Miracle-dz/UniFAR-FTUs huggingface dataset:
Regular Bernstein blocks

Abstract For a connected reductive group G defined over a non-archimedean local field F, we consider the Bernstein blocks in the category of smooth representations of G⁢(F){G(F)}. Bernstein blocks whose cuspidal support involves a regular supercuspidal representation are called regular Bernstein blocks. Most Bernstein blocks are regular when the residual characteristic of F is not too small. Under mild hypotheses on the residual characteristic, we show that the Bernstein center of a regular Bernstein block of G⁢(F){G(F)} is isomorphic to the Bernstein center of a regular depth-zero Bernstein block of G0⁢(F){G^{0}(F)}, where G0{G^{0}} is a certain twisted Levi subgroup of G. In some cases, we show that the blocks themselves are equivalent, and as a consequence we prove the ABPS Conjecture in some new cases.
A Multi-Strategy Approach to Overcoming Bias in Community Detection Evaluation

Community detection is key to understand the structure of complex networks. However, the lack of appropriate evaluation strategies for this specific task may produce biased and incorrect results that might invalidate further analyses or applications based on such networks. In this context, the main contribution of this paper is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we use multiple strategies that capture distinct aspects of the communities. The conclusion on the quality of these communities is based on the consensus among the strategies adopted for the structural evaluation, as well as on the comparison with communities detected by different methods and with their existing ground truths. In this way, our approach allows one to overcome biases in network data, detection algorithms and evaluation metrics, thus providing more consistente conclusions about the quality of the detected communities. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach.
Gradient-Consensus Method for Distributed Optimization in Directed Multi-Agent Networks

In this article, a distributed optimization problem for minimizing a sum, $\sum {_{i = 1}^n{f_i}} $, of convex objective functions, fi, on directed graph topologies is addressed. Here each function fi is a function of n variables, private to agent i which defines the agent’s objective. These fi’s are assumed to be Lipschitz-differentiable convex functions. For solving this optimization problem, we develop a novel distributed algorithm, which we term as the gradient-consensus method. The gradient-consensus scheme uses a finite-time terminated consensus protocol called ρ-consensus, which allows each local estimate to be ρ-close to each other at every iteration. The parameter ρ is a fixed constant independent of the network size and topology. It is shown that the estimate of the optimal solution at any local agent i converges geometrically to the optimal solution within an O(ρ) neighborhood, where ρ can be chosen to be arbitrarily small.
LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: Evidence from Mumbai

The satellite imagery classification task is fundamental to spatial knowledge discovery. Several image classification methods are used to create standardized Land use and Land cover (LULC) maps, which facilitate research on spatial and ecological processes and human activities. Local Climate Zones (LCZ) classification maps are an example of standardized maps which have been widely used to demarcate the homogeneity in built and natural character in the cities. The LCZ classification scheme is primarily focused on urban climate-related research, in which 17 climate zones are mapped in a city area with the 100-150m spatial resolution. Each zone exhibits physical properties related to urban form and functions essential for thermal behavior studies. Extending this widely adopted approach to create LULC maps at finer resolution using the LCZ mapping scheme would benefit the allied domains of urban planning, transportation, and water resources management. This study proposes a novel solution to generate classification maps with a 10-band Sentinel-2B dataset and Convolutional Neural Networks (CNN) at the 10m spatial resolution. The classification benefits from CNNs property to preserve local structures in the image datasets. The proposed CNN model outperforms traditional machine learning models such as Artificial Neural Network, Random Forests, and Support Vector Machines. The overall accuracy and kappa statistic of the CNN model trained on 14 urban and natural classes are 82 percent and 0.81, respectively. The study also discusses the utility of the model for specialized remote sensing tasks such as change detection, identification of slum settlements, and mapping pervious/impervious layers in urban settlements with higher accuracy.
Meaningful Causal Decompositions in Health Equity Research

Supplemental Digital Content is available in the text. Causal decomposition analyses can help build the evidence base for interventions that address health disparities (inequities). They ask how disparities in outcomes may change under hypothetical intervention. Through study design and assumptions, they can rule out alternate explanations such as confounding, selection bias, and measurement error, thereby identifying potential targets for intervention. Unfortunately, the literature on causal decomposition analysis and related methods have largely ignored equity concerns that actual interventionists would respect, limiting their relevance and practical value. This article addresses these concerns by explicitly considering what covariates the outcome disparity and hypothetical intervention adjust for (so-called allowable covariates) and the equity value judgments these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. From this discussion, we generalize decomposition estimands and formulae to incorporate allowable covariate sets (and thereby reflect equity choices) while still allowing for adjustment of non-allowable covariates needed to satisfy causal assumptions. For these general formulae, we provide weighting-based estimators based on adaptations of ratio-of-mediator-probability and inverse-odds-ratio weighting. We discuss when these estimators reduce to already used estimators under certain equity value judgments, and a novel adaptation under other judgments.
Predicting consumers engagement on Facebook based on what and how companies write

