Miracle-dz/UniFAR-FTUs huggingface dataset:
Is using the Gail model to calculate the risk of breast cancer in the Venezuelan population justified?

Objective To evaluate the accuracy of the Gail model (GM) in women who already have a diagnosis of breast cancer (BC) from the Breast Pathology Service, Hospital Oncology Department of the Venezuelan Social Security Institute (SOH-IVSS) in the period 2004–2014. To compare the accuracy of the GM in women aged above and below 40 years with a diagnosis of BC. Method Descriptive, retrospective, cross-sectional, 830 records of patients diagnosed with BC were reviewed between 2004 and 2014. Results The mean age for diagnosis of the disease was 46 ± 13 years; menarche age was 13 years ± 2; age at first birth 22 ± 5 years, with a history of biopsy 32 ± 11, the percentage of relatives with a primary history of BC reported (PHBC) 9.3%. Only 41% of women with a diagnosis of BC reported Gail >1.67 (positive Gail). In the dichotomous logistic regression that related positive Gail with the independent variables, it was observed: greater probability of positive Gail if menarche age <11 years (p < 0.036), PHBC (p = 0.005), previous biopsy (p = 0.007), age at first birth 25–29 years (p = 0.019). When stratifying by age, unlike the bivariate analysis, women over 40 years of age are more likely to have a positive Gail in menarche age <11 years (p = 0.008), PHBC (p = 0.001), previous biopsy (p = 0.025) when compared with younger women, the age at first birth between 25 and 29 years was statistically significant for both groups; however, the probability was higher in younger women (p = 0.008). Conclusion There is no conclusive evidence to consider that the GM is applicable to Venezuelan women due to its low precision since it only identified 41% of the patients who had BC as high risk; however, when the factors are analysed separately, we found a higher probability of a positive Gail with statistical significance in EM <11 years, PHBC, previous biopsy and age at first birth 25–29 years; When stratifying by age, we observed that the age at first birth 25–29 years in women aged 40 or less increases the probability of a positive Gail. It is necessary to develop new risk assessment models that are adapted to our female population.
The Capacity of Channels with O(1)-Bit Feedback

We consider point-to-point communication with partial noiseless feedback in which the number of feedback bits is $O(1)$ in the number of transmitted symbols. For $q \geq 2$, we study the general q-ary alphabet setting with both errors and erasures and seek to characterize the zero-error capacity. As our main result, we provide a tight characterization of zero-error capacity which we prove via novel achievability and converse schemes inspired by the study of causal/online adversarial channels without feedback. Perhaps surprisingly, we show that $O(1)$-bits of feedback are sufficient to achieve the zero-error capacity of the error channel with full noiseless feedback when the fraction of transmitted symbols in error is sufficiently small.
Learning support : perceptions and experiences of remote distance learners from marginalised communities in Botswana

This study pertains to the provision of learning support to remote distance learners from the Basarwa and Bakgalagadi communities in Botswana who enrolled for a secondary school-leaving certificate. The purpose of this case study was to document these learners’ perceptions and experiences of learning at a distance and to improve service delivery. The literature on distance learning support emphasises the importance of making learners’ voices audible as these help gauge the efficacy of learning support. The study was informed by an interpretive paradigm using a mixed-methods approach and is underpinned by Holmberg’ theory of conversational learning (2003). Qualitative data methods collection involved semi-structured interviews, journals, document analysis and observations while a questionnaire provided nested quantitative data. Data sets were triangulated and trustworthiness was enhanced by using Atlas.ti® for qualitative analysis and SAS version 8 software to generate percentages. The key findings showed that distance learners exhibited high intrinsic motivation and 72.1% of them were satisfied with the learning support provided. This substantiates that learner motivation remains a key attribute for successful distance learning in any context. However, policy and managerial flaws did frustrate and unintentionally disadvantage these remote distance learners. Despite their adverse circumstances positive perceptions and experiences were exhibited where learners had access to personalised academic and affective support from empathetic tutors. Implications for practice include policy reviews, ODL staff training and adoption of best practice. A needs assessment to establish learner needs, expectations and aspirations is critical for the design and development of relevant learning materials, and for the delivery of quality learning support to enhance the academic experience of remote learners from marginalised communities. Recommendations may be applicable in other underdeveloped distance learning contexts. Topics for further research exploration in learning support, policy and curriculum issues have been suggested.
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering

Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies. However, compared to self-attention mechanism, we find graph convolutions show a stronger ability in capturing local geometry information with less computational cost. In this paper, we employ a hybrid architecture design to construct our Graph Convolution Network with Attentive Filtering (AF-GCN), which takes advantage of both graph convolution and selfattention mechanism. We adopt graph convolutions to aggregate local features in the shallow encoder stages, while in the deeper stages, we propose a self-attention-like module named Graph Attentive Filter (GAF) to better model long-range contexts from distant neighbors. Besides, to further improve graph representation for point cloud segmentation, we employ a Spatial Feature Projection (SFP) module for graph convolutions which helps to handle spatial variations of unstructured point clouds. Finally, a graphshared down-sampling and up-sampling strategy is introduced to make full use of the graph structures in point cloud processing. We conduct extensive experiments on multiple datasets including S3DIS, ScanNetV2, Toronto-3D, and ShapeNetPart. Experimental results show our AF-GCN obtains competitive performance.
: Cognitive and

Manual dexterity is referred to as the skill to perform fine motor movements and it has been assumed to be associated to the cognitive domain, as well as the sensorimotor one. In this work, we investigated with functional near-infrared spectroscopy the cortical activations elicited by the execution of the 9-HPT, i.e., a standard test evaluating manual dexterity in which nine pegs were taken, placed into and then removed from nine holes on a board as quickly as possible. For comparison, we proposed a new active control task mainly involving the sensorimotor domain, in which the pegs must be placed and removed using the same single hole (1-HPT). Behaviorally, we found two distinct groups based on the difference between the execution time of the 9-HPT and the 1-HPT (ΔHPT). Cortical areas belonging to the network controlling reaching and grasping movements were active in both groups; however, participants showing a large ΔHPT presented significantly higher activation in prefrontal cortical areas (right BA10 and BA11) during 9-HPT and 1-HPT performance with respect to the participants with a small ΔHPT, who showed a deactivation in BA10. Unexpectedly, we observed a significant linear relationship between ΔHPT and right BA10 activity. This suggested that participants performing the 9-HPT more slowly than the 1-HPT recruited prefrontal areas implicitly exploiting the cognitive skills of planning, perhaps in search of a motor strategy to solve the test activating attentional and cognitive control processes, but this resulted not efficient and instead increased the time to accomplish a manual dexterity task.
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments

Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e.g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)). StarCraft II game replays encode intelligent (and adversarial) multiagent behavior and could provide a testbed for these tasks; however, extracting simple and standardized representations for prototyping these tasks is laborious and hinders reproducibility. In contrast, MNIST and CIFAR10, despite their extreme simplicity, have enabled rapid prototyping and reproducibility of ML methods. Following the simplicity of these datasets, we construct a benchmark spatial reasoning dataset based on StarCraft II replays that exhibit complex multi-agent behaviors, while still being as easy to use as MNIST and CIFAR10. Specifically, we carefully summarize a window of 255 consecutive game states to create 3.6 million summary images from 60,000 replays, including all relevant metadata such as game outcome and player races. We develop three formats of decreasing complexity: Hyperspectral images that include one channel for every unit type (similar to multispectral geospatial images), RGB images that mimic CIFAR10, and grayscale images that mimic MNIST. We show how this dataset can be used for prototyping spatial reasoning methods. All datasets, code for extraction, and code for dataset loading can be found at https://starcraftdata.davidinouye. com/.
Reaching Consensus in Dense Erdős–Rényi Graphs

Majority dynamics on the binomial Erdős–Rényi graph G(n, p) with $p = \lambda /\sqrt n $ is studied. In this process, each vertex has a state in {0, 1} and at each round, every vertex adopts the state of the majority of its neighbors, retaining its state in the case of a tie. It was conjectured by Benjamini et al. and proved by Fountoulakis et al. that this process reaches unanimity with high probability in at most four rounds. We postulate an improved conjecture, according to which, majority dynamics reaches consensus with high probability in at most three rounds, and support this conjecture by computer simulations. By adding some extra randomness and allowing the underlying graph to be drawn anew in each communication round, we prove that this process reaches consensus in only three communication rounds with probability approaching 1 as n grows to infinity. We also provide a converse result, showing that three rounds are not only sufficient in this case, but also necessary, thus tightly characterizing the communication complexity of this problem for dense Erdős–Rényi graphs with independent instances.
Optimal Subspace Perturbation Bounds under Gaussian Noise

