Miracle-dz/UniFAR-FTUs huggingface dataset:
THE PREJUDICES OF LEAST SQUARES, PRINCIPAL COMPONENTS AND COMMON FACTORS SCHEMESf C. A. Los

We prove mathematically that the least squares regression scheme is of little use for identification from inexact data, even in the low noise case. Its results depend solely on the prejudice~ concerning which subsets of variables are chosen as "regressands" and which as "regressors" from a given set of data variables. In practice least squares regression results are always biased and depend on the relative noise levels, while even the signs of the "estimates" are completely determined by the arbitrary choice of the regressands if the number of underlying relationships is misspecified. The principal components (or statistical common factor) scheme fares a similar fate, since the choice of how many principal components (or common factors) to retain is essentially prejudiced and not determined by the data. Both schemes produce artificial and unexplained correlations among the residuals. For the principal components scheme this occurs because it usually violates Wilson's inequality. Afortiori we prove exactly why in practice factor indeterminancy occurs. The exact (ideal) multiple common factor scheme of Frisch and Thurstone has not yet been solved, except for small numbers (six and less) of data variables.
Adaptive precision setting for cached approximate values

Caching approximate values instead of exact values presents an opportunity for performance gains in exchange for decreased precision. To maximize the performance improvement, cached approximations must be of appropriate precision: approximations that are too precise easily become invalid, requiring frequent refreshing, while overly imprecise approximations are likely to be useless to applications, which must then bypass the cache. We present a parameterized algorithm for adjusting the precision of cached approximations adaptively to achieve the best performance as data values, precision requirements, or workload vary. We consider interval approximations to numeric values but our ideas can be extended to other kinds of data and approximations. Our algorithm strictly generalizes previous adaptive caching algorithms for exact copies: we can set parameters to require that all approximations be exact, in which case our algorithm dynamically chooses whether or not to cache each data value. We have implemented our algorithm and tested it on synthetic and real-world data. A number of experimental results are reported, showing the effectiveness of our algorithm at maximizing performance, and also showing that in the special case of exact caching our algorithm performs as well as previous algorithms. In cases where bounded imprecision is acceptable, our algorithm easily outperforms previous algorithms for exact caching.
Estimation and diagnostic analysis in skew-generalized-normal regression models

ABSTRACT The skew-generalized-normal distribution [Arellano-Valle, RB, Gómez, HW, Quintana, FA. A new class of skew-normal distributions. Comm Statist Theory Methods 2004;33(7):1465–1480] is a class of asymmetric normal distributions, which contains the normal and skew-normal distributions as special cases. The main virtues of this distribution is that it is easy to simulate from and it also supplies a genuine expectation–maximization (EM) algorithm for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models assuming skew-generalized-normal random errors and we develop a diagnostics analyses via local influence and generalized leverage, following Zhu and Lee's approach. This is because Cook's well-known approach would be more complicated to use to obtain measures of local influence. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.
Multi-Curve Translator for High-Resolution

where a = c = 1 and b = 0. Both CycleGAN and UNIT contain two discriminators (DX and DY) and two generators (GX→Y and GY→X ). For UNIT, a generator can be further divided into an encoder E and a generator G, then GX→Y = GY(EX (x)) and GY→X = GX (EY(y)). Note that the process of sampling latent codes is omitted here. For simplicity, we denote the adversarial loss when training the generator G as Lgan. CycleGAN and UNIT both contain the cycle-consistency loss [25,9], which is formulated as:
Compressed sensing image processing in the wavelet transform domain

Multi-resolution is the good characteristics of wavelet transform. In wavelet transform domain, high frequency subband of image is sparse. Less high frequency coefficient can be sampled by compressed sensing technology. In this study, for an image, a sparse representation in the wavelet transform domain is found. Image is reconstructed by the orthogonal matching pursuit (OMP) , compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS) and Suspace Pursuit (SP), respectively. Different wavelet basis and sampling rate which affect the quality of the reconstruction are discussed. Experimental result shows that performance of IRLS is the best, OMP are easy implementation and fast speed, and Coif3 has a better performance than the other wavelet basis.
DeMoN : Depth and Motion Network for Learning Monocular Stereo – Supplementary Material – A . Network

Our network is a chain of encoder-decoder networks. Figures 15 and 16 explain the details of the two encoderdecoders used in the bootstrap and iterative net part. Fig. 17 gives implementation details for the refinement net. The encoder-decoders for the bootstrap and iterative net use additional inputs which come from previous predictions. Some of these inputs, like warped images or depth from optical flow, need to be generated with special layers, which we describe here.
DeMoN : Depth and Motion Network for Learning Monocular Stereo – Supplementary Material – A . Network

