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71abf0e5-4c4f-454a-a4a1-432b3c8fdd7f | Table REF presents an overview of all data smells along
with their distribution. The remainder of this report frequently
refers to these smells by their unique key which is also provided
here. The redundant and categorical value smells are the most
common categories with a total occurrence of 33 and 17 respectively.
T... | r |
f1081419-22d0-4bef-8ef6-6c6e972e4d6d | Finally we also analyse the distribution of smells within the
datasets. Figure REF
shows a two dimensional histogram of the smells and datasets such that
the intersection of datasets where a particular smell occurred is
filled. The histogram is colour-coded based on the smell category
which allows us to observe the mo... | r |
2d5ac67c-6736-4a5a-96b2-d5ccb4ed9f28 | The remainder of this section presents the smell groups and their
corresponding smells in more detail. We present examples of the smells
discovered along with an explanation of the underlying problems that
may arise. Where applicable, potential strategies to mitigate the
problem, context in which the smells may not app... | r |
c51864da-13c3-4ba3-822e-835552d97a46 | Code smells are frequently used by software engineers to identify
potential bugs, sources of technical debt and weak design choices.
Code smells in the context of traditional software have existed for
over three decades and have been extensively studied by the software
engineering research community. With the growing p... | d |
9a596437-2bc7-4376-956c-61540b27e825 | We consider our collection of data smells and the analysis of their
prevalence a first step towards aiding data scientists in the initial
stages of data analysis where human involvement is necessary. We hope
that our work raises awareness amongst practitioners to write better
documentation for their datasets and follow... | d |
2fd80a73-2ad2-4ea1-a9d3-4ee8e2051bd5 | The way speech prosody encodes linguistic, paralinguistic and non-linguistic information via multiparametric representations of the speech signals is still an open issue. Most models of intonation postulate that this encoding is performed by local and salient spatio-temporal patterns such as tones, atoms or breaks insc... | i |
a93868f8-4c1d-408e-9982-1d6c693c9a59 | The Gestalt model proposed by Aubergé and Bailly [1]} proposes that the encoding is direct, i.e. shapes make sense, and performed by spatio-temporal patterns that both cue each socio-communicative function and its scope, i.e. the linguistic units that are involved; e.g. the element carrying emphasis, the part of the ut... | i |
19bf014c-0cb8-41c7-80ab-9e06ab8f61d9 | These function-specific patterns, in fact, emerge from statistical modelling. Given a dataset that contains multiple instances of these patterns, the SFC extracts the shapes and their average contributions thanks to an iterative analysis-by-synthesis training process that consists of training function-specific pattern ... | i |
8c4320e2-49fd-4ab5-9ba4-5e8a9300fe01 | One shortcoming of the SFC model is that it is not sensitive to prominence: prosodic contours are simply superposed-and-added with no possibility of weighting their contributions.
In this paper we supplement the SFC architecture with components responsible for weighting the contribution of the elementary contours in th... | i |
60a14e43-ae7e-43ea-b06f-f8709bfdeeeb | We assessed the plausibility of the proposed WSFC, and used it to explore two prosodic phenomena: i) the impact of the attitude, and ii) the impact of emphasis on the prominence of the other functional contours in the utterance. The two phenomena were explored in two different languages: French and Chinese. The results... | i |
84a1474a-1082-417f-a831-be46079e5abe | We proposed a prosody model capable of capturing the prominence of elementary prosodic contours that are based on their context of use. The WSFC has been also shown to improve the modelling performance of the SFC due to an added weighting mechanism. We have demonstrated its robustness and its usefulness in analysing th... | d |
5679623c-895c-4d89-b544-08ee28ceb416 | When developing electrical equipment, engineers optimize initial design proposals by carefully identifying a large number of design parameters. In doing so, they rely on rules of thumb, know-how and previous experience, existing standards and, increasingly, simulation and optimization tools.
