Miracle-dz/UniFAR-FTUs huggingface dataset:
Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources capable of monitoring lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores > 93%, for both sensors. It also generalises well across lakes and winters with mIoU scores > 78% and >80% respectively. On average, our webcam approach achieves mIoU values of ≈87% and generalisation scores of ≈71% and ≈69% across different cameras and winters respectively. Additionally, we generate and make available a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras.
The tidal stream generated by the globular cluster NGC 3201

We detect a tidal stream generated by the globular cluster NGC 3201 extending over ∼140 deg on the sky, using the Gaia DR2 data, with the maximum-likelihood method we presented previously to study the M68 tidal stream. Most of the detected stream is the trailing one, which stretches in the southern Galactic hemisphere and passes within a close distance of 3.2 kpc from the Sun, therefore making the stream highly favourable for discovering relatively bright member stars, while the leading arm is further from us and behind a disc foreground that is harder to separate from. The cluster has just crossed the Galactic disc and is now in the northern Galactic hemisphere, moderately obscured by dust, and the part of the trailing tail closest to the cluster is highly obscured behind the plane. We obtain a best-fitting model of the stream which is consistent with the measured proper motion, radial velocity, and distance to NGC 3201, and show it to be the same as the previously detected Gjöll stream by Ibata et al. We identify ∼200 stars with the highest likelihood of being stream members using only their Gaia kinematic data. Most of these stars (170) are photometrically consistent with being members of NGC 3201 when they are compared to the cluster H–R diagram, only once a correction for dust absorption and reddening by the Galaxy is applied. The remaining stars are consistent with being random foreground objects according to simulated data sets. We list these 170 highly likely stream member stars.
A Simple but Efficient Concept of Blended Teaching of Mathematics for Engineering Students during the COVID-19 Pandemic

In this article we present a case study concerning a simple but efficient technical and logistic concept for the realization of blended teaching of mathematics and its applications in theoretical mechanics that was conceived, tested and implemented at the Department of Civil, Environmental and Mechanical Engineering (DICAM) of the University of Trento, Italy, during the COVID-19 pandemic. The concept foresees traditional blackboard lectures with a reduced number of students physically present in the lecture hall, while the same lectures are simultaneously made available to the remaining students, who cannot be present, via high-quality low-bandwidth online streaming. The case study presented in this paper was implemented in a single University Department and was carried out with a total of n=1011 students and n=68 professors participating in the study. Based on our first key assumption that traditional blackboard lectures, including the gestures and the facial expressions of the professor, are even nowadays still a very efficient and highly appreciated means of teaching mathematics at the university, this paper deliberately does not want to propose a novel pedagogical concept of how to teach mathematics at the undergraduate level, but rather presents a technical concept of how to preserve the quality of traditional blackboard lectures even during the COVID-19 pandemic and how to make them available to the students at home via online streaming with adequate audio and video quality even at low internet bandwidth. The second key assumption of this paper is that the teaching of mathematics is a dynamic creative process that requires the physical presence of students in the lecture hall as audience so that the professor can instantaneously fine-tune the evolution of the lecture according to his/her perception of the level of attention and the facial expressions of the students. The third key assumption of this paper is that students need to have the possibility to interact with each other personally, especially in the first years at the university. We report on the necessary hardware, software and logistics, as well as on the perception of the proposed blended lectures by undergraduate students from civil and environmental engineering at the University of Trento, Italy, compared to traditional lectures and also compared to the pure online lectures that were needed as emergency measure at the beginning of the COVID-19 pandemic. The evaluation of the concept was carried out with the aid of quantitative internet bandwidth measurements, direct comparison of transmitted video signals and a careful analysis of ex ante and ex post online questionnaires sent to students and professors.
On analytic interpolation with non-classical constraints for solving problems in robust control

In this work we consider robust stabilization of uncertain dynamical systems and show that this can be achieved by solving a non-classically constrained analytic interpolation problem. In particular, this non-classical constraint confines the range of the interpolant, when evaluated on the imaginary axis, to a frequency-dependent set. By considering a sufficient condition for when this interpolation problem has a solution, we derive an approximate solution algorithm that can also be used for controller synthesis. The conservativeness of the method is reduced by introducing a shift, which can be tuned by the user. Finally, the theory is illustrated on a numerical example with a plant with uncertain gain, phase, and output delay.
Spiking Neural Networks—Part II: Detecting Spatio-Temporal Patterns

