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We study the problem of online network change point detection. In this setting, a collection of independent Bernoulli networks is collected sequentially, and the underlying distributions change when a change point occurs. The goal is to detect the change point as quickly as possible, if it exists, subject to a constraint on the number or probability of false alarms. In this paper, on the detection delay, we establish a minimax lower bound and two upper bounds based on NP-hard algorithms and polynomial-time algorithms, i.e., \[ \mbox{detection delay} \begin{cases} \gtrsim \log(1/\alpha) \frac{\max\{r^2/n, \, 1\}}{\kappa_0^2 n \rho},\\ \lesssim \log(\Delta/\alpha) \frac{\max\{r^2/n, \, \log(r)\}}{\kappa_0^2 n \rho}, & \mbox{with NP-hard algorithms},\\ \lesssim \log(\Delta/\alpha) \frac{r}{\kappa_0^2 n \rho}, & \mbox{with polynomial-time algorithms}, \end{cases} \] where $\kappa_0, n, \rho, r$ and $\alpha$ are the normalised jump size, network size, entrywise sparsity, rank sparsity and the overall Type-I error upper bound. All the model parameters are allowed to vary as $\Delta$, the location of the change point, diverges. The polynomial-time algorithms are novel procedures that we propose in this paper, designed for quick detection under two different forms of Type-I error control. The first is based on controlling the overall probability of a false alarm when there are no change points, and the second is based on specifying a lower bound on the expected time of the first false alarm. Extensive experiments show that, under different scenarios and the aforementioned forms of Type-I error control, our proposed approaches outperform state-of-the-art methods.

Phase transitions and critical phenomena, which are dominated by fluctuations and correlations, are one of the fields replete with physical paradigms and unexpected discoveries. Especially for two-dimensional magnetism, the limitation of the Ginzburg criterion leads to enhanced fluctuations breaking down the mean-field theory near a critical point. Here, by means of magnetic resonance, we investigate the behavior of critical fluctuations in the two-dimensional ferromagnetic insulators $\rm CrXTe_3 (X=Si, Ge)$. After deriving the classical and quantum models of magnetic resonance, we deem the dramatic anisotropic shift of the measured $g$ factor to originate from fluctuations with anisotropic interactions. The deduction of the $g$ factor behind the fluctuations is consistent with the spin-only state (${g\approx}$ 2.050(10) for $\rm CrSiTe_3$ and 2.039(10) for $\rm CrGeTe_3$). Furthermore, the abnormal enhancement of $g$ shift, supplemented by specific heat and magnetometry measurements, suggests that $\rm CrSiTe_3$ exhibits a more typical two-dimensional nature than $\rm CrGeTe_3$ and may be closer to the quantum critical point.

In this paper, we design and analyze two new methods based on additive average Schwarz -- AAS method introduced in \cite{MR1943457}. The new methods design for elliptic problems with highly heterogeneous coefficients. The methods are of the non-overlapping type, and the subdomain interactions obtain via the coarse space. The first method is the minimum energy Schwarz -- MES method. MES has the minimum energy for the coarse space with constant extension inside each subdomain. The condition number of the MES method is always smaller than in the AAS method. The second class of methods is the non-overlapping spectral additive Schwarz -- NOSAS methods based on low-rank discrete energy harmonic extension in each subdomain. To achieve the low-rank, we solve a generalized eigenvalue problem in each subdomain. NOSAS have the minimum energy for a given rank of the coarse space. The condition number of the NOSAS methods does not depend on the coefficients. Additionally, the NOSAS methods have good parallelization properties. The size of the global problem is equal to the total number of eigenvalues chosen in each subdomain. It is only related to the number of high-permeable islands that touch the subdomains' interface.

We study a functional linear regression model that deals with functional responses and allows for both functional covariates and high-dimensional vector covariates. The proposed model is flexible and nests several functional regression models in the literature as special cases. Based on the theory of reproducing kernel Hilbert spaces (RKHS), we propose a penalized least squares estimator that can accommodate functional variables observed on discrete sample points. Besides a conventional smoothness penalty, a group Lasso-type penalty is further imposed to induce sparsity in the high-dimensional vector predictors. We derive finite sample theoretical guarantees and show that the excess prediction risk of our estimator is minimax optimal. Furthermore, our analysis reveals an interesting phase transition phenomenon that the optimal excess risk is determined jointly by the smoothness and the sparsity of the functional regression coefficients. A novel efficient optimization algorithm based on iterative coordinate descent is devised to handle the smoothness and group penalties simultaneously. Simulation studies and real data applications illustrate the promising performance of the proposed approach compared to the state-of-the-art methods in the literature.