Numerical optimization is u... | i |
73df7a8a-a032-4666-9670-4ab7dfa012c4 | In this section, the am () for transient eqs problems is validated. In the first step, the layered resistor of Fig. REF is considered, and the fe adjoint and analytic sensitivities are compared. In a second step, the method is applied to a nonlinear 320 kV cable joint specimen and the results are validated using resul... | r |
2ab61283-694d-426c-82a9-4e190e4ee5a5 | The adjoint variable method is a method for calculating gradients of selected quantities of interest with respect to a set of design parameters. It has computational costs nearly independent of the number of design parameters and is, thus, very efficient for problems where the number of parameters is larger than the nu... | d |
99bc48bb-c8c9-45b7-b0f7-9a2bce380315 | blackIn recent years, cloud computing has been of great interest to researchers [1]}[2]}. Its role in providing on-demand services and resources has opened its way into a variety of technological environments, ranging from data centers [3]}, power systems [3]} and video delivery systems [3]} to earthquake command syste... | i |
5acaa144-99aa-4ca6-8c41-b1a9aa12b026 | A variety of design objectives including fairness [1]}, fault tolerance [2]}, energy consumption [3]}, [4]} and reliability [5]} are considered in the design of cloud computing systems. blackHowever, security is probably the most critical design objective in this field [6]}[7]}[8]}[9]}[10]}[11]}.
| i |
c25a8830-43c0-4629-b870-d5f6a928ddfc | blackIoT frequently appears in the ecosystem of Cloud computing. This technology integrates geographically-distributed cyber-physical devices or cyber-enabled systems with the goal of providing strategic services[1]}, [2]}. The application areas of IoT vary from transport and healthcare to agriculture and FinTech. blac... | i |
a24a0276-b3e3-404c-afe0-d94899d62170 | blackFigure REF introduces the icons we will use in the rest of this paper for Cloud, IoT, CAIoT and IoTBC. In this figure, overlapping parallelograms represent technologies on top of each other. As seen in the figure, the dichotomy of Cloud and IoT leads to two emerging technologies, namely CAIoT and IoTBC. CAIoT dep... | i |
548bf8a5-344c-4054-b3df-5cd50996fc68 | blackThe dichotomy of Figure REF needs to be studied form both sides; CAIoT and IoTBC, each of which brings about a variety of challenges, issues and considerations. A comprehensive survey on each of the technologies can pave the way for further research. In this survey, we study CAIoT from a security point of view. T... | i |
d43ec014-7e9d-4fd7-994d-0dbfd36f1954 | blackWe present a survey on the literature of SCAIoT. We identify existing approaches towards the design of SCAIoT. We highlight security challenges faced by each approach. Moreover, we study the security controls used to address the challenges. We establish a layered architecture for SCAIoT, which reflects all the ide... | i |
c2f276ae-128d-48ed-897d-6e7bbef200c4 | blackThis review covered some sides and aspects of the dichotomy of cloud and IoT. The dichotomy gives raise to IoT-Based Cloud (IoTBC) and Cloud-Assisted IoT (CAIoT). This paper focused on the security of CAIoT. This research identified different approaches towards the design of secure CAIoT (SCAIoT) along with the re... | d |
97890967-a6f3-445d-9468-fb9d00641dc2 | Nowadays, the amount of data produced doubles every two years [1]}, and the peak of the heyday of computers in the classical meaning is coming to an end.
Maintaining the current momentum of technological development requires a change in the approach to computing.
One of the most promising solutions is to transfer the i... | i |
166d287a-3895-4e06-9b57-016f5e108ddc | In computer vision systems that for effective operation require processing of large amounts of data in real time, quantum neural networks (QNNs) can prove to be a very attractive solution.
On the basis of experiments carried out in recent years, it can be observed that the network training process is becoming quicker a... | i |
257560eb-a0b5-42cb-af9c-7f89bffa2468 | In this paper, we describe the results of our work on a quantum neural network for the classification of traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset.
The aim of our research was to analyze and compare the results obtained using a classical deep convolutional neural network (DCNN) an... | i |
175ac8b8-10c2-4898-a57e-24955a8b106f | The remainder of this paper is organized as follows.
Section provides background information on quantum neural networks.
In Section our motivation and the purpose of our experiment were described in the context of related work on machine learning applied to computer vision systems.
Section presents the experiments c... | i |
dbfa8772-8baf-4278-b5b2-17f25bcedd65 | Quantum machine learning is a challenging field.
This concerns not only the proper design of algorithms but also the very way in which classical real data are encoded in quantum space.
Uncertainty is also provided by the fact that the only official form of a quantum computer is not known, but rather a number of proposa... | d |
8b762800-2257-421d-a19e-78f900bb3347 | The experiment showed that it is possible to achieve a high classification accuracy (more than 94%) for a neural network with quantum convolution, but raised the question of quantum supremacy. Quantum algorithms require special preprocessing on the dataset, which in the case of hybrid networks, of which the network wit... | d |
2bfac99a-dbdf-4699-aead-458cdbefb5b3 | The prospects for further development of the project are very broad.
