Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble Riemannian Data Assimilation over the Wasserstein
Space

Abstract. In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Eulerian penalization of error in the Euclidean space, the Wasserstein metric can capture translation and difference between the shapes of square-integrable probability distributions of the background state and observations – enabling to formally penalize geophysical biases in state-space with non-Gaussian distributions. The new approach is applied to dissipative and chaotic evolutionary dynamics and its potential advantages and limitations are highlighted compared to the classic variational and filtering data assimilation approaches under systematic and random errors.
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition

We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the expected characteristics (length-scale, smoothness, cyclicity etc.) of the optimal solution encoded into the covariance function of a Gaussian Process. Our algorithm generates a sequence of finite-dimensional random subspaces of functional space spanned by a set of draws from the experimenter's Gaussian Process. Standard Bayesian optimisation is applied on each subspace, and the best solution found used as a starting point (origin) for the next subspace. Using the concept of effective dimensionality, we analyse the convergence of our algorithm and provide a regret bound to show that our algorithm converges in sub-linear time provided a finite effective dimension exists. We test our algorithm in simulated and real-world experiments, namely blind function matching, finding the optimal precipitation-strengthening function for an aluminium alloy, and learning rate schedule optimisation for deep networks.
Nanomechanical damping via electron-assisted relaxation of two-level systems

We report on measurements of dissipation and frequency noise at millikelvin temperatures of nanomechanical devices covered with aluminum. A clear excess damping is observed after switching the metallic layer from superconducting to the normal state with a magnetic field. Beyond the standard model of internal tunneling systems coupled to the phonon bath, here we consider the relaxation to the conduction electrons together with the nature of the mechanical dispersion laws for stressed/unstressed devices. With these key ingredients, a model describing the relaxation of two-level systems inside the structure due to interactions with electrons and phonons with well separated timescales captures the data. In addition, we measure an excess 1/f-type frequency noise in the normal state, which further emphasizes the impact of conduction electrons.
Combinatorics-Based Approaches to Controllability Characterization for Bilinear Systems

The control of bilinear systems has attracted considerable attention in the field of systems and control for decades, owing to their prevalence in diverse applications across science and engineering disciplines. Although much work has been conducted on analyzing controllability properties, the mostly used tool remains the Lie algebra rank condition. In this paper, we develop alternative approaches based on theory and techniques in combinatorics to study controllability of bilinear systems. The core idea of our methodology is to represent vector fields of a bilinear system by permutations or graphs, so that Lie brackets are represented by permutation multiplications or graph operations, respectively. Following these representations, we derive combinatorial characterization of controllability for bilinear systems, which consequently provides novel applications of symmetric group and graph theory to control theory. Moreover, the developed combinatorial approaches are compatible with Lie algebra decompositions, including the Cartan and non-intertwining decomposition. This compatibility enables the exploitation of representation theory for analyzing controllability, which allows us to characterize controllability properties of bilinear systems governed by semisimple and reductive Lie algebras.
Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition

Acoustic and linguistic analysis for elderly emotion recognition is an under-studied and challenging research direction, but essential for the creation of digital assistants for the elderly, as well as unobtrusive telemonitoring of elderly in their residences for mental healthcare purposes. This paper presents our contribution to the INTERSPEECH 2020 Computational Paralinguistics Challenge (ComParE) - Elderly Emotion Sub-Challenge, which is comprised of two ternary classification tasks for arousal and valence recognition. We propose a bi-modal framework, where these tasks are modeled using state-of-the-art acoustic and linguistic features, respectively. In this study, we demonstrate that exploiting task-specific dictionaries and resources can boost the performance of linguistic models, when the amount of labeled data is small. Observing a high mismatch between development and test set performances of various models, we also propose alternative training and decision fusion strategies to better estimate and improve the generalization performance.
Monotonicity of entropy for unimodal real quadratic rational maps

<p style='text-indent:20px;'>We show that the topological entropy is monotonic for unimodal interval maps which are obtained from the restriction of quadratic rational maps with real coefficients and real critical points. This confirms a conjecture made in [<xref ref-type="bibr" rid="b8">8</xref>]. </p>
Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy

Federated Learning (FL) allows multiple participants to collaboratively train machine learning models by keeping their datasets local and exchanging model updates. Recent work has highlighted weaknesses related to robustness and privacy in FL, including backdoor, membership and property inference attacks. In this paper, we investigate whether and how Differential Privacy (DP) can be used to defend against attacks targeting both robustness and privacy in FL. To this end, we present a first-of-its-kind experimental evaluation of Local and Central Differential Privacy (LDP/CDP), assessing their feasibility and effectiveness. We show that both LDP and CDP do defend against backdoor attacks, with varying levels of protection and utility, and overall more effectively than non-DP defenses. They also mitigate white-box membership inference attacks, which our work is the first to show. Neither, however, defend against property inference attacks, prompting the need for further research in this space. Overall, our work also provides a re-usable measurement framework to quantify the trade-offs between robustness/privacy and utility in differentially private FL.
Finding Diverse Trees, Paths, and More

Mathematical modeling is a standard approach to solve many real-world problems and diversity of solutions is an important issue, emerging in applying solutions obtained from mathematical models to real-world problems. Many studies have been devoted to finding diverse solutions. Baste et al. (Algorithms 2019, IJCAI 2020) recently initiated the study of computing diverse solutions of combinatorial problems from the perspective of fixed-parameter tractability. They considered problems of finding r solutions that maximize some diversity measures (the minimum or sum of the pairwise Hamming distances among them) and gave some fixed-parameter tractable algorithms for the diverse version of several well-known problems, such as Vertex Cover, Feedback Vertex Set, d-Hitting Set}, and problems on bounded-treewidth graphs. In this work, we further investigate the (fixed-parameter) tractability of problems of finding diverse spanning trees, paths, and several subgraphs. In particular, we show that, given a graph G and an integer r, the problem of computing r spanning trees of G maximizing the sum of the pairwise Hamming distances among them can be solved in polynomial time. To the best of the authors' knowledge, this is the first polynomial-time solvable case for finding diverse solutions of unbounded size.
Minimality of balls in the small volume regime for a general Gamow-type functional

Abstract We consider functionals given by the sum of the perimeter and the double integral of some kernel g : ℝ N × ℝ N → ℝ + {g:\mathbb{R}^{N}\times\mathbb{R}^{N}\to\mathbb{R}^{+}} , multiplied by a “mass parameter” ε. We show that, whenever g is admissible, radial and decreasing, the unique minimizer of this functional among sets of given volume is the ball as soon as ε ≪ 1 {\varepsilon\ll 1} .
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling, efficiency and effectiveness are still not fully achieved by recent works that use complicate structures and overlook risk of false negative instances. In this paper, we first provide a novel understanding of negative instances by empirically observing that only a few instances are potentially important for model learning, and false negatives tend to have stable predictions over many training iterations. Above findings motivate us to simplify the model by sampling from designed memory that only stores a few important candidates and, more importantly, tackle the untouched false negative problem by favouring high-variance samples stored in memory, which achieves efficient sampling of true negatives with high-quality. Empirical results on two synthetic datasets and three real-world datasets demonstrate both robustness and superiorities of our negative sampling method.
Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective

Deep Learning algorithms have achieved state-of-the-art performance for Image Classification. For this reason, they have been used even in security-critical applications, such as biometric recognition systems and self-driving cars. However, recent works have shown those algorithms, which can even surpass human capabilities, are vulnerable to adversarial examples. In Computer Vision, adversarial examples are images containing subtle perturbations generated by malicious optimization algorithms to fool classifiers. As an attempt to mitigate these vulnerabilities, numerous countermeasures have been proposed recently in the literature. However, devising an efficient defense mechanism has proven to be a difficult task, since many approaches demonstrated to be ineffective against adaptive attackers. Thus, this article aims to provide all readerships with a review of the latest research progress on Adversarial Machine Learning in Image Classification, nevertheless, with a defender’s perspective. This article introduces novel taxonomies for categorizing adversarial attacks and defenses, as well as discuss possible reasons regarding the existence of adversarial examples. In addition, relevant guidance is also provided to assist researchers when devising and evaluating defenses. Finally, based on the reviewed literature, this article suggests some promising paths for future research.
Regular ArticleEvidence for massive clonal growth in the invasive weed Fallopia japonica (Japanese Knotweed)

Clonal growth in introduced populations of Japanese Knotweed (Fallopia japonica) in Britain was assessed using RAPDs (Randomly Amplified Polymorphic DNA). A total of 150 British samples was analysed for genetic variation using ten arbitrary decamer primers, and compared with data from 16 samples of other introduced populations from Europe and the U.S.A. All samples produced an identical multi-primer RAPD profile. Accepting that RAPD profile identity need not equate to genet identity, based on the sensitivity of these markers for detecting genetic diversity in related taxa and on the absence of male fertile individuals of this species in Britain, we interpret this result as consistent with the presence of a single, exceptionally widespread clone. This clone must represent one of the world's largest vascular plants.
Physical conditions of five O vi absorption systems towards PG 1522+101