The Davis–Kahan–Wedin theorem describes how the singular subspaces of a matrix change when subjected to a small perturbation. This classic result is sharp in the worst case scenario. In this paper, we prove a stochastic version of the Davis–Kahan–Wedin theorem when the perturbation is a Gaussian random matrix. Under certain structural assumptions, we obtain an optimal bound that significantly improves upon the classic Davis–Kahan–Wedin theorem. One of our key tools is a new perturbation bound for the singular values, which may be of independent interest.
Levenshtein’s Reconstruction Problem with Different Error Patterns

In this paper, we consider Levenshtein’s sequence reconstruction problem in which the same codeword is transmitted through N channels each introducing a different set of errors to the transmitted word. In previous works, it has been assumed that each channel provides a unique output word while we assume that each channel introduces a unique set of errors to the transmitted word and hence some output words can be identical. This property seems natural and we also give several results for this problem. Moreover, we also extend optimal decoding algorithm for substitution errors in binary Hamming space to q-ary Hamming spaces. Especially, quaternary Hamming spaces are relevant due to applications related to DNA-memories. Hence, we introduce an efficient Las Vegas decoding algorithm for simultaneous insertion, deletion and substitution errors in quaternary Hamming spaces.
Arithmetic Complexity of Frequency-Domain Representations of Time-Computable Signals

The duality between time- and frequency-domain representations of information-carrying signals is an established cornerstone of information theory. In terms of computability and signal processing, asymptotically-vanishing sequences are well-behaved in the time-domain, since they can be equipped with Banach-Space norms. It is then possible to define computable asymptotically-vanishing sequences, each of which is characterized by an effective global approximation procedure. In this paper, we investigate whether the time-frequency duality preserves these characteristics, i.e., whether the image of asymptotically-vanishing sequences under the Z-Transform yields a set of computationally well-behaved functions. In particular, we classify the associated radius of convergence into the arithmetical hierarchy of definable numbers by Zheng and Weihrauch, and, as a corollary, present that it may attain non-computable values. We then proceed to investigate the computability of upper and lower bounds on the radius of convergence, as well as several related decidability problems. Lastly, we subsume our insights into a collection of contemporary results on the fundamental limits of numerical techniques in signal processing and information theory.
A Simple and Tighter Derivation of Achievability for Classical-Quantum Channel Coding

In this paper, we propose an elegant and simple one-shot achievability proof for classical-quantum channel coding. The established one-shot bound admits a closed-form expression as in the celebrated Holevo–Helstrom Theorem. Namely, the average error probability of sending M messages through a classical-quantum channel is upper bounded by the minimum error of distinguishing the joint state between channel input and output against (M−1)-many products of its marginals. Our result provides the tightest achievable one-shot capacity for classical-quantum channel coding, and it improves the third-order coding rate in the asymptotic scenario. Moreover, the derived bound directly leads to asymptotic achievability results in the large deviation, small deviation, and moderate deviation regimes in a unified manner.The full version of the manuscript can be found at [arXiv:2208.02132] [1].
Minimax Risk Upper Bounds Based on Shell Analysis of a Quantized Maximum Likelihood Estimator

This paper develops a unified framework for upper bounding the minimax risk in high-dimensional parameter estimation problems. To this end, we study a quantized maximum likelihood estimator, where the estimator computes the likelihood for all points within a discrete cover, and outputs the candidate with the maximal likelihood. While this concept is straightforward, our analysis is quite delicate. It splits the competing candidates in the cover to small shells, and controls the number of candidates in each shell, as well as the probability that a candidate in the shell outscores a candidate which is close to the true parameter. We demonstrate the utility of our bounds by applying them to different Gaussian problems, and showing that they recover the optimal minimax rate for the Gaussian location model and the spiked Wigner Model. For the multi-reference alignment problem we obtain a novel minimax upper bound, which essentially places no assumptions on the signal of interest.
Zero-Error Distributed Function Compression