Inspired by the operation of biological brains, Spiking Neural Networks (SNNs) have the unique ability to detect information encoded in spatio-temporal patterns of spiking signals. Examples of data types requiring spatio-temporal processing include logs of time stamps, e.g., of tweets, and outputs of neural prostheses and neuromorphic sensors. In this letter, the second of a series of three review papers on SNNs, we first review models and training algorithms for the dominant approach that considers SNNs as a Recurrent Neural Network (RNN) and adapt learning rules based on backpropagation through time to the requirements of SNNs. In order to tackle the non-differentiability of the spiking mechanism, state-of-the-art solutions use surrogate gradients that approximate the threshold activation function with a differentiable function. Then, we describe an alternative approach that relies on probabilistic models for spiking neurons, allowing the derivation of local learning rules via stochastic estimates of the gradient. Finally, experiments are provided for neuromorphic data sets, yielding insights on accuracy and convergence under different SNN models.
Unifying Matrix Data Structures: Simplifying and Speeding up Iterative Algorithms

Many algorithms use data structures that maintain properties of matrices undergoing some changes. The applications are wide-ranging and include for example matchings, shortest paths, linear programming, semi-definite programming, convex hull and volume computation. Given the wide range of applications, the exact property these data structures must maintain varies from one application to another, forcing algorithm designers to invent them from scratch or modify existing ones. Thus it is not surprising that these data structures and their proofs are usually tailor-made for their specific application and that maintaining more complicated properties results in more complicated proofs. In this paper we present a unifying framework that captures a wide range of these data structures. The simplicity of this framework allows us to give short proofs for many existing data structures regardless of how complicated the to be maintained property is. We also show how the framework can be used to speed up existing iterative algorithms, such as the simplex algorithm. More formally, consider any rational function $f(A_1,.,A_d)$ with input matrices $A_1,. ,A_d$. We show that the task of maintaining $f(A_1,.,A_d)$ under updates to $A_1,. ,A_d$ can be reduced to the much simpler problem of maintaining some matrix inverse $M^{-1}$ under updates to $M$. The latter is a well studied problem called dynamic matrix inverse. By applying our reduction and using known algorithms for dynamic matrix inverse we can obtain fast data structures and iterative algorithms for much more general problems.
Fast Interleaved Bidirectional Sequence Generation

Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming independence between neighbouring tokens (semi-autoregressive decoding, SA), we take inspiration from bidirectional sequence generation and introduce a decoder that generates target words from the left-to-right and right-to-left directions simultaneously. We show that we can easily convert a standard architecture for unidirectional decoding into a bidirectional decoder by simply interleaving the two directions and adapting the word positions and selfattention masks. Our interleaved bidirectional decoder (IBDecoder) retains the model simplicity and training efficiency of the standard Transformer, and on five machine translation tasks and two document summarization tasks, achieves a decoding speedup of ~2x compared to autoregressive decoding with comparable quality. Notably, it outperforms left-to-right SA because the independence assumptions in IBDecoder are more felicitous. To achieve even higher speedups, we explore hybrid models where we either simultaneously predict multiple neighbouring tokens per direction, or perform multi-directional decoding by partitioning the target sequence. These methods achieve speedups to 4x–11x across different tasks at the cost of <1 BLEU or <0.5 ROUGE (on average)
Singularities in the weak turbulence regime for the quintic Schrödinger equation

In this paper, we discuss the problem of derivation of kinetic equations from the theory of weak turbulence for the quintic Schrodinger equation. We study the quintic Schrodinger equation on LT, with L 1 and with a non-linearity of size e 1. We consider the correlations f (T) of the Fourier coefficients of the solution at times t = T e −2 when e → 0 and L → ∞. Our results can be summed up in the following way : there exists a regime for e and L such that for T dyadic, f (T) has the form expected from the physics literature, but such that f has an infinite number of discontinuity points.
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction

Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is often portrayed as passive and powerless (“_She daydreams about being a doctor_”) while a man is portrayed as more proactive and powerful (“_He pursues his dream of being a doctor_”). We formulate **Controllable Debiasing**, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals. We then introduce PowerTransformer as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates. One key challenge of our task is the lack of parallel corpora. To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models. Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks. Furthermore, we demonstrate the use of PowerTransformer as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.
Transporter Networks: Rearranging the Visual World for Robotic Manipulation

Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass an object, part of an object, or end effector. In this work, we propose the Transporter Network, a simple model architecture that rearranges deep features to infer spatial displacements from visual input - which can parameterize robot actions. It makes no assumptions of objectness (e.g. canonical poses, models, or keypoints), it exploits spatial symmetries, and is orders of magnitude more sample efficient than our benchmarked alternatives in learning vision-based manipulation tasks: from stacking a pyramid of blocks, to assembling kits with unseen objects; from manipulating deformable ropes, to pushing piles of small objects with closed-loop feedback. Our method can represent complex multi-modal policy distributions and generalizes to multi-step sequential tasks, as well as 6DoF pick-and-place. Experiments on 10 simulated tasks show that it learns faster and generalizes better than a variety of end-to-end baselines, including policies that use ground-truth object poses. We validate our methods with hardware in the real world. Experiment videos and code will be made available at this https URL
Local Granger causality.

Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the local Granger causality, i.e., the profile of the information transferred from the driver to the target process at each discrete time point; in this frame, GC is the average of its local version. We show that the variability of the local GC around its mean relates to the interplay between driver and innovation (autoregressive noise) processes, and it may reveal transient instances of information transfer not detectable from its average values. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear complex systems studied in the Gaussian approximation.
Extension of the Hauser-Feshbach fission fragment decay model to multichance fission

The Hauser-Feshbach fission fragment decay model, $\mathtt{HF^3D}$, which calculates the statistical decay of fission fragments, has been expanded to include multi-chance fission, up to neutron incident energies of 20 MeV. The deterministic decay takes as input pre-scission quantities - fission probabilities and the average energy causing fission - and post-scission quantities - yields in mass, charge, total kinetic energy, spin, and parity. From these fission fragment initial conditions, the full decay is followed through both prompt and delayed particle emissions, allowing for the calculation of prompt neutron and $\gamma$ properties, such as multiplicity and energy distributions, both independent and cumulative fission yields, and delayed neutron observables. In this work, we describe the implementation of multi-chance fission into the $\mathtt{HF^3D}$ model, and show an example of prompt and delayed quantities beyond first-chance fission, using the example of neutron-induced fission on $^{235}$U. This expansion represents significant progress in consistently modeling the emission of prompt and delayed particles from fissile systems.
Know Where To Drop Your Weights: Towards Faster Uncertainty Estimation

Estimating epistemic uncertainty of models used in low-latency applications and Out-Of-Distribution samples detection is a challenge due to the computationally demanding nature of uncertainty estimation techniques. Estimating model uncertainty using approximation techniques like Monte Carlo Dropout (MCD), DropConnect (MCDC) requires a large number of forward passes through the network, rendering them inapt for low-latency applications. We propose Select-DC which uses a subset of layers in a neural network to model epistemic uncertainty with MCDC. Through our experiments, we show a significant reduction in the GFLOPS required to model uncertainty, compared to Monte Carlo DropConnect, with marginal trade-off in performance. We perform a suite of experiments on CIFAR 10, CIFAR 100, and SVHN datasets with ResNet and VGG models. We further show how applying DropConnect to various layers in the network with different drop probabilities affects the networks performance and the entropy of the predictive distribution.
Refining and defining riverscape genetics: How rivers influence population genetic structure