Engaged customers are a very import part of current social media marketing. Public figures and brands have to be very careful about what they post online. That is why the need for accurate strategies for anticipating the impact of a post written for an online audience is critical to any public brand. Therefore, in this paper, we propose a method to predict the impact of a given post by accounting for the content, style, and behavioral attributes as well as metadata information. For validating our method we collected Facebook posts from 10 public pages, we performed experiments with almost 14000 posts and found that the content and the behavioral attributes from posts provide relevant information to our prediction model.
The Giant magnetocaloric effect and magnetic field induced magneto-structural phase transition in A2-disordered Co2FeAl Heusler nanoalloy

We have synthesized A2 disordered Heusler nanoalloy and observed giant magnetocaloric effect during the magnetic field induced magneto structural phase transition (MST). The magnetic entropy change shows a positive anomaly at 1252 K. The entropy change is increased linearly with magnetic field and a large value of ~15 J/Kg is detected under a moderate field of 1.4 T near the magneto-structural phase transition temperature (Tt). It leads to a net relative cooling power of 89 J/Kg for the change of magnetic field of 1.4 T. The Arrott plot explores the essential information of the nature of MST. This giant magnetocaloric effect is anticipated to be incorporated in magnetic refrigeration that will not only be an alternative of energy saving but also eco-friendly.
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning

The majority of ML research concerns slow, statistical learning of i.i. d. samples from large, labelled datasets. Animals do not learn this way. An enviable characteristic of animal learning is 'episodic' learning - the ability to rapidly memorize a specific experience as a composition of existing concepts, without provided labels. The new knowledge can then be used to distinguish between similar experiences, to generalize between classes, and to selectively consolidate to long-term memory. The Hippocampus is known to be vital to these abilities. AHA is a biologically-plausible computational model of the Hippocampus. Unlike most machine learning models, AHA is trained without any external labels and uses only local and immediate credit assignment. We demonstrate AHA in a superset of the Omniglot classification benchmark. The extended benchmark covers a wider range of known Hippocampal functions by testing pattern separation, completion, and reconstruction of original input. These functions are all performed within a single configuration of the computational model. Despite these constraints, results are comparable to state-of-the-art deep convolutional ANNs. In addition to the demonstrated high degree of functional overlap with the Hippocampal region, AHA is remarkably aligned to current macro-scale biological models and uses biologically plausible micro-scale learning rules.
Measurements of inclusive jet spectra in 
pp
 and central Pb-Pb collisions at 
sNN=5.02TeV

This article reports measurements of the pT-differential inclusive jet cross-section in pp collisions at √ s = 5.02 TeV and the pT-differential inclusive jet yield in Pb–Pb 0–10% central collisions at √ sNN = 5.02 TeV. Jets were reconstructed at mid-rapidity with the ALICE tracking detectors and electromagnetic calorimeter using the anti-kT algorithm. For pp collisions, we report jet crosssections for jet resolution parameters R = 0.1−0.6 over the range 20 < pT,jet < 140 GeV/c, as well as the jet cross-section ratios of different R, and comparisons to two next-to-leading-order (NLO)based theoretical predictions. For Pb–Pb collisions, we report the R = 0.2 and R = 0.4 jet spectra for 40 < pT,jet < 140 GeV/c and 60 < pT,jet < 140 GeV/c, respectively. The scaled ratio of jet yields observed in Pb–Pb to pp collisions, RAA, is constructed, and exhibits strong jet quenching and a clear pT-dependence for R = 0.2. No significant R-dependence of the jet RAA is observed within the uncertainties of the measurement. These results are compared to several theoretical predictions. ∗See Appendix A for the list of collaboration members ar X iv :1 90 9. 09 71 8v 1 [ nu cl -e x] 2 0 Se p 20 19 Measurements of jet spectra in pp and Pb–Pb collisions at √ sNN = 5.02 TeV ALICE Collaboration
Deep Adaptive Input Normalization for Time Series Forecasting