Our network is a chain of encoder-decoder networks. Figures 12 and 13 explain the details of the two encoderdecoders used in the bootstrap and iterative net part. Fig. 14 gives implementation details for the refinement net. The encoder-decoders for the bootstrap and iterative net use additional inputs which come from previous predictions. Some of these inputs, like warped images or depth from optical flow, need to be generated with special layers, which we describe here.
DeMoN: Depth and Motion Network for Learning Monocular Stereo

Our network is a chain of encoder-decoder networks. Figures 15 and 16 explain the details of the two encoderdecoders used in the bootstrap and iterative net part. Fig. 17 gives implementation details for the refinement net. The encoder-decoders for the bootstrap and iterative net use additional inputs which come from previous predictions. Some of these inputs, like warped images or depth from optical flow, need to be generated with special layers, which we describe here.
Exploring Trust-Aware Neighbourhood in Trust-based Recommendation

Traditional Recommender Systems (RS) do not consider any personal user information beyond rating history. Such information, on the other hand, is widely available on social networking sites (Facebook, Twitter). As a result, social networks have recently been used in recommendation systems. In this paper, we propose an efficient method for incorporating social signals into the recommendation process by building a trust network which supplements the users' rating profiles. We first show the effect of different cold-start users types on the Collaborative Filtering (CF) technique in several real-world datasets. Later, we propose a "Trust-Aware Neighbourhood" algorithm which addresses a performance issue of the former by limiting the trusted neighbourhood. We show the doubling of the rating coverage compared to the traditional CF technique, and a significant improvement in the accuracy for some datasets. Focusing specifically on cold-start users, we propose a "Hybrid Trust-Aware Neighbourhood" algorithm which expands the neighbourhood by considering both trust and rating history of the users. We show a near complete coverage with a rich trust network dataset-- Flixster. We conclude by discussing the potential implementation of this algorithm in a budget-constrained cloud environment.
The Behavior of Squall Lines in Horizontally Heterogeneous Coastal Environments

AbstractInland squall lines respond to the stable marine atmospheric boundary layer (MABL) as they move toward a coastline and offshore. As a storm’s cold pool collides with the marine layer, characteristics of both determine the resulting convective forcing mechanism over the stable layer and storm characteristics. Idealized numerical experiments exploring a parameter space of MABL characteristics show that the postcollision forcing mechanism is determined by the buoyancy of the cold pool relative to the MABL. When the outflow is less buoyant, storms are forced by a cold pool within the marine environment. When the buoyancies are equivalent, a hybrid cold pool–internal gravity wave develops after the collision. The collision between a cold pool and less buoyant MABL initiates internal waves along the stable layer, regardless of MABL depth. These waves are inefficient at lifting air into the storm, and ascent from the trailing cold pool is needed to support deep convection. Storm intensity decreases with .
Title : Representation learning with reward prediction errors 27 August , 2021

The Reward Prediction Error hypothesis proposes that phasic activity in the midbrain dopaminergic system reflects prediction errors needed for learning in reinforcement learning. Besides the well-documented association between dopamine and reward processing, dopamine is implicated in a variety of functions without a clear relationship to reward prediction error. Fluctuations in dopamine levels influence the subjective perception of time, dopamine bursts precede the generation of motor responses, and the dopaminergic system innervates regions of the brain, including hippocampus and areas in prefrontal cortex, whose function is not uniquely tied to reward. In this manuscript, we propose that a common theme linking these functions is representation , and that prediction errors signaled by the dopamine system, in addition to driving associative learning, can also support the acquisition of adaptive state representations. In a series of simulations, we show how this extension can account for the role of dopamine in temporal and spatial representation, motor response, and abstract categorization tasks. By extending the role of dopamine signals to learning state representations, we resolve a critical challenge to the Reward Prediction Error hypothesis of dopamine function.
A Nonsmooth Nonconvex Descent Algorithm

The paper presents a new descent algorithm for locally Lipschitz continuous functions $f:X\to\mathbb{R}$. The selection of a descent direction at some iteration point $x$ combines an approximation of the set-valued gradient of $f$ on a suitable neighborhood of $x$ (recently introduced by Mankau & Schuricht) with an Armijo type step control. The algorithm is analytically justified and it is shown that accumulation points of iteration points are critical points of $f$. Finally the algorithm is tested for numerous benchmark problems and the results are compared with simulations found in the literature.
Leveraging over Prior Knowledge for Online Learning of Visual Categories across Robots

