Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for the IMS Regional Kinematic Calibration

Abstract : Among the problems related to CTBT a very important role belongs to the calibration of the regional travel times (regional kinematic calibration). The success in solving this task would significantly improve the accuracy of the weak seismic event location by the system. The methods that are applied to perform this kind of calibration are assumed in the present paper. All these methods can be broken into two principal groups: methods for direct calibration and those utilizing the velocity model of the media that is to be developed in an intermediate stage. Direct methods are used to construct the time correction surfaces making use of previously learned seismological experience (regional travel time tables) or directly employing the experimental data. The first approach represents the try to perform the task through the linear combination of regional travel time tables (godographs) and their errors. Another technique is a kind of spatial interpolation of the regional observations for given station. Indirect methods solve the calibration problem in two steps. Initially the 3-D velocity model of the media is being produced by the inversion of the observations. Afterwards the field of travel times is generated on the base of the velocity model. Methods inside the group differ depending upon what way is chosen for the reverse kinematic problem solution. Currently the velocity models are being built by "try-and-drop" technique or by tomographic inversion. The tomographic P-models developed till now do not possess the horizontal resolution enough to be successfully applied in a regional scale. The main reason of poor lateral resolution for the uppermost mantle comes from low amount and uneven distribution of regional P-wave observations used to produce the global tomographic models. The reliability of 3-D model constructed by "try-and-drop" method is unknown but its using is still reasonable unless enough data for the area to be calibrated have been collected.
Convergent evolution in silico reveals shape and dynamic principles of directed locomotion

Active, directed locomotion on the ground is present in many phylogenetically distant species. Bilateral symmetry and modularity of the body are common traits often associated with improved directed locomotion. Nevertheless, both features result from natural selection, which is contingent (history-dependent) and multifactorial (several factors interact simultaneously). Based solely on the unique natural history on Earth, it is difficult to conclude that bilateral symmetry and modularity of the body are required traits for an improved locomotion ability as they can result from chance or be related to other body functions. As a way to avoid these caveats, we propose using physics-based simulations of 3D voxel-based soft robots evolved under different evolutionary scenarios to test the necessity of both traits for efficient directed locomotion on the ground. We found that an intermediate number of body modules (appendages) and high body symmetry are evolutionarily selected regardless of gravitational environments, robot sizes, and genotype encoding. Therefore, we conclude that both traits are strong candidates for universal principles related to efficient directed locomotion.
Golgi membrane proteins YIPF3 and YIPF4 regulate turnover of the Golgi apparatus through autophagy

The degradation of organelles by autophagy is essential for cellular homeostasis. The Golgi apparatus has recently been demonstrated to be degraded by autophagy, but little is known about how the Golgi is recognized by the autophagosome. Using quantitative proteomic analysis and two Golgiphagy-reporter systems that we developed, we found that five-transmembrane Golgi-resident proteins, YIPF3 and YIPF4, constitute a Golgiphagy receptor. The YIPF3–YIPF4 complex interacts with LC3B, GABARAP, and GABARAPL1 via the LIR motif in YIPF3, whose stability is dependent on YIPF4. Phosphorylation of the LIR in YIPF3 seems to be required for YIPF3–ATG8 interaction. Moreover, expression of the YIPF3 LIR mutant caused an elongated Golgi morphology, indicating the importance of Golgi turnover via selective autophagy. The reporter assays that we established will pave the way for future studies to obtain deeper insights into Golgiphagy.
DYSON’S RANK FOR

Let Q ( n ) denote the number of partitions of n into distinct parts. We show that Dyson’s rank provides a combinatorial interpretation of the well-known fact that Q ( n ) is almost always divisible by 4. This interpretation gives rise to a new false theta function identity that reveals surprising analytic properties of one of Ramanujan’s mock theta functions, which in turn gives generating functions for values of certain Dirichlet L -functions at nonpositive integers.
SAPED: System of Assistance for PEople with Disabilities

The use of powered wheelchairs with high navigational intelligence is one of the great steps towards the integration of severely physically disabled and mentally handicapped people. Driving a wheelchair is a difficult task even for a normal person and it becomes much more difficult for people with disabilities. This paper describes the use of the case-based reasoning (cbr) paradigm in this field. The objective is to discharge the user from control constraints by assisting him in the reproduction of routes frequently employed. The assistance task is based on two important steps. The first one consists in recognizing the followed route based on a similarity computation with one already carried out. In the second, future directions of the wheelchair are predicted in order to reduce the number of commands which the user has to define. An automatic learning of routes is proposed allowing the adaptation of the wheelchair behavior in different environments.
Increasing effect sizes of pairwise conditional independence tests between random vectors

