-generation mechanism. The method performance is examined using a field obtained dataset of buoyancy driven turbulent boundary layer flow. Here, the selection of a two-fold εT=2increase is used and the sensitivity of the method is examined. Spectral shapes of non-bursting periods show distinguished similarity to those of the Kolmogorov theory, while the bursting period spectral shapes vary significantly. Low-resolution records of temperature fluctuations were observed to exhibit a significant decrease in temperature (scalar) dissipation rate during bursting periods. Based on this observation and additional processing, a statistical examination of temperature (scalar) dissipation rate is presented along with a normalization procedure. Future examination of additional scalar variations, i.e. particulate matter and/or gaseous pollutant concentrations, in connection with turbulent bursting periods can assist in further understanding of bursting generation and scalar transfer processes.
KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm

Knowledge representation is a long-history topic in AI, which is very important. A variety of models have been proposed for knowledge graph embedding, which projects symbolic entities and relations into continuous vector space. However, most related methods merely focus on the data-fitting of knowledge graph, and ignore the interpretable semantic expression. Thus, traditional embedding methods are not friendly for applications that require semantic analysis, such as question answering and entity retrieval. To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments show that our model outperforms other state-of-the-art baselines substantially.
Active Learning with Multiple Views

Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.
Soft then Hard: Rethinking the Quantization in Neural Image Compression

The core of this experiment is an image compression task. We build the compression model that first encodes the image from MNIST dataset to latent variables. We then try three quantization methods to discretize the latent variables for end-to-end optimization. A decoder will correspondingly generate the reconstruction from the quantized latent variables. Since the image resolution in MNIST dataset is 28× 28, the network architecture is designed as follows:
Geometrical and Numerical Design of Structured Unitary Space&#8211;Time Constellations

There exist two important design criteria for unitary space time codes. In the situation where the signal-to-noise ratio (SNR) is large the diversity product (DP) of a constellation should be as large as possible. It is less known that the diversity sum (DS) is a very important design criterion for codes working in a low SNR environment. So far, no general method to design good-performing constellations with large diversity for any number of transmit antennas and any transmission rate exists. In this correspondence, we propose constellations with suitable structures, which allow one to construct codes with excellent diversity using geometrical symmetry and numerical methods. The presented design methods work for any dimensional constellation and for any transmission rate
World's largest extrusive body of sand?

Using three-dimensional seismic and well data from the northern North Sea, we describe a large (10 km 3 ) body of sand and interpret it as extrusive. To our knowledge, this is the world9s largest such sandbody. It would bury Manhattan, New York (60 km 2 ), under 160 m of sand, or the whole of London, UK (1579 km 2 ), under 6 m of sand. This sand vented to the seafloor, when it was more than 500 m deep, during the Pleistocene glacial period. The sandbody (1) covers an area of more than 260 km 2 , (2) is up to 125 m thick, (3) fills low areas around mounds, which formed when underlying sand injectites lifted the overburden, (4) wedges out, away from a central thick zone, (5) is locally absent along irregular ditches, 20 km long and up to 50 m deep, which overlie feeders on the flanks of the mounds, and (6) consists of fine-grained to medium-grained, sub-rounded to rounded grains. We compare the distribution of the sand with the results of scaled physical experiments. In our interpretation, high fluid pressure fractured the regional Hordaland Group seal in the study area, so that fluidized sand moved rapidly to the seafloor through fissures on the flanks of underlying mounds, mixed with seawater, and formed lateral gravity currents. These transported the sand as much as 8 km away from the blow-out fissures and formed extruded sand sheets. Large extrusive sands represent a new type of economically interesting reservoir.
ESTIMATION OF STOCHASTIC VOLATILITY MODELS BY NONPARAMETRIC FILTERING

A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties of the proposed estimators.
Resource GENCODE : The reference human genome annotation for The ENCODE Project

GENCODE: The reference human genome annotation for The ENCODE Project Jennifer Harrow, Adam Frankish, Jose M. Gonzalez, Electra Tapanari, Mark Diekhans, Felix Kokocinski, Bronwen L. Aken, Daniel Barrell, Amonida Zadissa, Stephen Searle, If Barnes, Alexandra Bignell, Veronika Boychenko, Toby Hunt, Mike Kay, Gaurab Mukherjee, Jeena Rajan, Gloria Despacio-Reyes, Gary Saunders, Charles Steward, Rachel Harte, Michael Lin, Cédric Howald, Andrea Tanzer, Thomas Derrien, Jacqueline Chrast, Nathalie Walters, Suganthi Balasubramanian, Baikang Pei, Michael Tress, Jose Manuel Rodriguez, Iakes Ezkurdia, Jeltje van Baren, Michael Brent, David Haussler, Manolis Kellis, Alfonso Valencia, Alexandre Reymond, Mark Gerstein, Roderic Guigó, and Tim J. Hubbard Wellcome Trust Sanger Institute, Wellcome Trust Campus, Hinxton, Cambridge CB10 1SA, United Kingdom; University of California, Santa Cruz, California 95064, USA; Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA; Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland; Centre for Genomic Regulation (CRG) and UPF, 08003 Barcelona, Catalonia, Spain; Yale University, New Haven, Connecticut 06520-8047, USA; Spanish National Cancer Research Centre (CNIO), E-28029 Madrid, Spain; Center for Genome Sciences & Systems Biology, St. Louis, Missouri 63130, USA
Multi-Column Deep Neural Networks for offline handwritten Chinese character classification

