Miracle-dz/UniFAR-FTUs huggingface dataset:
Seismic Interferometry in Image Domain Based on Reverse Time Migration for Passive Seismic Monitoring

I propose a direct migration method for passive seismic data based on a reverse time migration combined with a concept of the seismic interferometry. The subsurface image is obtained by three steps in this method which is recognized as the seismic interferometry in the image domain; the first is model-based wavefield extrapolation of time-windowed passive seismic signals, the second is crosscorrelation of the forward and backward extrapolated wavefields in the image domain, and the third is summation of the correlated image for all seismic events or all time windows. As a result of the modified imaging condition, surface related multiple reflections in the passive seismic data are focused on the reflection points. This image domain interferometry has potential to be an efficient processing method of continuous recording data. The daily subsurface condition can be updated by accumulating new reflection images with the continuous monitoring. A numerical experiment simulating local earthquakes with SEG/EAGE overthrust model validates the imaging algorithm with the image domain interferometry. Another numerical simulation result shows that this method has applicability to detect time-lapse velocity changes, though difference by the source distribution change is not negligible.
An efficient dynamic programming algorithm for the generalized LCS problem with multiple substring inclusive constraints

In this paper, we consider a generalized longest common subsequence problem with multiple substring inclusive constraints. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a set of $d$ constraints $P=\{P_1,\cdots,P_d\}$ of total length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ including each of constraint string in $P$ as a substring and the length of $Z$ is maximized. A new dynamic programming solution to this problem is presented in this paper. The correctness of the new algorithm is proved. The time complexity of our algorithm is $O(d2^dnmr)$. In the case of the number of constraint strings is fixed, our new algorithm for the generalized longest common subsequence problem with multiple substring inclusive constraints requires $O(nmr)$ time and space.
Learning-Based Resource Allocation Scheme for TDD-Based CRAN System

Explosive growth in the use of smart wireless devices has necessitated the provision of higher data rates and always-on connectivity, which are the main motivators for designing the fifth generation (5G) systems. To achieve higher system efficiency, massive antenna deployment with tight coordination is one potential strategy for designing 5G systems, but has two types of associated system overhead. First is the synchronization overhead, which can be reduced by implementing a cloud radio access network (CRAN)-based architecture design, that separates the baseband processing and radio access functionality to achieve better system synchronization. Second is the overhead for acquiring channel state information (CSI) of the users present in the system, which, however, increases tremendously when instantaneous CSI is used to serve high-mobility users. To serve a large number of users, a CRAN system with a dense deployment of remote radio heads (RRHs) is considered, such that each user has a line-of-sight (LOS) link with the corresponding RRH. Since, the trajectory of movement for high-mobility users is predictable; therefore, fairly accurate position estimates for those users can be obtained, and can be used for resource allocation to serve the considered users. The resource allocation is dependent upon various correlated system parameters, and these correlations can be learned using well-known \emph{machine learning} algorithms. This paper proposes a novel \emph{learning-based resource allocation scheme} for time division duplex (TDD) based 5G CRAN systems with dense RRH deployment, by using only the users' position estimates for resource allocation, thus avoiding the need for CSI acquisition. This reduces the overall system overhead significantly, while still achieving near-optimal system performance; thus, better (effective) system efficiency is achieved. (See the paper for full abstract)
Kernel method based on non-linear coherent states in quantum feature space

In this paper, by mapping datasets to a set of non-linear coherent states, the process of encoding inputs in quantum states as a non-linear feature map is re-interpreted. As a result of the fact that the radial basis function is recovered when data is mapped to a complex Hilbert state represented by coherent states, non-linear coherent states can be considered as a natural generalisation of the associated kernels. In this paper, as an example of kernels based on non-linear coherent states, we propose kernel functions based on generalized hypergeometric functions, as orthogonal polynomial functions. The suggested kernel is implemented with the support vector machine (SVM) on two well known datasets (make_circles, and make_moons) and outperforms the baselines, even when the level of noise is high. In addition, we study the impact of the geometrical properties of the feature space, obtained by the non-linear coherent states, on the SVM classification task, by considering the Fubini–Study metric of the associated coherent states.
Joint scheduling and resource allocation in uplink OFDM systems for broadband wireless access networks