A simple approach to test for conditional independence of two random vectors given a third random vector is to simultaneously test for conditional in-dependence of every pair of components of the two random vectors given the third random vector. In this work, we show that conditioning on additional components of the two random vectors that are independent given the third one increases the tests’ effect sizes while leaving the validity of the over-all approach unchanged. We leverage this result to derive a practical pairwise testing algorithm that ﬁrst chooses tests with a relatively large effect size and then does the actual testing. We show both numerically and theoretically that our algorithm outperforms standard pairwise independence testing and other existing methods if the dependence within the two random vectors is sufﬁciently high.
Exploring deep learning for intelligent image retrieval

In this chapter, we further explore cross-modal retrieval to address the challenges posited by the heterogeneity gap and the semantic gap. To be speciﬁc, we propose integrating Shannon information theory and adversarial learning. In terms of the heterogeneity gap, we integrate modality classiﬁcation and information entropy maximization adversarially. For this purpose, a modality classiﬁer (as a discriminator) is built to distinguish the text and image modalities according to their diﬀerent statistical properties. This discriminator uses its output probabilities to compute Shannon information entropy, which measures the uncertainty of the modality classiﬁcation it performs. Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy. Thus, maximizing information entropy gradually reduces the distribution discrepancy of cross-modal features, thereby achieving a domain confusion state where the discriminator cannot classify two modalities conﬁdently. To reduce the semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss are used to associate the intra-and inter-modality similarity between features in the shared space. Furthermore, a regularization term based on KL-divergence with temperature scaling is used to calibrate the biased label classi-ﬁer caused by the data imbalance issue.
The Catalog Problem: Clustering and Ordering Variable-Sized Sets

Prediction of a varying number of ordered clusters from sets of any cardinality is a challenging task for neural networks, combining elements of set representation, clustering and learning to order. This task arises in many diverse areas, ranging from medical triage and early discharge, through machine part management and multi-channel signal analysis for petroleum exploration to product catalog structure prediction. This paper focuses on that last area, which exemplifies a number of challenges inherent to adaptive ordered clustering, referred to further as the eponymous Cat-alog Problem . These include learning variable cluster constraints, exhibiting relational reasoning and managing combinatorial complexity. Despite progress in both neural clustering and set-to-sequence methods, no joint, fully differentiable model exists to-date. We develop such a modular architecture, referred to further as Neural Ordered Clusters (NOC), enhance it with a specific mechanism for learning cluster-level cardinality constraints, and provide a robust comparison of its performance in relation to alternative models. We test our method on three datasets, including synthetic catalog structures and PROCAT, a dataset of real-world catalogs consisting of over 1 . 5 M products, achieving state-of-the-art results on a new, more challenging formulation of the underlying problem, which has not been addressed before. Additionally, we examine the network’s ability to learn higher-order interactions.
Stroke and Alzheimer’s Disease

Stroke primarily affects the population over the age of 65 years, each year 795,000 people will experience new or recurrent stroke. It the most common cause of severe long-term disability (AHA 2010) in the US. Alzheimer’s disease (AD) is the most common form of dementia in the elderly. The prevalence of Alzheimer’s disease is known to increase as the population ages. Stroke and AD can occur together and result in deficits that appear to multiply. They share common vascular risk factors such as Hypertension, abdominal obesity and physical inactivity. It is difficult to differentiate the cognitive deficits that result from stroke and from Alzheimer’s disease individually and prognosticate function. The dysfunction and mismatch between the needs of the neural tissue to the mechanisms that regulate cerebral blood flow (CBF) have been postulated to result in cognitive deficits in both these disease processes. The neurovascular pathways are believed to be common to both the diseases and elucidate the potentiating effects that these two entities have on each other. This paper attempts to provide an understanding of the neurological pathways that may be common to the disease processes and pathology. Understanding of the mechanism of AD and cognitive deficits following stroke will help establish guideline and strategies in the prevention of these two diseases with similar and common neurological pathways.
Elektrodiagnostyka i diagnostyka obrazowa serca w Laboratorium Biocybernetyki

: This article deals with current issues in electrodiagnostics and image diagnostics of the heart and vessels. All research takes place in the Biocybernetics Laboratory of AGH University of Science and Technology. In the article, an idea of a portable ECG recorder, which auto adaptively changes ECG interpretation software, is being presented. This group of systems is extended by hybrid systems for supervision and control. They consist of personal and stationary parts, which are responsible respectively for signal acquisition and patient monitoring, as well as identifica-tion of potentially threatening conditions. In the field of medical imaging, three domains are presented, for which image segmentation algorithms are proposed: a myocardium, an intravascular implants and carotid arteries. These methods are mostly based on the deformable models: active contour and level-set methods.
Community Aware Recommendation System with Explicit and Implicit Link Prediction