It is planned to try to compare the influence of data augmentation on learning accuracy and the potential overfitting effect in the comparison of the classical network and the network with quantum convolution. In addition, another task will be underta... | d |
99569e2b-a218-4b56-a84d-ad0bd7984003 | When medical images were stored, they may have different image orientations. In the further segmentation or computing, this difference may affect the results, since current deep neural network (DNN) systems generally only take the input and output of images as matrices or tensors, without considering the imaging orient... | i |
70a58b33-aeb6-40d4-99f0-281764cecdd9 | Deep neural network has performed outstandingly in computer vision and gradually replaced the traditional methods. DNN also take a important role in medical image processing, such as image segmentation[1]} and myocardial pathology analysis[2]}. For CMR images, standardization of all the images is a prerequisite for fur... | i |
31507f62-7eaa-4e55-87a8-20e00e5598d9 | Most studies in the field of medical image processing have only focused on the further computing, so they have to spend a lot of manpower to do the preprocess. If we can auto adjust the images, it will save lots of time. Nevertheless, recognizing the orientation of different modality CMR images and adjusting them into ... | i |
0edb501b-fe1e-499c-9257-bb108d8c07e5 | In most image classification problem like ImageNet, we do some transformation to the image but these transformation do not change the label, for example we rotate a dog image and it's still a dog image. However, the orientation could be changed if we do transformation like flipping to the images. In this work, we utili... | i |
8f587d5b-0f39-4809-8ac1-232d5db799d4 | This work is aimed at designing a DNN-based approach to achieve orientation recognition for multiple CMR modalities. Figure REF presents the pipeline of our proposed method. The main contributions of this work are summarized as follows:
<FIGURE> | i |
9799c25e-4674-4e3f-884a-48d83cb4c135 |
We propose a scheme to standardize the CMR image orientation and categorize all the orientations for classification.
We present a DNN-based orientation recognition method for CMR image and transfer it to other modalities.
We propose a predicting method to improve the accuracy for orientation recognition.
| i |
7f9acd56-5d71-4d52-b8f1-5682eea2c1dd | In this section, we introduce our proposed method for orientation recognition. Our proposed method is based on Deep Neural Network which was proved effective in image classification. In CMR Image Orientation Categorization, we improved the predicting accuracy by the following four steps. Firstly, we apply invertible op... | m |
f6c4432c-d36f-4bfc-ad03-2cafbe4e4d7d | DNN model get quite high accuracy in recognition of CMR image orientation and transfer learning make it easy to be transferred to other modalities . Thanks to the data expansion and augmentation, the model only need a few data. The improved prediction we proposed further increase the accuracy. We are sure that DNN mode... | d |
5205039c-51a9-485d-911a-5a63b2de732d | Invariance against different transformations is crucial in many tasks such as image classification and object detection.
Previous works have addressed this challenge, from early work on feature descriptors [1]} to modeling geometric transformations [2]}.
It is also very beneficial if the network can detect the importan... | i |
b6cd0814-b5c9-4990-9df3-5b65d0e02911 | With recent advance in deep learning, there has been a breakthrough in various areas of Computer Vision mainly caused by the advances in Convolution Networks [1]}, [2]}.
Introducing deeper and more complex classification network architectures [3]}, [4]}, [5]} has led to achieving high accuracy in challenging datasets s... | i |
c2a0ce98-5e57-45cf-9412-08e20d0805d2 | In [1]}, the authors introduced the STN method for improving the classification accuracy.
In STN, a network is trained to generate parameters of an affine transformation which is applied to the input image.
They showed that this modification simplified the task and improved the performance.
In their work, affine parame... | i |
1a741b1e-cd55-4c8c-b3df-4ee5fa8ce12f | Similar to STN, we address improving the classifier accuracy by applying an affine transformation to the input.
Different from their approach, we model the task as a Markovian Decision Process.
We break the affine transformation to a sequence of discrete and simple transformations and use RL to search for a combination... | i |
a70d37e9-e4d0-4a84-88bb-08bde0a2d0cf | Since the breakthrough in RL [1]}, many works have successfully utilized it for solving different vision problems [2]}, [3]}, [4]}, [5]}.