In this paper, we put forward the model of zero-error distributed function compression system of two binary memoryless sources X and Y as depicted in Fig. 1. In the model, there are two encoders En1 and En2 and one decoder De, connected by two channels with capacity constraints C1 and C2, respectively. The encoder En1 can observe X or (X, Y), and the encoder En2 can observe Y or (X, Y). Here, we use two switches s1 and s2 open or closed (taking values 0 or 1) to represent whether En1 can observe Y and En2 can observe X, respectively. The decoder De is required to compress the binary arithmetic sum f(X, Y) = X + Y with zero error by using the system multiple times. We use (s1s2; C1, C2; f) to denote the model. The compression capacity is defined as the maximum average number of times that the function f can be compressed with zero error for one use of the system, which measures the efficiency for using the system. In the paper, the compression capacities for all the four models are fully characterized. Amongst them, the characterization of the compression capacity for (01; C1, C2; f) is very difficult. Toward this end, we develop a novel graph coloring approach in the converse part and the proof is highly nontrivial. Furthermore, we apply the compression capacity for (01; C1, C2; f) to the open problem in network function computation that whether the best known upper bound by Guang et al. on computing capacity is in general tight. This upper bound is always tight for all previously considered network function computation problems whose computing capacities are known. By considering equivalent network function computation models of (01; C1, C2; f), we give the answer that in general the upper bound of Guang et al. is not tight.
An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed “FlowerConvNet”. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy.
Readers ’ Discourses and the Construction of Climate Change Below the Line

Climate change is the single most pressing existential threat facing humanity, and while the scientific consensus is unassailable, how this knowledge is communicated to and circulated by the public suggests that – in the minds of the public, at least – the science is not yet settled. While there are many reasons that might underpin this perception, given the role of newspapers as conveyors of information in the public interest, how information about climate change is articulated becomes a matter of importance in terms of leveraging changes in consumer behaviour and voter priorities. In this paper, using below-the-line comments of two British national daily online newspapers by readers responding to articles on climate change related topics over a period of six years, from 2014 to 2019 inclusive, as corpora, I explore how the matter of climate change is understood and articulated by applying linguistic analyses to generate profiles characterising the discourse of each corpus. This study lends broad support to earlier research by demonstrating that the readership of the politically right-leaning newspaper tends towards an ‘attack discourse’ and general scepticism, even hostility, towards the scientific consensus, while the readership of the politically left leaning paper evidence support for the consensus. The paper concludes by considering some of the implications of these findings for scientific communications about climate change.
Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks

Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware. State-of-the-art SNNs are typically composed of simple Leaky Integrate-and-Fire (LIF) neurons and have become comparable to ANNs in image classification tasks on large-scale datasets. However, the robustness of these deep SNNs has not yet been fully uncovered. In this paper, we first experimentally observe that layers in these SNNs mostly communicate by rate coding. Based on this rate coding property, we develop a novel rate coding SNN-specified attack method, Rate Gradient Approximation Attack (RGA). We generalize the RGA attack to SNNs composed of LIF neurons with different leaky parameters and input encoding by designing surrogate gradients. In addition, we develop the time-extended enhancement to generate more effective adversarial examples. The experiment results indicate that our proposed RGA attack is more effective than the previous attack and is less sensitive to neuron hyperparameters. We also conclude from the experiment that rate-coded SNN composed of LIF neurons is not secure, which calls for exploring training methods for SNNs composed of complex neurons and other neuronal codings. Code is available at https://github.com/putshua/SNN_attack_RGA
A Geometric Study of Wasserstein Spaces: Isometric Rigidity in Negative Curvature

This article deals with the space of probability measures (with finite second order moments) over a CAT(0) space. The Wasserstein metric turns this space of measures into a geodesic space called Wasserstein space. We are interested in the geometric properties of this space. In this paper, we study its isometry group. The main result is that the isometry group of the Wasserstein space of a CAT(0) space X coincides with that of X when X is negatively curved. This result is known to be false when X is the Euclidean space.
On the best approximation by finite Gaussian mixtures

We consider the problem of approximating a general Gaussian location mixture by finite mixtures. The minimum order of finite mixtures that achieve a prescribed accuracy (measured by various f-divergences) are determined within constant factors for the family of compactly supported or subgaussian mixing distributions. While the upper bound is achieved using the technique of local moment matching, the lower bound is established by relating the best approximation error to the low-rank approximation of certain trigonometric moment matrices and weighted moment matrices, followed by a refined spectral analysis of the minimum eigenvalue of these matrices. In the case of Gaussian mixing distributions, this result corrects a previous lower bound in [1].