: Recommendation systems are essential tools that help users discover content they may be interested in, amidst the vast amount of information available online. However, current methods, such as using historical user-item interactions and collaborative filtering, have limitations in accurately predicting user preferences. Our research aims to address these challenges and improve the performance of recommendation systems. In this article
Comparison of Nadir and Off-Nadir Multispectral Response Patterns for Six Tallgrass Prairie Treatments in Eastern Kansas

Ground-level spectroradiometer measurements taken at nadir and 45° off-nadir from four sensor view azimuths were used to examine the spectral reflectance patterns of six tallgrass prairie treatments in eastern Kansas. The six treatments included native prairie, burned, mowed, hayed, grazed, and unmanaged grasslands. Spectral reflectance patterns and biophysical measurements were made for each treatment. A red band (band center = 683.8 nm), a near-infrared (NIR) band (band center = 816.5 nm), and the NDVI were used in multivariate analysis of variance and analysis of variance to test for differences in spectral response among treatments. Correlation analysis was used to test for relationships between the spectral variables and plant cover and canopy height. The native and unmanaged treatments were spectrally unique from the other four grassland treatments. View zenith and azimuth had no effect on the spectral separability among treatments. Across all treatments, only the off-nadir measurements taken at a 0° view azimuth (sun behind sensor) were significantly different from those taken at other view zenith-azimuth angles.
Non-native plant species in mountainous areas: A case study of Linaria dalmatica in the Greater Yellowstone Ecosystem

Non-native plant species are increasingly being viewed as a threat to sub-alpine and alpine ecosystems, and the need to determine the causes and mechanisms of nonnative plant invasions in mountain systems has been recognized. This study was initiated to provide: 1) useful information for managers in the local area and, 2) information that can be used in concert with data from similar studies to help elucidate the causes and mechanisms of non-native plant species invasions in mountain systems at the global scale. Eighteen populations a non-native species (Linaria dalmatica) and 14 populations of a closely related native species (Castilleja miniata) were surveyed along an elevation gradient in the Greater Yellowstone Ecosystem. Climate, environmental, and species demographic data were collected from each site from 2008-2011. Climate and environmental predictors varied along the elevation gradient, with stem density of the two test species being explained by different sets of predictors. Vital rates of L. dalmatica were found to be more variable and more highly associated with climate and environmental predictor variables than those of C. miniata. The population growth rate of L. dalmatica did not show any consistent trend with elevation, but did appear to be influenced by extremely cold temperatures and predictors related to the vegetative community. Probability of occurrence of L. dalmatica was related to elevation. Percent cover of L. dalmatica was not associated with elevation, instead being strongly associated with vegetative community characteristics such as percent perennial cover. The current range of L. dalmatica was broader than the majority of native species in the study area, and the non-native plant community in the study area was significantly more nested with increased elevation than the native plant community. These results suggest that L. dalmatica is a broadly adapted species. Overall, our data fail to provide conclusive evidence of climatic limits for this species, but suggest that it may be limited from upward expansion by extremely cold winter temperatures. Until conclusive evidence of climatic limitation is provided, the upper populations of this species should be monitored and managed to prevent further spread into sub-alpine/alpine environments. 1 CHAPTER 1: INTRODUCTION AND OBJECTIVES
THE FOOD PROBLEM AND THE EVOLUTION OF INTERNATIONAL INCOME LEVELS

This paper examines the effect of agricultural development on a country’s overall development and growth experience. In most poor countries, large fractions of land, labor, and other productive resources are devoted to producing food for subsistence needs. This “food problem” can delay a country’s industrial development for a long period of time, causing its per capita income to fall far behind the world leader. Once industrialization begins, this trend is reversed. The extent to which a country catches up to the leader depends primarily on factors that affect productivity in non-agricultural activities: agricultural productivity is thus largely irrelevant in the very long run. But in the short run, a country that experiences large improvements in agricultural productivity (due to, say, a Green Revolution) will experience a rapid increase in its income relative to the leaders.
Online Public Hate Speech Detection Using Machine Learning

: Globally, social media platforms have billions—not millions—of users. People's interactions on these widely accessible social media platforms, like Twitter, have a significant impact. There are undesired negative effects on modern life. One of the most extravagant social media platforms in our time, Twitter, is now used as a weapon to share unethical, excessive amounts of opinions and media. These widely used major communication platforms have evolved into a great source of disseminating unwanted data and irrelevant information. The suggested effort divides the nine sorts of insulting remarks and tweets directed at specific individuals. These types of tweets are further divided into non-shaming tweets towards persons. According to observation, the majority of people who write comments on a certain occasion and are interested in doing so are likely to change the person in question. Additionally, it is the Twitter shamer who checks the increase faster than the non-shaming devotee.
Gasoline Prices , Fuel Economy , and the Energy Paradox