Combining RL methodology with deep learning as well as significant improvement in RL algorithms [6]}, [7]} has made it a powerful search method for different applications [2]}, [9]}.... | i |
aa2962af-d1c3-4905-88cc-36ebe385204f | To sum up, we formulate the transformation task as a sequential decision-making problem,
in which instead of finding a one-step transformation, the model searches for a combination of discrete transformations to improve the performance.
We use RL for solving the search problem and apply both Policy Gradient and Actor-C... | i |
4ef3fc84-e2c1-4a20-a666-64766c04b4f1 | Our work is mainly related to STN model and the RL algorithms that we utilize for solving the sequential transformation task.
In this part, we focus on explaining the main ideas of STN approach as well as the required background about RL algorithms.
| w |
ede05da2-ab8b-4a48-a4c5-394c8b0ba3a1 | In this section, we present the experimental setup for testing the performance of our method in improving the classification accuracy by applying a sequence of discrete transformations to the input image.
We proceed with a discussion on results and an ablation study on the impact of reward design and episode length.
| m |
7cf1e042-483b-4981-af08-87f7700684a2 | In this work, we present an extension of the STN model, in which we model the problem as a sequence of discrete transformations.
We formulate finding the affine transformation as a search problem and aim to learn a combination of discrete transformations which improves the classification accuracy.
We use both Policy Gr... | d |
e606b00f-1be2-4dc9-8112-943fec2df957 | Over 30 years ago, Johnson et.al. asked "How easy is neighbourhood search?" [1]}. They investigated the complexity of finding locally optimal solutions to NP-hard combinatorial optimisation problems. They show that, even if finding an improving neighbour (or proving there isn't one) takes polynomial time, finding a loc... | w |
ffa70cef-7f46-4e95-b565-8e338446c564 | [1]} considers hill-climbing using flips of \(n\) zero-one variables. If the objective values are randomly generated the number of local optima tends to grow exponentially with \(n\) . Thus the expected number of successful flips to reach a local optimum from an arbitrary point grows only linearly with \(n\) . A more ... | w |
4838a1a9-00e4-4c48-b0b3-12a6e8de6b07 | [1]} analysed the average difference between a candidate solution and its neighbours, for five well-known combinatorial optimisation problems. Since this difference is positive for candidates with less than average fitness it implies that any local optima must have a better than average fitness. The result also shows h... | w |
8dfd4151-5852-4959-88ff-337f96d3c69f | The idea of counting the number of solutions at each fitness level is directly related to the concept of the density of states which applies to continuous fitness measures encountered in solid state physics [1]}.
This work additionally shows how to estimate the density of states for a problem using Boltzmann strategies... | w |
b9a7239f-1565-41b1-9093-0512d711cf32 | Neighbours' similar fitness (NSF) is related to the idea of fitness correlation, spelled out for example in the paper "Correlated and Uncorrelated Fitness Landscapes and How to Tell the Difference" [1]}.
In this paper random tours (each pair of points on the tour being neighbours) are used to predict the fitness of a p... | w |
2d79a00f-bf3c-4f2d-84c5-a64dbeeb397e | These notions of correlation are tied to the landscape structure, in contrast to NSF which only applies to the immediate neighbours of points with a given fitness.
NSF holds of many well-known neighbourhood operators, and we suggest that it is even a criterion used in designing such operators.
| w |
a214d764-b6f9-430a-b221-137f30ddc76b | Artificial Intelligence (AI) systems make errors. They should be corrected without damage of existing skills. The problem of non-destructive correction arises in many areas of research and development, from AI to mathematical neuroscience, where the reverse engineering of the brain ability to learn on-the-fly remains a... | i |
619a20ac-0317-48a9-a855-2c6209f83ebf | The non-desrructive correction requires separation of the situations (samples) with errors from the samples corresponding to correct behavior by a simple and robust classifier. Linear discriminants introduced by [1]} are simple, robust, require just the inverse covariance matrix of data, and may be easily modified for ... | i |
e48bbde4-5e39-4a61-b92e-92013ffc8ede | In this work, we demonstrate that in high dimensions and even for exponentially large samples, linear classifiers in their classical Fisher's form are powerful enough to separate errors from correct responses with high probability and to provide efficient solution to the non-destructive corrector problem.