We present the analysis of five O VI absorbers identified across a redshift path of z $\sim (0.6 - 1.3)$ towards the background quasar PG $1522+101$ with information on five consecutive ionization stages of oxygen from O II to O VI. The combined $HST$ and $Keck$ spectra cover UV, redshifted EUV, and optical transitions from a multitude of ions spanning ionization energies in the range of $\sim (13 - 300)$ eV. Low ionization (C II, O II, Si II, Mg II) and very high ionization species (Ne VIII, Mg X) are non-detections in all the absorbers. Three of the absorbers have coverage of He I, in one of which it is a $> 3 \sigma$ detection. The kinematic structures of these absorbers are extracted from C IV detected in $HIRES$ spectra. The farthest absorber in our sample also contains the detections of Ne V and Ne VI. Assuming co-spatial absorbing components, the ionization models show the medium to be multiphased with small-scale density-temperature inhomogeneities that are sometimes kinematically unresolved. In two of the absorbers, there is an explicit indication of the presence of a warm gas phase ($T \gtrsim 10^5$ K) traced by O VI. In the remaining absorbers, the column densities of the ions are consistent with a non-uniform photoionized medium. The sub-solar [C/O] relative abundances inferred for the absorbers point at enrichment from massive Type II supernovae. Despite metal enrichment, the inferred wide range for [O/H] $\sim$ [$-2.1, +0.2$] amongst the absorbers along with their anti-correlation with the observed H I suggest poor small-scale mixing of metals with hydrogen in the regions surrounding galaxies and the IGM.
Alternating Direction Method of Multipliers for Quantization

Quantization of the parameters of machine learning models, such as deep neural networks, requires solving constrained optimization problems, where the constraint set is formed by the Cartesian product of many simple discrete sets. For such optimization problems, we study the performance of the Alternating Direction Method of Multipliers for Quantization ($\texttt{ADMM-Q}$) algorithm, which is a variant of the widely-used ADMM method applied to our discrete optimization problem. We establish the convergence of the iterates of $\texttt{ADMM-Q}$ to certain $\textit{stationary points}$. To the best of our knowledge, this is the first analysis of an ADMM-type method for problems with discrete variables/constraints. Based on our theoretical insights, we develop a few variants of $\texttt{ADMM-Q}$ that can handle inexact update rules, and have improved performance via the use of "soft projection" and "injecting randomness to the algorithm". We empirically evaluate the efficacy of our proposed approaches.
High-throughput relation extraction algorithm development associating knowledge articles and electronic health records

Objective: Medical relations are the core components of medical knowledge graphs that are needed for healthcare artificial intelligence. However, the requirement of expert annotation by conventional algorithm development processes creates a major bottleneck for mining new relations. In this paper, we present Hi-RES, a framework for high-throughput relation extraction algorithm development. We also show that combining knowledge articles with electronic health records (EHRs) significantly increases the classification accuracy. Methods: We use relation triplets obtained from structured databases and semistructured webpages to label sentences from target corpora as positive training samples. Two methods are also provided for creating improved negative samples by combining positive samples with naive negative samples. We propose a common model that summarizes sentence information using large-scale pretrained language models and multi-instance attention, which then joins with the concept embeddings trained from the EHRs for relation prediction. Results: We apply the Hi-RES framework to develop classification algorithms for disorder-disorder relations and disorder-location relations. Millions of sentences are created as training data. Using pretrained language models and EHR-based embeddings individually provides considerable accuracy increases over those of previous models. Joining them together further tremendously increases the accuracy to 0.947 and 0.998 for the two sets of relations, respectively, which are 10-17 percentage points higher than those of previous models. Conclusion: Hi-RES is an efficient framework for achieving high-throughput and accurate relation extraction algorithm development.
Weed Control in Cotton(Gossypium hirsutum L.)