Traditional analysis in population genetics evaluates differences among groups of individuals and, in some cases, considers the effects of distance or potential barriers to gene flow. Genetic variation of organisms in complex landscapes, seascapes, or riverine systems, however, may be shaped by many forces. Recent research has linked habitat heterogeneity and landscape or seascape configuration to genetic structure by integrating methods from landscape ecology, population genetics, and spatial statistics in approaches known as landscape or seascape genetics. However, functional differences between terrestrial or seascapes systems in comparison to riverscape topography (i.e., movement pathways for aquatic obligate species are constrained to river channels) make translating these approaches into freshwater analyses problematic. Studies that may be described as riverscape genetics (RG) have linked temperature, stream gradient, and confluences to genetic variability. Lack of consistency in methodology, however, has made comparisons across species and scales difficult. We provide a perspective on how RG could be used to provide a more comprehensive conceptual and applied understanding of connectivity and dispersal in freshwater systems. We describe four thematic areas of study representing current and future research opportunities and describe a basic workflow for conducting RG analysis. Although numerous methodological challenges remain, a RG approach can enhance our understanding of habitat heterogeneity in shaping gene flow and spatial genetic structure. These characteristics of populations are critical components for interpreting demographic and evolutionary consequences of habitat loss and fragmentation. WIREs Water 2018, 5:e1269. doi: 10.1002/wat2.1269
Synthesis and Molecular Modeling Studies of Bicyclic Inhibitors of Dihydrofolate Reductase, Receptor Tyrosine Kinases and Tubulin

Dedication iv vii Acknowledgements viii List of Tables x List of Figures xii List of Schemes xx List of Abbreviations xxiii A. Biochemical review 1 B. Chemical review 84 C. Statement of the problem 100 D. Chemical discussion 147 E. Summary 252 F. Experimental 262 G. Bibliography 294 H.1. Appendix-1 327 H.2. Appendix-2 347
The Efficiency Gap

Parameter estimation via M- and Z-estimation is broadly considered to be equally powerful in semiparametric models for one-dimensional functionals. This is due to the fact that, under sufficient regularity conditions, there is a one-to-one relation between the corresponding objective functions - strictly consistent loss functions and oriented strict identification functions - via integration and differentiation. When dealing with multivariate functionals such as multiple moments, quantiles, or the pair (Value at Risk, Expected Shortfall), this one-to-one relation fails due to integrability conditions: Not every identification function possesses an antiderivative. The most important implication of this failure is an efficiency gap: The most efficient Z-estimator often outperforms the most efficient M-estimator, implying that the semiparametric efficiency bound cannot be attained by the M-estimator in these cases. We show that this phenomenon arises for pairs of quantiles at different levels and for the pair (Value at Risk, Expected Shortfall), where we illustrate the gap through extensive simulations.
The Association of Prevalence and Genotypes of Torque Teno Virus (TTV) Among Chronic Hepatitis B and C Patients in Ahvaz, 2012

Background: Torque teno virus (TTV) belongs to the Circoviridae family and was separated from Japanese patients with elevated levels of serum alaninetransferasefortheﬁrsttime. Followingthis,extensivestudieshavebeencarriedoutaroundtheworld. Objectives: Thepresentstudywasdesignedtoinvestigateanddeterminethefrequencyof TTVgenotypesinpatientswithchronichepatitisBandC inAhvazcityin2012. Patients and Methods: Samples from 30 patients with chronic hepatitis C and 30 patients with chronic hepatitis B were studied and compared with30healthysubjectsascontrols. TTvirusDNAwasinvestigatedusingsemi-nestedPCR.Thebasicprimersusedwere5’ UTR. Results: Outof the90specimenstested,ninesamples(10%)wereTTV-DNApositive. Of thehepatitisCsamples,foursamples(13.3%)werepositive. Of thehepatitisBsamples,two(6.67%)werepositiveand3(10%)of thecontrolgroupsampleswerepositive. Bothtth4andtth31isolateswereseparated. Conclusions: TTVisaDNAvirus,butitsgeneticdiversityisincreasing. ThePCRmethodsusedwerenoteﬃcientenoughandwerenotabletoidentify the types of TTV in blood. The frequency obtained from our study is inconsistent with some countries but is consistent with a study conducted in India. Ingeneral,arelationshipbetweensexandageandtheprevalenceof TTVwasnotfound,andthisresultisthesameasmostotherstudies.