Open ended learning is a dynamic process based on the continuous processing of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On the other, it is important to continuously update an existing model so to exploit the new incoming data, especially if their informative content is very different from what is already known (online learning). Until today these two aspects of the learning process have been tackled separately. In this thesis we propose an algorithm that takes the best of both worlds: we consider a sequential learning setting, and we exploit the potentiality of knowledge transfer with a computationally cheap solution. At the same time, by relying on past experience we boost online learning to predict reliably on future problems. A theoretical analysis, coupled with extensive experiments, show that our approach performs well in terms of the online number of training mistakes, as well as in terms of performance on separate test sets.
Discrimination of Mature and Dissipating Severe-Wind-Producing MCSs with Layer-Lifting Indices

AbstractThe environmental factors that drive the dissipation of linear severe-wind-producing mesoscale convective systems (MCSs) are investigated. Layer-lifting indices are emphasized, which measure convective instability in forward-propagating MCSs by considering that deep convective latent heating depends on 1) the potential latent heating within the atmospheric column, measured by the integrated CAPE (ICAPE), and 2) the dilution of buoyancy due to midtropospheric inflow, measured by the inflow fraction (IF) of convectively unstable air to total system-relative inflow. These elements are integrated to define the layer-lifting CAPE (CAPEll), which depends on environmental thermodynamics, kinematics, and the MCS’s movement vector. Radar reflectivity plots are used to subjectively identify and classify MCSs in terms of their stage (mature or dissipating) and degree of organization (highly or weakly organized). Nonparametric statistical inferences are performed on several metrics computed at maturity and di.
Adaptive Re-ranking of Deep Feature for Person Re-identification

Typical person re-identification (re-ID) methods train a deep CNN to extract deep features and combine them with a distance metric for the final evaluation. In this work, we focus on exploiting the full information encoded in the deep feature to boost the re-ID performance. First, we propose a Deep Feature Fusion (DFF) method to exploit the diverse information embedded in a deep feature. DFF treats each sub-feature as an information carrier and employs a diffusion process to exchange their information. Second, we propose an Adaptive Re-Ranking (ARR) method to exploit the contextual information encoded in the features of neighbors. ARR utilizes the contextual information to re-rank the retrieval results in an iterative manner. Particularly, it adds more contextual information after each iteration automatically to consider more matches. Third, we propose a strategy that combines DFF and ARR to enhance the performance. Extensive comparative evaluations demonstrate the superiority of the proposed methods on three large benchmarks.
Iterative Projection and Matching : Finding Structure-preserving Representatives and Its Application to Computer Vision : Supplementary Material

The supplementary material provided in this document is organized as follows. In Section 1, we present the proofs for the lemmas and propositions stated in the main manuscript. In Section 2 our theoretical results are validated by performing experiments on real and synthetic data. Finally, in Section 3, further experiments are provided to investigate the performance of the proposed approach on several different real datasets.
E 2 PN: Efﬁcient SE(3)-Equivariant Point Network

This paper proposes a new point-cloud convolution structure that learns SE(3)- equivariant features. Compared with existing SE(3)-equivariant networks, our design is lightweight, simple, and ﬂexible to be incorporated into general point-cloud learning networks. We strike a balance between the complexity and capacity of our model by selecting an unconventional domain for the feature maps. We further reduce the computational load by properly discretizing R 3 to fully leverage the rotational symmetry. Moreover, we employ a permutation layer to recover the full SE(3) group from its quotient space. Experiments show that our method achieves comparable or superior performance in various tasks while consuming much less memory and running faster than existing work. The proposed method can foster the adoption of equivariant feature learning in various practical applications based on point clouds and inspire future developments of equivariant feature learning for real-world applications.
SemEval-2013 Task 2: Sentiment Analysis in Twitter

In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a messagelevel subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The bestperforming team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively.
Blind Signal Classification Analysis and Impact on User Pairing and Power Allocation in Nonorthogonal Multiple Access

This paper studies blind signal classification (SC) in the nonorthogonal multiple access (NOMA) system, which especially determines whether or not the received NOMA signal requires successive interference cancellation (SIC) without a priori signal information. In this paper, two types of blind SC errors are analyzed: 1) the signal that has to cancel the superposed interference component is classified as one that does not require SIC and 2) the signal that has no need of performing SIC is classified as one that requires SIC. Here, the interesting observation is that the classification error of the first type decreases with the SNR, but that of the second type increases with the SNR. In this regard, this paper proposes the joint user pairing and power allocation policy for the NOMA system based on the above observation that the blind SC performance of the NOMA user who does not perform SIC decreases with the SNR. The joint optimization problem maximizes the sum-rate gain of NOMA over orthogonal multiple access (OMA) with constraints on the maximum classification error probability and the minimum data rate. Since the problem is nonconvex, we propose the numerical algorithm that iteratively finds the appropriate user scheduling and power allocation. Simulation results show that the proposed scheme outperforms existing user scheduling methods.
Low-density parity-check codes