Multi-Column Deep Neural Networks achieve state of the art recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human accuracy. This performance is the result of averaging 11-layers deep networks with hundreds of maps per layer, trained on raw, distorted images to prevent them from overfitting. The entire framework runs on a normal desktop computer with a CUDA capable graphics card.
Use of the Triangular Fuzzy Numbers for Student Assessment (Revised)

In an earlier work we have used the Triangular Fuzzy Numbers (TFNs) as an assessment tool of student skills. This approach led to an approximate linguistic characterization of the students’ overall performance, but it was not proved to be sufficient in all cases for comparing the performance of two different student groups, since two TFNs are not always comparable. In the present paper we complete the above fuzzy assessment approach by presenting a defuzzification method of TFNS based on the Center of Gravity (COG) technique, which enables the required comparison. In addition we extend our results by using the Trapezoidal Fuzzy Numbers (TpFNs) too, which are a generalization of the TFNs, for student assessment and we present suitable examples illustrating our new results in practice.
Theory of Deep Learning III : explaining the non-overfitting puzzle by

A main puzzle of deep networks revolves around the absence of overfitting despite overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamical systems associated with gradient descent minimization of nonlinear networks behave near zero stable minima of the empirical error as gradient system in a quadratic potential with degenerate Hessian. The proposition is supported by theoretical and numerical results, under the assumption of stable minima of the gradient. Our proposition provides the extension to deep networks of key properties of gradient descent methods for linear networks, that as, suggested in (1), can be the key to understand generalization. Gradient descent enforces a form of implicit regularization controlled by the number of iterations, and asymptotically converging to the minimum norm solution. This implies that there is usually an optimum early stopping that avoids overfitting of the loss (this is relevant mainly for regression). For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for “low noise” datasets. The implied robustness to overparametrization has suggestive implications for the robustness of deep hierarchically local networks to variations of the architecture with respect to the curse of dimensionality. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. 1 ar X iv :1 80 1. 00 17 3v 1 [ cs . L G ] 3 0 D ec 2 01 7
T HE F OES OF N EURAL N ETWORK ’ S D ATA E FFICIENCY A MONG U NNECESSARY I NPUT D IMENSIONS

Input dimensions are unnecessary for a given task when the target function can be expressed without such dimensions. Object’s background in image recognition or redundant sentences in text classification are examples of unnecessary dimensions that are often present in datasets. Deep neural networks achieve remarkable generalization performance despite the presence of unnecessary dimensions but it is unclear whether these dimensions negatively affect neural networks or how. In this paper, we investigate the impact of unnecessary input dimensions on one of the central issues of machine learning: the number of training examples needed to achieve high generalization performance, which we refer to as the network’s data efficiency. In a series of analyses with multi-layer perceptrons and deep convolutional neural networks, we show that the network’s data efficiency depends on whether the unnecessary dimensions are task-unrelated or task-related (unnecessary due to redundancy). Namely, we demonstrate that increasing the number of task-unrelated dimensions leads to an incorrect inductive bias and as a result degrade the data efficiency, while increasing the number of task-related dimensions helps to alleviate the negative impact of the task-unrelated dimensions. These results highlight the need for mechanisms that remove task-unrelated dimensions, such as crops or foveation for image classification, to enable data efficiency gains.
Sparse graph-based transduction for image classification

Abstract. Motivated by the remarkable successes of graph-based transduction (GT) and sparse representation (SR), we present a classifier named sparse graph-based classifier (SGC) for image classification. In SGC, SR is leveraged to measure the correlation (similarity) of every two samples and a graph is constructed for encoding these correlations. Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph. Finally, SGC can be obtained by plugging the graph Laplacian into the conventional GT framework. In the image classification procedure, SGC utilizes the correlations which are encoded in the learned graph Laplacian, to infer the labels of unlabeled images. SGC inherits the merits of both GT and SR. Compared to SR, SGC improves the robustness and the discriminating power of GT. Compared to GT, SGC sufficiently exploits the whole data. Therefore, it alleviates the undercomplete dictionary issue suffered by SR. Four popular image databases are employed for evaluation. The results demonstrate that SGC can achieve a promising performance in comparison with the state-of-the-art classifiers, particularly in the small training sample size case and the noisy sample case.
Inferring Gender: A Scalable Methodology for Gender Detection with Online Lexical Databases