This work present a music dataset named MusicTM-Dataset, which is utilized in improving the representation learning ability of different types of cross-modal retrieval (CMR). Little large music dataset including three modalities is available for learning representations for CMR. To collect a music dataset, we expand the original musical notation to synthesize audio and generated sheet-music image, and build musical notation based sheet-music image, audio clip and syllable-denotation text as fine-grained alignment, such that the MusicTM-Dataset can be exploited to receive shared representation for multimodal data points. The MusicTM-Dataset presents 3 kinds of modalities, which consists of the image of sheet-music, the text of lyrics and synthesized audio, their representations are extracted by some advanced models. In this paper, we introduce the background of music dataset and express the process of our data collection. Based on our dataset, we achieve some basic methods for CMR tasks. The MusicTM-Dataset are accessible in https: //github.com/dddzeng/MusicTM-Dataset., Comment: 12 pages, 5 figures, 2 tables

In this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin.ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform can be used for the enhancement of the social networking user experience as well as for content marketing routines. Guided by the MBTI personality type, automatically derived from a user social network content, SoMin.ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns. The real-time user feedback to the platform via the platform's GUI fine-tunes the content generation model and the evaluation results demonstrate the promising performance of the proposed multi-view personality profiling framework when being applied in the content generation scenario. By leveraging content generation at a large scale, marketers will be able to execute more effective digital marketing campaigns at a lower cost., Comment: WSDM 2021 - Demonstration

Training a robust system, e.g.,Speech to Text (STT), requires large datasets. Variability present in the dataset such as unwanted nuisances and biases are the reason for the need of large datasets to learn general representations. In this work, we propose a novel approach to induce invariance using adversarial forgetting (AF). Our initial experiments on learning invariant features such as accent on the STT task achieve better generalizations in terms of word error rate (WER) compared to the traditional models. We observe an absolute improvement of 2.2% and 1.3% on out-of-distribution and in-distribution test sets, respectively.

Manipulating materials with atomic-scale precision is essential for the development of next-generation material design toolbox. Tremendous efforts have been made to advance the compositional, structural, and spatial accuracy of material deposition and patterning. The family of 2D materials provides an ideal platform to realize atomic-level material architectures. The wide and rich physics of these materials have led to fabrication of heterostructures, superlattices, and twisted structures with breakthrough discoveries and applications. Here, we report a novel atomic-scale material design tool that selectively breaks and forms chemical bonds of 2D materials at room temperature, called atomic-layer substitution (ALS), through which we can substitute the top layer chalcogen atoms within the 3-atom-thick transition-metal dichalcogenides using arbitrary patterns. Flipping the layer via transfer allows us to perform the same procedure on the other side, yielding programmable in-plane multi-heterostructures with different out-of-plane crystal symmetry and electric polarization. First-principle calculations elucidate how the ALS process is overall exothermic in energy and only has a small reaction barrier, facilitating the reaction to occur at room temperature. Optical characterizations confirm the fidelity of this design approach, while TEM shows the direct evidence of Janus structure and suggests the atomic transition at the interface of designed heterostructure. Finally, transport and Kelvin probe measurements on MoXY (X,Y=S,Se; X and Y corresponding to the bottom and top layers) lateral multi-heterostructures reveal the surface potential and dipole orientation of each region, and the barrier height between them. Our approach for designing artificial 2D landscape down to a single layer of atoms can lead to unique electronic, photonic and mechanical properties previously not found in nature.

In this paper, we propose a technique to address the most challenging aspect of algorithmic songwriting process, which enables the human community to discover original lyrics, and melodies suitable for the generated lyrics. The proposed songwriting system, Automatic Neural Lyrics and Melody Composition (AutoNLMC) is an attempt to make the whole process of songwriting automatic using artificial neural networks. Our lyric to vector (lyric2vec) model trained on a large set of lyric-melody pairs dataset parsed at syllable, word and sentence levels are large scale embedding models enable us to train data driven model such as recurrent neural networks for popular English songs. AutoNLMC is a encoder-decoder sequential recurrent neural network model consisting of a lyric generator, a lyric encoder and melody decoder trained end-to-end. AutoNLMC is designed to generate both lyrics and corresponding melody automatically for an amateur or a person without music knowledge. It can also take lyrics from professional lyric writer to generate matching melodies. The qualitative and quantitative evaluation measures revealed that the proposed method is indeed capable of generating original lyrics and corresponding melody for composing new songs., Comment: 15 pages

This paper reviews recent developments in fundamental limits and optimal algorithms for change point analysis. We focus on minimax optimal rates in change point detection and localisation, in both parametric and nonparametric models. We start with the univariate mean change point analysis problem and review the state-of-the-art results in the literature. We then move on to more complex data types and investigate general principles behind the optimal procedures that lead to minimax rate-optimal results.