Deep learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appropriately normalized. This issue is even more apparent when DL is used for financial time series forecasting tasks, where the nonstationary and multimodal nature of the data pose significant challenges and severely affect the performance of DL models. In this brief, a simple, yet effective, neural layer that is capable of adaptively normalizing the input time series, while taking into account the distribution of the data, is proposed. The proposed layer is trained in an end-to-end fashion using backpropagation and leads to significant performance improvements compared to other evaluated normalization schemes. The proposed method differs from traditional normalization methods since it learns how to perform normalization for a given task instead of using a fixed normalization scheme. At the same time, it can be directly applied to any new time series without requiring retraining. The effectiveness of the proposed method is demonstrated using a large-scale limit order book data set, as well as a load forecasting data set.
First Detection of Submillimeter-wave [13C i] 3P1–3P0 Emission in a Gaseous Debris Disk of 49 Ceti with ALMA

We have detected the submillimeter-wave fine-structure transition (3P1–3P0) of 13C, [13C i], in the gaseous debris disk of 49 Ceti with the Atacama Large Millimeter/submillimeter Array (ALMA). Recently, the [C i] 3P1–3P0 emission has been spatially resolved in this source with ALMA. In this data set, the F = 3/2–1/2 hyperfine component of [13C i], which is blueshifted by 2.2 km s−1 from the normal species line, [C i], has been identified in the outer part of the 49 Ceti disk, thanks to the narrow velocity widths of the gas components. The [C i]/[13C i] line intensity ratio is found to be 12 ± 3, which is significantly lower than the 12C/13C abundance ratio of 77 in the interstellar medium. This result clearly reveals that the [C i] emission is optically thick in 49 Ceti at least partly, as speculated by the previous work. As far as we know, this is the first detection of [13C i] 3P1–3P0 emission at 492 GHz not only in debris disks but also in the interstellar medium.
RILOD: near real-time incremental learning for object detection at the edge

Object detection models shipped with camera-equipped edge devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, RILOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of RILOD is a novel incremental learning algorithm that trains end-to-end for one-stage deep object detection models only using training data of new object classes. Specifically to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented RILOD under both edge-cloud and edge-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.
Teach like a champion: 49 techniques that put students on the path to college / Doug Lemov

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Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration

Age related macular degeneration is a major cause of blindness and visual impairment in older adults. Its exudative form, where fluids leak into the macula, is especially damaging. The standard treatment involves injections of anti-VEGF (vascular endothelial growth factor) agents into the eye, which prevent further vascular growth and leakage, and can restore vision. These intravitreal injections have a risk of devastating complications including blindness from infection and are expensive. Optimizing the interval between injections in a patient specific manner is of great interest, as the retinal response is partially patientspecific. In this paper we propose a machine learning approach to predict the retinal response at the end of a standardized 12-week induction phase of the treatment. From a longitudinal series of optical coherence tomography (OCT) images, a number of quantitative measurements are extracted, describing the underlying retinal structure and pathology and its response to initial treatment. After initial feature selection, the selected set of features is used to predict the treatment response status at the end of the induction phase using the support vector machine classifier. On a population of 30 patients, leave-one-out cross-validation showed the classification success rate of 87% of predicting whether the subject will show a response to the treatment at the next visit. The proposed methodology is a promising step towards the much needed image-guided prediction of patient-specific treatment response.
May's Instability in Large Economies

Will a large economy be stable? Building on Robert May's original argument for large ecosystems, we conjecture that evolutionary and behavioural forces conspire to drive the economy towards marginal stability. We study networks of firms in which inputs for production are not easily substitutable, as in several real-world supply chains. Relying on results from random matrix theory, we argue that such networks generically become dysfunctional when their size increases, when the heterogeneity between firms becomes too strong, or when substitutability of their production inputs is reduced. At marginal stability and for large heterogeneities, we find that the distribution of firm sizes develops a power-law tail, as observed empirically. Crises can be triggered by small idiosyncratic shocks, which lead to "avalanches" of defaults characterized by a power-law distribution of total output losses. This scenario would naturally explain the well-known "small shocks, large business cycles" puzzle, as anticipated long ago by Bak, Chen, Scheinkman, and Woodford.
Querying Zero-streaming Cameras