A low-density parity-check code is a code specified by a parity-check matrix with the following properties: each column contains a small fixed number j \geq 3 of l's and each row contains a small fixed number k > j of l's. The typical minimum distance of these codes increases linearly with block length for a fixed rate and fixed j . When used with maximum likelihood decoding on a sufficiently quiet binary-input symmetric channel, the typical probability of decoding error decreases exponentially with block length for a fixed rate and fixed j . A simple but nonoptimum decoding scheme operating directly from the channel a posteriori probabilities is described. Both the equipment complexity and the data-handling capacity in bits per second of this decoder increase approximately linearly with block length. For j > 3 and a sufficiently low rate, the probability of error using this decoder on a binary symmetric channel is shown to decrease at least exponentially with a root of the block length. Some experimental results show that the actual probability of decoding error is much smaller than this theoretical bound.
Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100)

MAGIS-100 is a next-generation quantum sensor under construction at Fermilab that aims to explore fundamental physics with atom interferometry over a 100 m baseline. This novel detector will search for ultralight dark matter, test quantum mechanics in new regimes, and serve as a technology pathfinder for future gravitational wave detectors in a previously unexplored frequency band. It combines techniques demonstrated in state-of-the-art 10-meter-scale atom interferometers with the latest technological advances of the world’s best atomic clocks. MAGIS-100 will provide a development platform for a future kilometer-scale detector that would be sufficiently sensitive to detect gravitational waves from known sources. Here we present the science case for the MAGIS concept, review the operating principles of the detector, describe the instrument design, and study the detector systematics.
A new concept — fluid substitution by integrating rock physics and machine learning

This study presents a new and patented concept for fluid substitution that can be integrated with Machine Learning to provide robust and simple fluid substitution with approximately the same or better accuracy as Gassmann theory. The method is called ‘ROck physics Fluid Substitution’ (ROFS) and integrates machine learning and rock physics. ROFS allows for rapid and simple fluid substitution that in many cases give more physically consistent results than applied Gassmann theory. A stepwise workflow for the method is given. Comparison with Gassmann theory shows that the ROFS approach better predicts velocities in core plugs that are substituted from dry to brine filled. When applying the method on well logs, it is also demonstrated that for high porous rocks where the Gassmann assumptions are met, the methods give very similar results. But for intermediate-to-low porosity rocks, Gassmann theory seems to overpredict the fluid effect while the new model is more realistic. The method can be applied for both siliciclastic rocks and carbonates. By using a rock physics model for carbonates, the new method can account for the effect of microstructure variations such as pore shape variations and cracks when performing fluid substitution.
Geometry of tensor triangulated categories

Given a quasi-projective scheme X with an action of a finite group G we consider the tensor triangulated category D(X) . We relate the spectrum of this category, as defined by P. Balmer, with the spectrum of the category of all perfect complexes over the scheme X/G. Similarly, we consider the category of perfect complexes D(X) over a split super-scheme X. We give isomorphism of the spectrum of D(X) with the spectrum of D(X). Here X denotes the even part of the super-scheme X ; it is a scheme in the usual sense. The computation of these two spectrums gives examples of two distinct categories with isomorphic Balmer spectrums. Our result also shows the limitations of the geometric notion spectrum beyond the category of schemes. We suggest some possible generalisations of Balmer’s notion of spectrum.
Damage detection in beam through change in measured frequency and undamaged curvature mode shape

ABSTRACT This paper develops and presents a technique on the identification of the damage location and depth in a beam using a frequency contour method. Unlike the frequency contour methods presented in the literature, the one presented in this work does not require neither the solving of the governing differential equation analytically to obtain the contour lines, nor the calculation of the frequencies by varying all damage severities and locations in order to use an inverse problem to plot the contours. In other words, the detection method does not require the modelling of the damage. In this work, a contour line is plotted using only the value of the changes in the measured natural frequencies and the vectors of the curvature mode shapes of the intact structure which can be calculated numerically or can be derived from the measured mode shapes. In order to verify the practical applicability of the developed theoretical method, a numerical simulation and an experimental framework on cantilever beam have been designed and the results confirm the applicability of the developed method in the real applications.