Orthogonal frequency division multiplexing (OFDM) with dynamic scheduling and resource allocation is a key component of most emerging broadband wireless access networks such as WiMAX and LTE (long term evolution) for 3GPP. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (i) the discrete nature of subchannel assignments, and (ii) the heterogeneity of the users' subchannel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem and solved using a dual decomposition approach. This optimal solution has prohibitively high computational complexity but reveals guiding principles that we use to generate lower complexity sub-optimal algorithms. We analyze the complexity and compare the performance of these algorithms via extensive simulations.
A Variable-Splitting Augmented Lagrangian Framework

We consider a class of sparse learning problems in high dimen sional feature space regularized by a structured sparsity-inducing norm that incorporates pri or knowledge of the group structure of the features. Such problems often pose a considerable chall enge to optimization algorithms due to the non-smoothness and non-separability of the regulari zation term. In this paper, we focus on two commonly adopted sparsity-inducing regularization erms, the overlapping Group Lasso penaltyl1/l2-norm and thel1/l∞-norm. We propose a unified framework based on the augmented Lagrangian method, under which problems with both types of r egularization and their variants can be efficiently solved. As one of the core building-blocks of this framework, we develop new algorithms using a partial-linearization/splitting tech nique and prove that the accelerated versions of these algorithms require O( 1 ε ) iterations to obtain anε-optimal solution. We compare the performance of these algorithms against that of the alterna ing direction augmented Lagrangian and FISTA methods on a collection of data sets and apply them t o two real-world problems to compare the relative merits of the two norms.
An efficient distributed learning algorithm based on effective local functional approximations

Scalable machine learning over big data is an important problem that is receiving a lot of attention in recent years. On popular distributed environments such as Hadoop running on a cluster of commodity machines, communication costs are substantial and algorithms need to be designed suitably considering those costs. In this paper we give a novel approach to the distributed training of linear classifiers (involving smooth losses and L2 regularization) that is designed to reduce the total communication costs. At each iteration, the nodes minimize locally formed approximate objective functions; then the resulting minimizers are combined to form a descent direction to move. Our approach gives a lot of freedom in the formation of the approximate objective function as well as in the choice of methods to solve them. The method is shown to have $O(log(1/\epsilon))$ time convergence. The method can be viewed as an iterative parameter mixing method. A special instantiation yields a parallel stochastic gradient descent method with strong convergence. When communication times between nodes are large, our method is much faster than the Terascale method (Agarwal et al., 2011), which is a state of the art distributed solver based on the statistical query model (Chuet al., 2006) that computes function and gradient values in a distributed fashion. We also evaluate against other recent distributed methods and demonstrate superior performance of our method.
Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators

Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data where the observations show temporal dependencies. Several studies have analysed how diﬀerent performance estimation methods compare with each other for approximating the true loss incurred by a given forecasting model. However, these studies do not address how the estimators behave for model selection: the ability to select the best solution among a set of alternatives. We address this issue and compare a set of estimation methods for model selection in time series forecasting tasks. We attempt to answer two main questions: (i) how often is the best possible model selected by the estimators; and (ii) what is the performance loss when it does not. We empirically found that the accuracy of the estimators for selecting the best solution is low, and the overall forecasting performance loss associated with the model selection process ranges from 1.2% to 2.3%. We also discovered that some factors, such as the sample size, are important in the relative performance of the estimators.
A New Bound on the Performance of the Bandwidth Puzzle

A bandwidth puzzle was recently proposed to defend against colluding adversaries in peer-to-peer networks. The colluding adversaries do not do actual work but claim to have uploaded contents for each other to gain free credits from the system. The bandwidth puzzle guarantees that if the adversaries can solve the puzzles, they must have spent substantial bandwidth, the size of which is comparable to the size of the contents they claim to have uploaded for each other. Therefore, the puzzle discourages the collusion. In this paper, we study the performance of the bandwidth puzzle and give a lower bound on the average number of bits the adversaries must receive to be able to solve the puzzles with a certain probability. We show that our bound is tight in the sense that there exists a strategy to approach this lower bound asymptotically within a small factor. The new bound gives better security guarantees than the existing bound, and can be used to guide better choices of puzzle parameters to improve the system performance.
Capacity Bounds for Vertically-Drifted First Arrival Position Channels under a Second-Moment Constraint