Low-cost cameras grow rapidly, producing colossal videos that enable powerful analytics but also stress network and compute resources. An unexploited opportunity is that most of the videos remain "cold" without ever being queried. For resource efficiency, we advocate for these cameras to be zero-streaming: they capture videos directly to their cheap local storage and only communicate with the cloud when analytics is requested. To this end, we present a system that spans the cloud and cameras. Our key ideas are twofold. When capturing video frames, a camera learns accurate knowledge on a sparse sample of frames, rather than learning inaccurate knowledge on all frames; in executing one query, a camera processes frames in multiple passes with multiple operators trained and picked by the cloud during the query, rather than one pass processing with operator(s) decided ahead of the query. On diverse queries over 15 videos and with typical wireless network bandwidth and low-cost camera hardware, our system prototype runs at more than 100x video realtime. It outperforms competitive alternative designs by at least 4x and up to two orders of magnitude.
The evolution of pitch angles of spiral arms

In spiral galaxies, the pitch angle, α, of the spiral arms is often proposed as a discriminator between theories for the formation of the spiral structure. In Lin–Shu density wave theory, α stays constant in time, being simply a property of the underlying galaxy. In other theories (e.g. tidal interaction, and self-gravity), it is expected that the arms wind up in time, so that to a first approximation $\cot \alpha \propto t$. For these theories, it would be expected that a sample of galaxies observed at random times should show a uniform distribution of $\cot \alpha$. We show that a recent set of measurements of spiral pitch angles (Yu & Ho) is broadly consistent with this expectation.
Instanton Bundles on the Blowup of the Projective 3 -Space at a Point

We propose a general definition of mathematical instanton bundle with given charge on any Fano threefold extending the classical definitions on $\mathbb P^3$ and on Fano threefold with cyclic Picard group. Then we deal with the case of the blow up of $\mathbb P^3$ at a point, giving an explicit construction of instanton bundles satisfying some important extra properties: moreover, we also show that they correspond to smooth points of a component of the moduli space.
FROM POSTASPIRATION TO AFFRICATION: NEW PHONETIC CONTEXTS IN WESTERN ANDALUSIAN SPANISH

Western Andalusian Spanish is one of the many coda /s/ weakening varieties of Spanish in the world. It is documented that there is a growing tendency for speakers of this variety to realize /st/ clusters not only with postaspiration (pasta [pát̪ h a]) rather than preaspiration (pasta [páht̪a]), but also with affrication (pasta [pát̪ s a]). In this study, we present evidence of postaspiration and affrication beyond /st/ clusters, i.e., also in stop + /t/ clusters, as detected auditorily in recorded background interviews with Western Andalusian Spanish speakers. Further acoustic analyses of duration, zero crossing rate, and center of gravity of the friction segment in the tokens corroborated the existence of postaspiration and affrication in both contexts. However, while postaspirated tokens were similar in all parameters for both /st/ and /Ct/ clusters, affricated tokens presented higher frequency and concentration of energy in /Ct/ clusters. Further research into these emerging phenomena seems to be needed.
THE SANITARY QUALITY OF TIFTON 85 BERMUDAGRASS AND WRANGLER GRASS HAY STORED UNDER DIFFERENT ENVIRONMENTAL

This study evaluated the variations in temperature and dry matter content in Tifton 85 bermudagrass and Wrangler grass hay stored under different environmental conditions. The fungal and mycotoxin populations in the hay production area were also evaluated. Fifty samples (30 hay bales, ten samples of organic matter [OM], and ten samples of grass [G] before baling) were used in a completely randomized design and factorial scheme with ten treatments (a combination of two classes of G: Tifton 85 bermudagrass and Wrangler grass) and three storage systems: a covered shed without side walls, covered with a clear tarp; an open environment covered with a double-sided tarp; and a ventilated closed shed. Four additional treatments, five replications, and one sample per experimental unit completed the study design. The environment influenced the temperature of the Tifton 85 bermudagrass and Wrangler grass bales, and the dry matter content of the bales during the evaluated period was above the recommended limit (800 g kg). The genus Penicillium occurred with the highest frequency in the Wrangler grass stored under a double-sided tarp (6.13 log CFU g). In the additional treatment, the OM deposited in the soil of the Wrangler grass production area exhibited a greater occurrence of the fungus Fusarium (4.66 log CFU g) and of the mycotoxin zearalenone (ZEA; 594.2 μg kg). The hay stored in the shed with a clear tarp exhibited more aflatoxin (AFLA) and deoxynivalenol (DON). The concentration of mycotoxins varied across the storage environments and between the stored species; however, the shed storage system used in hay production might also be a source of hay contamination via the incorporation of decomposing OM with mycotoxins in the hay.