Pre-emergence applications of thiazopyr at 240 g ha−1 significantly reduced the density and dry weight of weeds over weedy check. Whereas pre-plant incorporation of trifluralin at 1000 g ha−1 followed by glyphosate at 1025 g ha−1 or glufosinate 500 g ha−1 and paraquat 360 g ha−1 proved next best treatments and at par with each other. Significantly less per cent control Echinochloa crusgalli L. and Trianthema portulacastrum L. was noticed where trifluralin at 1000 g ha−1 was applied alone. Seed cotton yield was recorded in season long weed-free situation. Pre-emergence application of thiazopyr at 240 g ha−1 or pre-pht incorporation of trifluralin at 1000 g ha−1 and followed by other post-emergence herbicides increased the seed cotton yield significantly over weedy check. Single application of glyphosate at 1025 g ha−1 or glufosinate at 500 g ha−1 at 40 and 60 DAS increased the seed cotton yield.
Relationships Among Apathy, Depression, and Cognitive Impairment in HIV/AIDS

This study was designed to determine whether apathy is associated with neurocognitive symptoms and/or depressive symptoms in HIV/AIDS and also whether apathy is associated with patient expectancies about antiretroviral medication adherence. Seventy-five HIV+ homosexual men and 58 HIV+ women were assessed for depressive disorders and symptoms. Neuropsychological tests measured attention, concentration, learning, memory, executive function, and psychomotor speed. Other measures included Marin's Apathy Evaluation Scale, the Adherence Determinants Questionnaire, CD4 cell count, and HIV RNA viral load. Apathy was consistently related to depression and unrelated to neuropsychological impairment. Patient expectancies regarding medication adherence were unrelated to apathy when the analysis was controlled for depressive symptoms.
Mild hypothermia mitigates post-ischemic neuronal death following focal cerebral ischemia in rat brain: Immunohistochemical study of Fas, caspase-3 and TUNEL

Mild hypothermia is considered to have a protective effect during ischemic neuronal cell death. The present study provides experimental evidence for this beneficial role of mild hypothermia using reversible middle cerebral artery occlusion (MCAo) in a Sprague-Dawley (SD) rat model. MCAo was induced in rats for 1 h followed by reperfusion at different periods. Hematoxylin-eosin (HE) staining in normothermic (NT) 37 degrees C and hypothermic (HT) 33 degrees C groups of rats confirmed cerebral infarcts. The mean per cent infarct area was significantly reduced in the HT group of rats. Immunohistochemical analysis was done using anti-Fas and caspase-3 antibodies. The immunohistochemical expression of Fas and caspase-3 was demonstrable as early as 5 h after reperfusion, but the expression pattern maximized at 24 h after reperfusion. The expression of Fas and caspase-3 proteins showed a clear decrease in the HT group over the NT group. In situ detection of DNA fragmentation was done using the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling method (TUNEL). TUNEL-positive cells were first observed at 5h after reperfusion and progressively increased by 24h. A higher number of TUNEL-positive cells was found in the NT group, but they were significantly decreased in the HT group. Further, DNA fragmentation was confirmed by size fractionation in agarose gel. These findings demonstrate a positive relation between the expression of Fas, caspase-3 and TUNEL-positive cells.
Tailored treatment of anastomotic leak after rectal cancer surgery according to the presence of a diverting stoma

Purpose A variety of clinical features of anastomotic leak occur during the surgical treatment of rectal cancer. However, little information regarding management of leakage is available and treatment guidelines have not been validated. The aim of this study was to evaluate the validity of currently proposed expert opinions on the management of anastomotic leak, after low anterior resection for rectal cancer. Methods A retrospective analysis was conducted for 1,786 patients who underwent sphincter-preserving surgery for rectal cancer between 2005 and 2015. Clinical outcomes including anastomotic leak-associated mortality and permanent stoma were analyzed. Results The overall incidence of anastomotic leak was 6.8% (122 of 1,786), including 6.1% (30 of 493 patients) with diverting stoma and 7.1% (92 of 1,293 patients) without diverting stoma (P = 0.505). A majority of patients without diversion were treated with diverting stoma (76 of 88 patients [86.4%]); 1 mortality (0.8%) was observed in this group. Treatments in the diversion group mainly included conservative treatment, local drainage, and/or transanal repair (26 of 30 patients [86.7%]). The anastomotic failure rates were 20.7% (19 of 92 patients) in the no diversion group and 53.3% (16 of 30 patients) in the diversion group. In the multivariate analysis, preoperative chemoradiotherapy (P < 0.001) and delayed diagnosis of anastomotic leak (P = 0.036) were independent risk factors for permanent stoma. Conclusion Management of anastomotic leak should be tailored to individual patients. When anastomotic leak occurred, preoperative chemoradiotherapy and delayed diagnosis seemed to be associated with permanent stoma.