We prove that... | i |
cd6de488-c4b8-4b35-9001-af1d55c738d3 | A problem of data fusion in multiagent systems has clear similarity to the problem of non-destructive correction. According to [1]}, data collected by different agents may not be naively combined due to changes in the context, and special procedures for their assimilation without damage of gained skills are needed. The... | i |
b55aa05e-cb15-4b30-ac43-a21b04962d60 | Let us start from the equidistribution in the unit ball \(\mathbb {B}_n\) in \(\mathbb {R}^n\) . The probability \(p\) that a random point belongs to a layer \(\mathbb {B}_n \setminus r\mathbb { B}_n\) (\(0<r<1\) ) between spheres of radius 1 and of radius \(r\) is \(p=1-r^n\) . Let us take a unit vector \( v\) . T... | r |
d316696e-d1e2-4457-b42b-bec2879a19de | Theorem 1
Let \(\lbrace x_1, \ldots , x_M\rbrace \) be a set of \(M\) i.i.d. random points from the equidustribution in the unit ball \(\mathbb {B}_n\) , \(0<r<1\) . Then
\(\begin{split}&\mathbf {P}\left(\Vert x_M\Vert >r \mbox{ and } \left(x_i,\frac{x_M}{\Vert x_M\Vert }\right)<r \mbox{ for all } i\ne M \right)\\... | r |
b63dbc76-697d-4198-acbf-dd43d5f817fb | The proof is based on the independence of random points \(\lbrace x_1, \ldots , x_M\rbrace \) , on the geometric picture presented in Fig. REF , and on an elementary inequality \(\mathbf {P}(A_1 \& A_2 \& \ldots \& A_m)\ge 1- \sum _i(1-\mathbf {P}(A_i))\) for any events \(A_1, \ldots , A_m\) . In Fig. REF we should t... | r |
b704cc5a-d799-410c-840b-8fd2b4d03960 | Corollary 1 Let \(\lbrace x_1, \ldots , x_M\rbrace \) be a set of \(M\) i.i.d. random points from the equidustribution in the unit ball \(\mathbb {B}_n\) and \(0<r,\vartheta <1\) . If
\(M<2({\vartheta -r^n})/{\rho ^{n}},\)
| r |
0990b106-6226-4b6e-9aa3-81d48116652d | Remark 1 According to (REF ) the pre exponential factor in the estimate for \(M^2\) may be chosen as \(\vartheta \) , while the exponent depends on \(r\) only. For example, for \(r=1/\sqrt{2}\) the simple sufficient condition (REF ) gives \(M^2<\frac{2}{3}\vartheta 2^{n/2}\) . For \(\vartheta =0.01\) (or specifici... | r |
d8d555aa-9d50-4106-a110-009f93ae4ed5 | Thus, if we select 2,700,000 i.i.d. points from an equidistribution in a unit ball in \(\mathbb {R}^{100} \) then with probability \(p>0.99\) all these points will be vertices of their convex hull.
| r |
24dd9598-7246-479a-8139-8edffb22881e | Estimates similar to (REF ), (REF ), and (REF ) are useful for the equidistribution of the normalized data on a unit sphere too. This is because they not only establish the fact of separability but also specify separation margins.
| r |
fcd42cd4-2959-4d14-9cea-70f674941e59 | Consider a product distribution in an \(n\) -dimensional unit cube. Let the coordinates of a random point, \(X_1, \ldots , X_n\) (\(0 \le X_i \le 1\) ) be independent random variables with expectations \(\overline{X}_i\) and variances \(\sigma _i^2>\sigma _0^2>0\) . Let \(\overline{x}\) be a vector with coordinates ... | r |
d0e1cc71-e819-4987-b51c-687c6581ca35 | Concentration near the spheres with different centres implies concentration in the vicinity of their intersection (an example of the `waist concentration' [1]}). The vicinity of the spheres, where the distribution is concentrated, can be estimated by the Hoeffding inequality [2]}. Let \(Y_1, \ldots , Y_n\) be independ... | r |
053bb538-8640-4f07-945a-3b43bc629d69 | Let us take \(Y_i=(X_i-c_i)^2\) . Consider the centres located in the cube, \(0\le c_i \le 1\) . Then \(0\le Y_i \le 1\) and \(\mathbf {E}\left[\overline{Y} \right]=\frac{1}{n}R^2\) . In particular, if \(c_i=\overline{X}_i\) then \(\mathbf {E}\left[\overline{Y} \right]=\frac{1}{n}R_0^2\) (the minimal possible value)... | r |
5720dcd9-d7d1-4bda-abd3-10a804fc3145 | With probability \(p>1- 2\exp \left(-2nt^2 \right)\) a random point \(x\) belongs to the spherical layer (\(\delta ={nt}/{R_0^2}\) , \(t=\delta R_0^2/n\) ):
\(1-\delta \le {\Vert x-\overline{x}\Vert ^2}/{R_0^2}\le 1+\delta .\)
| r |
1a32c8c5-1590-40a8-9711-b5f19e67b769 | Consider \(M\) i.i.d. points \(\lbrace x_1, \ldots , x_M\rbrace \) from the product distribution. With probability \(p>1- 2M\exp \left(-2nt^2 \right)\) they all belong to the spherical layer (REF ). Therefore, with this probability we return to the situation presented in Fig. REF with internal radius \(\sqrt{1-\del... | r |
cb8af0e7-20ec-43db-9e9a-50459bcc8599 | The radius of ball \(B\) is defined by \(\rho ^2=(1+\delta )R_0^2-(1-\delta )R_0^2=2\delta R_0^2\) .