This paper presents a new method for automatic detection of gendered terms in large-scale language datasets. Currently, the evaluation of gender bias in natural language processing relies on the use of manually compiled lexicons of gendered expressions, such as pronouns and words that imply gender. However, manual compilation of lists with lexical gender can lead to static information if lists are not periodically updated and often involve value judgements by individual annotators and researchers. Moreover, terms not included in the lexicons fall out of the range of analysis.To address these issues, we devised a scalable dictionary-based method to automatically detect lexical gender that can provide a dynamic, up-to-date analysis with high coverage. Our approach reaches over 80% accuracy in determining the lexical gender of words retrieved randomly from a Wikipedia sample and when testing on a list of gendered words used in previous research.
VON NEUMANN ENTROPY PENALIZATION AND LOW-RANK MATRIX ESTIMATION

We study a problem of estimation of a Hermitian nonnegatively deﬁnite matrix ρ of unit trace (e.g., a density matrix of a quantum system) based on n i.i. d. measurements (X 1 ,Y 1 ),. ,(X n ,Y n ) , where Y j = tr (ρX j ) + ξ j , j = 1 ,. ,n, { X j } being random i.i. d. Hermitian matrices and { ξ j } being i.i. d. random variables with E (ξ j | X j ) = 0. The estimator argmin is considered, where S is the set of all nonnegatively deﬁnite Hermitian m × m matrices of trace 1. The goal is to derive oracle inequalities showing how the estimation error depends on the accuracy of approximation of the unknown state ρ by low-rank matrices. , , , ,
High-Accuracy Low-Precision Training

There is currently an arms race to design low-precision hardware accelerators capable of training machine learning models. This is because purpose-built, low-precision hardware accelerators can lower both the time and energy needed to complete a task. Unfortunately, the statistical effects of low-precision computation during training are still not well understood. As a result, it is difficult to reach the statistical accuracies of traditional hardware architectures on these new, lower-precision accelerators. This is due to a tradeoff with standard training algorithms: as the number of bits is decreased, noise that limits statistical accuracy is increased. In this paper we argue that one can reap the hardware benefits of low-precision accelerators without sacrificing statistical accuracy. To do this we introduce a training algorithm called High-Accuracy Low-Precision (HALP). HALP is a low-precision stochastic gradient descent variant that only uses low-precision computation in its inner loop while infrequently recentering with higher-precision computation in an outer loop. On strongly convex problems, we show both theoretically and empirically that HALP converges at the same linear rate as full-precision SVRG. Inspired by these results, we show on neural network applications (CNNs and a LSTM) that HALP can empirically match higher-precision training algorithms.
Partial Optimality by Pruning for MAP-Inference with General Graphical Models

We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random Fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal nonrelaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous ap proaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.
, Fellow , IEEE , and Charalampos Mavroforakis , and

The paper presents a new framework for complex Support Vector Regression as well as Support Vector Machines for quaternary classification. The method exploits the notion of widely linear estimation to model the input-output relation for complex-valued data and considers two cases: a) the complex data are split into their real and imaginary parts and a typical real kernel is employed to map the complex data to a complexified feature space and b) a pure complex kernel is used to directly map the data to the induced complex feature space. The recently developed Wirtinger’s calculus on complex reproducing kernel Hilbert spaces (RKHS) is employed in order to compute the Lagrangian and derive the dual optimization problem. As one of our major results, we prove that any complex SVM/SVR task is equivalent with solving two real SVM/SVR tasks exploiting a specific real kernel which is generated by the chosen complex kernel. In particular, the case of pure complex kernels leads to the generation of new kernels, which have not been considered before. In the classification case, the proposed framework inherently splits the complex space into four parts. This leads naturally in solving the four class-task (quaternary classification), instead of the typical two classes of the real SVM. In turn, this rationale can be used in a multiclass problem as a split-class scenario based on four classes, as opposed to the one-versus-all method; this can lead to significant computational savings. Experiments demonstrate the effectiveness of the proposed framework for regression and classification tasks that involve complex data.
RESOLUTION OF THE LINEAR-BOUNDED AUTOMATA

This work resolves a longstanding open question in automata theory, i.e., the linear-bounded automata question (LBA question), which can also be phrased succinctly in the language of computational complexity theory as DS PACE[n] ? = NS PACE[n]. We show first that DS PACE[n] , NS PACE[n]. Then, we show by padding argument that DS PACE[log n] , NS PACE[log n]. Our proof technique is primarily based on diagonalization by a universal nondeterministic n space-bounded Turing machine against all deterministic n space-bounded Turing machines. Our proof also implies the following fundamental consequences: (1) There is no deterministic Turing machine of space complexity log n deciding the st-connectivity question (STCON); (2) L , P.