—In this paper, we delve into the capacity problem of additive vertically-drifted ﬁrst arrival position noise channel, which models a communication system where the position of molecules is harnessed to convey information. Drawing inspira-tion from the principles governing parallel Gaussian channels, we examine this capacity problem within the context of a second-moment constraint on input distributions. We offer analytical upper and lower bounds on this capacity for a three-dimensional spatial setting. This is achieved through a meticulous analysis of the characteristic function coupled with an investigation into the stability properties. The results of this study contribute to the ongoing effort to understand the fundamental limits of molecular communication systems.
Self-View Grounding Given a Narrated 360 Degree Video

Narrated 360◦ videos are typically provided in many touring scenarios to mimic real-world experience. However, previous work has shown that smart assistance (i.e., providing visual guidance) can significantly help users to follow the Normal Field of View (NFoV) corresponding to the narrative. In this project, we aim at automatically grounding the NFoVs of a 360◦ video given subtitles of the narrative (referred to as “NFoV-grounding”). We propose a novel Visual Grounding Model (VGM) to implicitly and efficiently predict the NFoVs given the video content and subtitles. Specifically, at each frame, we efficiently encode the panorama into feature map of candidate NFoVs using a Convolutional Neural Network (CNN) and the subtitles to the same hidden space using an RNN with Gated Recurrent Units (GRU). Then, we apply soft-attention on candidate NFoVs to trigger sentence decoder aiming to minimize the reconstruct loss between the generated and given sentence. Finally, we obtain the NFoV as the candidate NFoV with the maximum attention without any human supervision. To train VGM more robustly, we also generate a reverse sentence conditioning on one minus the soft-attention such that the attention focuses on candidate NFoVs less relevant to the given sentence. The negative log reconstruction loss of the reverse sentence (referred to as “irrelevant loss”) is jointly minimized to encourage the reverse sentence to be different from the given sentence. To evaluate our method, we collect the first narrated 360◦ videos dataset and achieve state-of-the-art NFoV-grounding performance. Introduction Thanks to the availability of consumer-level 360◦ video cameras, many 360◦ videos are shared on websites like YouTube and Facebook. Among these videos, a subset of them is narrated with natural language phrases describing the video content. For instance, many videos consist of the guided tour of real estates (e.g., houses and apartments) or tourist locations. Intuitively, a 360◦ touring video provides viewers the freedom to follow the narrative and select Normal Field of Views (NFoVs) provided by typical video players (see Fig. 1). However, a study in Lin et al. (2017) suggests that visual guidance (i.e., assistance through visual indicator) is preferred to assist viewers to follow the NFoV described by the narrative. In order to provide the visual Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. t t-1 t+1 NFOV NFOV
High-Quality Face Image SR Using Conditional Generative Adversarial Networks

We propose a novel single face image super-resolution method, which named Face Conditional Generative Adversarial Network(FCGAN), based on boundary equilibrium generative adversarial networks. Without taking any facial prior information, our method can generate a high-resolution face image from a low-resolution one. Compared with existing studies, both our training and testing phases are end-to-end pipeline with little pre/post-processing. To enhance the convergence speed and strengthen feature propagation, skip-layer connection is further employed in the generative and discriminative networks. Extensive experiments demonstrate that our model achieves competitive performance compared with state-of-the-art models.
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph-toplogies

Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels. This 4-neighbourhood topological simplicity makes the application of convolutional masks straightforward for time series data, such as video applications, but many high-dimensional time series data are not organised in regular lattices, and instead values may have adjacency relationships with non-trivial topologies, such as small-world networks or trees. In our application case, human kinematics, it is currently unclear how to generalise convolutional kernels in a principled manner. Therefore we define and implement here a framework for general graph-structured CNNs for time series analysis. Our algorithm automatically builds convolutional layers using the specified adjacency matrix of the data dimensions and convolutional masks that scale with the hop distance. In the limit of a lattice-topology our method produces the well-known image convolutional masks. We test our method first on synthetic data of arbitrarily-connected graphs and human hand motion capture data, where the hand is represented by a tree capturing the mechanical dependencies of the joints. We are able to demonstrate, amongst other things, that inclusion of the graph structure of the data dimensions improves model prediction significantly, when compared against a benchmark CNN model with only time convolution layers.
Glyph: Symbolic Regression Tools

We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at http://github.com/ambrosys/glyph . Domain experts are be able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a generic interface design. Glyph is available on PyPI and Github.
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition

Dynamic: Our ATT-G and SEMAN-G negative generators are built upon Dynamic [10]. Dynamic [10] calibrates few-shot prototypes using base class classifier. We reimplemented Dynamic using the standard Transformer attention block. We compare this baseline in Tab. 3 in the main paper for GFSOR performance (i.e., DynamicFSL) , where we calculate its accuracy in the normal way, while for AUROC, we take argmaxy∈ Cf∪C∗p(x|y) as the rejection score. We also evaluate its performance on CIFAR-FS and FC-100 in Tab. 6, where we take argmaxy∈ Cf p(x|y) as the rejection score.
Error-Correcting Tournaments

We present a family of pairwise tournaments reducing k-class classification to binary classification. These reductions are provably robust against a constant fraction of binary errors. The results improve on the PECOC construction (15) with an exponential improvement in compu tation, fromO(k) to O(log 2 k), and the removal of a square root in the regret dependence, matching the best p os ible computation and regret up to a constant.
Decomposition Methods for Large Scale LP Decoding

When binary linear error-correcting codes are used over symmetric channels, a relaxed version of the maximum likelihood decoding problem can be stated as a linear program (LP). This LP decoder can be used to decode error-correcting codes at bit-error-rates comparable to state-of-the-art belief propagation (BP) decoders, but with significantly stronger theoretical guarantees. However, LP decoding when implemented with standard LP solvers does not easily scale to the block lengths of modern error correcting codes. In this paper, we draw on decomposition methods from optimization theory, specifically the alternating direction method of multipliers (ADMM), to develop efficient distributed algorithms for LP decoding. The key enabling technical result is a “two-slice” characterization of the parity polytope, the polytope formed by taking the convex hull of all codewords of the single parity check code. This new characterization simplifies the representation of points in the polytope. Using this simplification, we develop an efficient algorithm for Euclidean norm projection onto the parity polytope. This projection is required by the ADMM decoder and its solution allows us to use LP decoding, with all its theoretical guarantees, to decode large-scale error correcting codes efficiently. We present numerical results for LDPC codes of lengths more than 1000. The waterfall region of LP decoding is seen to initiate at a slightly higher SNR than for sum-product BP, however an error floor is not observed for LP decoding, which is not the case for BP. Our implementation of LP decoding using the ADMM executes as fast as our baseline sum-product BP decoder, is fully parallelizable, and can be seen to implement a type of message-passing with a particularly simple schedule.
Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE

Variational auto-encoder(VAE) is an effective neural network architecture to disentangle a speech utterance into speaker identity and linguistic content latent embeddings, then generate an utterance for a target speaker from that of a source speaker. This is possible by concatenating the identity embedding of the target speaker and the content embedding of the source speaker uttering a desired sentence. In this work, we found a suitable location of VAE’s decoder to add a self-attention layer for incorporating non-local information in generating a converted utterance and hiding the source speaker’s identity. In experiments of zero-shot many-to-many voice conversion task on VCTK data set, the self-attention layer enhances speaker classiﬁcation accuracy on unseen speakers by 27% while increasing the decoder parameter size by 12%. The voice quality of converted utterance degrades by merely 3% measured by the MOSNet scores. To reduce over-ﬁtting and generalization error, we further applied a relaxed group-wise splitting method in network training and achieved a gain of speaker classiﬁcation accuracy on unseen speakers by 46% while maintaining the conversion voice quality in terms of MOSNet scores. Our encouraging ﬁndings point to future research on integrating more variety of attention structures in VAE framework for advancing zero-shot many-to-many voice conversions. 1 2
Optimal Distributed Scheduling in Wireless Networks Under the SINR Interference Model

In wireless networks, the design of radio resource sharing mechanisms is complicated by the complex interference constraints among the various links. In their seminal paper (IEEE Trans. Autom. Control, vol. 37, no. 12, pp. 1936-1948), Tassiulas and Ephremides introduced Maximum Weighted Scheduling, a centralized resource sharing algorithm, and proved its optimality. Since then, there have been extensive research efforts to devise distributed implementations of this algorithm. Recently, distributed adaptive CSMA scheduling schemes have been proposed and shown to be optimal, without the need of message passing among transmitters. However, their analysis relies on the assumption that interference can be accurately modeled by a simple interference graph. In this paper, we consider the more realistic and challenging signal-to-interference-plus-noise ratio (SINR) interference model. We present distributed scheduling algorithms that: 1) are optimal under the SINR interference model; and 2) do not require any message passing. These algorithms are based on a combination of a simple and efficient power allocation strategy referred to as Power Packing and randomization techniques. The optimality of our algorithms is illustrated in various traffic scenarios using numerical experiments.