The concentration radius (REF ) for the spheres concentric with the ball \(B\) (Fig. REF ) is defined by \(R^2=R_0^2+(1-\delta )R_0^2=(2-\delta )R_0^2\) .
Therefore, a random point does not belong to the ball \(B\) w... | r |
5fa71ae2-a5ad-470c-b776-34a923f1c224 | Theorem 2 Let \(\lbrace x_1, \ldots , x_M\rbrace \) be i.i.d. random points from the product distribution in a unit cube, \(0< \delta <2/3\) . Then
\(\begin{split}&\mathbf {P}\left(1-\delta \le \frac{\Vert x_j-\overline{x}\Vert ^2}{R^2_0}\le 1+\delta \mbox{ and } \right. \\& \left.\left(\frac{x_i-\overline{x}}{R_0},\... | r |
0b04f87e-d8f0-42e9-8ff5-f8bd30ab39a8 | When the value of delta is chosen as \(\delta =0.5\) and \(R_0\) is replaced with its estimate from below, \(R^2_0\ge n \sigma _0^2\) , inequalities (REF ) and (REF ) result in the following estimates:
\(\begin{split}&\mathbf {P}\left(\frac{1}{2} \le \frac{\Vert x_j-\overline{x}\Vert ^2}{R^2_0}\le \frac{3}{2} \mbox{ ... | r |
1abccbce-ce6c-4a38-9e4b-99464eb5ad0f | Corollary 2 Let \(\lbrace x_1, \ldots , x_M\rbrace \) be i.i.d. random points from the product distribution in a unit cube and \(0<\vartheta <1\) . If
\(M<\frac{1}{3}\vartheta \exp \left(0.5 n\sigma _0^4 \right),\)
| r |
b454d8d2-8763-4cb8-9763-fb4e5bf6db86 | then with probability \(p>1-\vartheta \)
\(\begin{split}& 0.5\le \frac{\Vert x_j-\overline{x}\Vert ^2}{R_0^2} \le 1.5 \mbox{ and }\left(\frac{x_i-\overline{x}}{R_0},\frac{x_M-\overline{x}}{\Vert x_M-\overline{x}\Vert }\right)<\frac{\sqrt{2}}{2} \\&\mbox{ for all } i,j, i \ne M.\end{split}\)
| r |
de268d45-977f-45d2-879c-d5267ad64948 | then with probability \(p>1-\vartheta \)
\(\begin{split}&0.5\le \frac{\Vert x_j-\overline{x}\Vert ^2}{R_0^2} \le 1.5 \mbox{ and }\left(\frac{x_i-\overline{x}}{R_0},\frac{x_j-\overline{x}}{\Vert x_j-\overline{x}\Vert }\right)<\frac{\sqrt{2}}{2} \\& \mbox{ for all }i,j, i\ne j.\end{split}\)
| r |
0fb424c2-5d8f-456d-b947-e13fc663a768 | The estimates (REF ), (REF ) are far from being optimal and can be improved. The main message here is their exponential dependence on \(n\) : the upper boundary of \(M\) can grow with \(n\) exponentially. Numerical experiments show that the equidistribution in cube is not worse, from the practical point of view, than... | r |
e461b4f4-16b8-4107-b24f-9d7c30f2cad0 | In general position, a set of \(n\) points in \(\mathbb {R}^{n-1}\) is linearly separable. Therefore, if \(n-1\) or less points from \(\mathcal {M}=\lbrace x_1, \ldots , x_{M-1}\rbrace \) are not separated from \(x\) by the hyperplane \(L\) (Fig. REF ) then they can be separated by an additional hyperplane orthog... | r |
d55cf73a-217a-4b06-9fdc-54cc76ba4992 | Theorem 3
Let \(S=\lbrace x_1, \ldots , x_M\rbrace \) be a set of \(M\) i.i.d. random points from the equidustribution in the unit ball \(\mathbb {B}_n\) , \(0<r<1\) . Then
\(\begin{split}&\mathbf {P} \left(\Vert x_M\Vert >r \, \&\, \left(x_i,\frac{x_M}{\Vert x_M\Vert }\right)<r\mbox{ for at least } M\!-\!n \mbox{... | r |
31334b4e-a50e-4386-a8b4-57a5d2b11d6c | For \(r=1/\sqrt{2}\) , \(n=100\) , and \(M=2,74\cdot 10^6\) , (REF ) gives: \(\mathbf {P} \left(\Vert x_M\Vert >r \& \left(x_i,\frac{x_M}{\Vert x_M\Vert }\right)<r\mbox{ for at least } M\!-\!n \ \ x_i\ \in S\right) \ge 1-\theta \) with \(\theta <5\cdot 10^{-14}\) . The probability stays close to 1 for much larger va... | r |