It is often asserted that consumers purchasing automobiles or other goods and services underweight the costs of gasoline or other "add-ons. " We test this hypothesis in the US automobile market by examining the e¤ects of time series variation in gasoline price expectations on the prices and market shares of vehicles with di¤erent fuel economy ratings. When gas prices rise, demand for high fuel economy vehicles increases, pushing up their relative prices. Market share changes-increased production of high fuel economy vehicles and scrappage of low fuel economy vehicles-attenuate these price changes. Intuitively, the less that equilibrium vehicle prices and shares respond to changes in expected gasoline prices, the less that consumers appear to value gasoline costs. We estimate a nested logit discrete choice model using a remarkable dataset that includes market shares, characteristics, expected usage, and transaction price microdata for all new and used vehicles available between 1999 and 2008. To address simultaneity bias, we introduce a new instrument for used vehicle market shares, based on the fact that gasoline prices cause variation in new vehicle shares that then persists over time as the vehicles move through resale markets. Our results show that US auto consumers are willing to pay just $0.61 to reduce expected discounted gas expenditures by $1. We incorporate the estimated parameters into a new discrete choice approach to behavioral welfare analysis, which suggests with caution that a paternalistic energy e¢ ciency policy could generate welfare gains of $3.6 billion per year. at Manheim for sharing their expertise. We thank Carmen Collyns and Jordon Ricks for research assistance on this and related projects. We especially appreciate the meticulous research assistance of Sounman Hong.
Standing stock of Antarctic krill ( Euphausia superba Dana, 1850) (Euphausiacea) in the Southwest Atlantic sector of the Southern Ocean, 2018–19

Estimates of the distribution and density of Antarctic krill ( Euphausia superba Dana, 1850) were derived from a large-scale survey conducted during the austral summer in the Southwest Atlantic sector of the Southern Ocean and across the Scotia Sea in 2018–19, the ‘2018–19 Area 48 Survey’. Survey vessels were provided by Norway, the Association of Responsible Krill harvesting companies and Aker BioMarine AS, the United Kingdom, Ukraine, Republic of Korea, and China. Survey design followed the transects of the Commission for the Conservation of Antarctic Marine Living Resources synoptic survey, carried out in 2000 and from regular national surveys performed in the South Atlantic sector by the U.S., China, Republic of Korea, Norway, and the U.K. The 2018–19 Area 48 Survey represents only the second large-scale survey performed in the area and this joint effort resulted in the largest ever total transect line (19,500 km) coverage carried out as one single exercise in the Southern Ocean. We delineated and integrated acoustic backscatter arising from krill swarms to produce distribution maps of krill areal biomass density and standing stock (biomass) estimates. Krill standing stock for the Area 48 was estimated to be 62.6 megatonnes (mean density of 30 g m –2 over 2 million km 2 ) with a sampling coefficient variation of 13%. The highest mean krill densities were found in the South Orkney Islands stratum (93.2 g m –2 ) and the lowest in the South Georgia Island stratum (6.4 g m –2 ). The krill densities across the strata compared to those found during the previous survey indicate some regional differences in
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances

We study the problem of best-arm identification (BAI) in the fixed-budget setting with heterogeneous reward variances. We propose two variance-adaptive BAI algorithms for this setting: SHVar for known reward variances and SHAdaVar for unknown reward variances. Our algorithms rely on non-uniform budget allocations among the arms where the arms with higher reward variances are pulled more often than those with lower variances. The main algorithmic novelty is in the design of SHAdaVar, which allocates budget greedily based on overestimating the unknown reward variances. We bound probabilities of misidentifying the best arms in both SHVar and SHAdaVar. Our analyses rely on novel lower bounds on the number of pulls of an arm that do not require closed-form solutions to the budget allocation problem. Since one of our budget allocation problems is analogous to the optimal experiment design with unknown variances, we believe that our results are of a broad interest. Our experiments validate our theory, and show that SHVar and SHAdaVar outperform algorithms from prior works with analytical guarantees.
Differentiable Shadow Mapping for Efficient Inverse Graphics

We show how shadows can be efficiently generated in differentiable rendering of triangle meshes. Our central observation is that pre-filtered shadow mapping, a technique for approximating shadows based on rendering from the perspective of a light, can be combined with existing differentiable rasterizers to yield differentiable visibility information. We demonstrate at several inverse graphics problems that differentiable shadow maps are orders of magnitude faster than differentiable light transport simulation with similar accuracy - while differentiable rasterization without shadows often fails to converge.