2b11e159-c3ee-4f84-8df2-67a5900bba3e | Classical measure concentration theorems state that random points are concentrated in a thin layer near a surface (a sphere, an average or median level set of energy or another function, etc.).
The stochastic separation theorems describe thin structure of these thin layers: the random points are not only concentrated i... | d |
1b966857-df05-454d-a256-cd50eba2eb26 |
Relax the requirement of independent coordinates in Theorem REF to that of weakly dependent vector-valued variables;
Instead of equidistributions, consider distributions with strongly log-concave probability densities;
Use various simple and robust nonlinear classifiers like small neural cascades (compare to Theore... | d |
45806a0e-ac02-4d53-bc91-5cc1b5c41fde | Stochastic separation Theorems 1–3 are important for synthesis and one-shot correction of AI systems. For example, inequalities (REF ) and (REF ) evaluate the probability that a randomly selected point \(x_M\) is linearly separable from all other \(M-1\) points by the linear functional \(l(x)=(x,x_M-\overline{x})\) .... | d |
c240e12f-8c95-4e44-a168-16a35facfc6b | Stochastic separation theorems can simplify high-dimensional data analysis and generate the 'blessing of dimensionality' [1]}. For example, according to (REF ), in a dataset with 100 attributes and \(M<2.7\cdot 10^6\) samples we should not be surprised to observe the linear separability of each sample from the rest of... | d |
32425a46-5b43-4fb0-b1bb-0f5f3fa910a8 | We analysed separation of random points from random sets. This is the problem of single correction of a legacy AI system. The question of generalisability of this correction is of great practical importance. It leads to a problem of separation of two random sets. A simple series of generalisations can be immediately pr... | d |
8c778edc-e351-48f0-9616-44850c442962 | The reported extreme separation capabilities of linear functionals offer new insights into the Grandmother cell or concept cell phenomena that are broadly reported in neuroscience [1]}, [2]}. The essence of the phenomenon is that some neurons in the human brain respond unexpectedly selective to particular persons or ob... | d |
f3075dcd-05d1-485d-a3fc-ae66b8526b7b | Recommender systems aim to provide users with personalized products or services. They can help handle the increasing online information overload problem and improve customer relationship management. Collaborative Filtering (CF) is a canonical recommendation technique, which predicts interests of a user by aggregating i... | i |
0af2a972-9d91-4ba2-9a92-76510c9ea576 | Nevertheless, the negative sampling techniques suffer from the following limitations.
Firstly, they introduce additional computation and memory costs.
In existing CF-based methods, the negative sampling algorithm should be carefully designed in order to not select the observed positive user-item pairs.
Specifically, to... | i |
0b183166-5d58-4254-a184-06bd11c2a5e8 | Self-supervised learning (SSL) models [1]}, [2]}, [3]}, that are proposed recently, provide us a possible solution to tackle the aforementioned limitations. SSL enables training a model by iteratively updating network parameters without using negative samples. Thus, it presents a way to scale recommender systems into b... | i |
ac698618-7ee9-444a-bdc7-0a1bb97e4841 | To the best of our knowledge, BUIR [1]} is the only framework for CF to learn user and item latent representations without negative samples.
BUIR is derived from BYOL [2]}. Similar to BYOL, BUIR employs two distinct encoder networks (i.e., online and target networks) to address the recurring of trivial constant solutio... | i |
6867fcd6-d7f4-4b51-951e-be30f0b56df4 | In this paper, we propose a self-supervised collaborative filtering framework, which performs posterior perturbation on user and item output embeddings, to obtain a contrastive pair.
On architecture design, our framework uses only one encoder that is shared by the online network and the target network.
This design make... | i |
0595bc8f-e9e7-4c59-9635-6e9bd9fe31f6 |
We propose a novel framework, SELFCF, that learns latent representations of users/items solely based on positively observed interactions. The framework uses posterior output perturbation to generate different augmented views of the same user/item embeddings for contrastive learning.
We design three output perturbatio... | i |
9c813045-c852-499a-8686-51bd89e2a521 | We evaluate the framework on three publicly available datasets and compare its performance with BUIR [1]} by encapsulating two popular CF-based baselines.
The CF baselines serve as a supervised counterpart compared with our self-supervised framework.
All baselines as well as the frameworks are trained on a single GeFor... | m |
8da1b0e4-0212-4eb7-a127-be944c29f172 |
RQ1: Whether the self-supervised baselines that only leverage positive user-item interactions can outperform their supervised counterparts?
RQ2: How SELFCF shapes the recommendation results for cold-start and loyal users?
RQ3: Why SELFCF works, and which component is essential in preventing collapsing?
| m |
f94ba4f7-b029-40b8-80e4-fe690bac67ae | We address the first research question by evaluating our framework against supervised baselines with 3 datasets under 4 evaluation metrics.
Next, we dive into the recommendation results of the baselines under both supervised and self-supervised settings and analyze their performance on users with different number of in... | m |
1a5c5605-7269-4bc9-9e94-f02d9a341a8d | In this paper, we propose a framework on top of Siamese networks to learn representation of users and items without negative samples or labels.
We argue the self-supervised learning techniques that widely used in vision cannot be directly applied in recommendation domain. Hence we design a Siamese network architecture ... | d |
8b9c83d5-6555-4db4-8e64-e1bbfcc98012 | This work is motivated by the biological challenge of spaceflight and the effects of cosmic radiation on human health. Cosmic radiation is able to penetrate thick layers of shielding and body tissue and its carcinogenic nature is a major cause for concern for long-distance space travel [1]}. Understanding the exact mec... | i |
90ff2fb4-3077-4d89-878d-f2a08ab6ef0e | In-vitro experiments on human cells are an alternative data source, but these lack the etiological validity of a complete organism [1]} - especially given how radiation interacts with the different tissues it is penetrating [2]}.
A second alternative is animal models, which can be exposed to radiation that mimics a cos... | i |
4a06aacf-a0a2-4c54-8571-da218c40bc94 | A causal framework has been proposed in the literature based on identifying invariance across different data-generating environments, leading to advances in domain generalization [1]}, [2]}, [3]}The choice of IRM in this work has an exploratory purpose; these results suggest that other causal methods would also work.. ... | i |
bce2aa4b-68af-436d-b65d-54c1a047761e | Our contributions include demonstrating the effectiveness of this framework for identifying invariant relationships present in cross-organism datasets. Further, we provide two open source cross-organism datasets to the community for further research (with matched gene-homologues): one based on acute gamma radiation exp... | i |
3e5c937e-45e3-4efa-9f8e-61c4d9574a68 | We set up our experiment as a binary classification task and trained an IRM model to classify irradiated samples from non-irradiated controls. We consider two types of experiments (augmentation and substitution) for each combined biological dataset presented in Section . In Augmentation Experiments we investigate varyi... | m |
b8e83f4e-a34d-4c87-8d39-449c716c2763 | For each experiment, the output is a ranked list of features (genes), where the order is defined by coefficients extrapolated from the model \(\Phi \) in Equation ; the more a feature is consistently predictive of the target variable across multiple environments, the higher the corresponding coefficient will be.
These... | m |
a366e951-3664-4adb-b718-14d3e8ce1677 | We demonstrate a novel paradigm for generating human-relevant biomedical insights from observational datasets with limited size by leveraging and augmenting with animal model data. Experiments are presented in radiation exposure and carcinogenics,
with a key novel contribution being the identification of SLC8A3 as a po... | d |
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