arXiv subjects

Siqi Wu

Publications and source records attributed to Siqi Wu.

50 records · Page 3Linked to original sources

arXiv2021

Metal-insulator-like transition, superconducting dome and topological electronic structure in Ga-doped Re$_{3}$Ge$_{7}$

Superconductivity frequently appears by doping compounds that show a collective phase transition. So far, however, this has not been observed in topological materials. Here we report the discovery of superconductivity induced by Ga doping in orthorhombic Re$_{3}$Ge$_{7}$, which undergoes a second-order metal-insulator-like transition at $\sim$58 K and is predicted to have a nontrivial band topology. It is found that the substitution of Ga for Ge leads to hole doping in Re$_{3}$Ge$_{7-x}$Ga$_{x}$. As a consequence, the phase transition is gradually suppressed and disappears above $x$ = 0.2. At this $x$ value, superconductivity emerges and $T_{\rm c}$ exhibits a dome-like doping dependence with a maximum value of 3.37 K at $x$ = 0.25. First-principles calculations suggest that the phase transition in Re$_{3}$Ge$_{7}$ is associated with an electronic instability driven by Fermi surface nesting and the nontrival band topology is preserved after Ga doping. Our results indicate that Ga-doped Re$_{3}$Ge$_{7}$ provides a rare opportunity to study the interplay between superconductivity and competing electronic states in a topologically nontrivial system.

cond-mat.supr-con↗

arXiv2021

Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.

cs.SI↗

arXiv2021

AttentionFlow: Visualising Influence in Networks of Time Series

The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements.

cs.SI↗

arXiv2020

Doping-induced superconductivity in the topological semimetal Mo5Si3

Chemical doping of topological materials may provide a possible route for realizing topological superconductivity. However, all such cases known so far are based on chalcogenides. Here we report the discovery of superconductivity induced by Re doping in the topological semimetal Mo$_{5}$Si$_{3}$ with a tetragonal structure. Partial substitution of Re for Mo in Mo$_{5-x}$Re$_{x}$Si$_{3}$ results in an anisotropic shrinkage of the unit cell up to the solubility limit of approximately $x$ = 2. Over a wide doping range (0.5 $\leq$ $x$ $\leq$ 2), these silicides are found to be weakly coupled superconductors with a fully isotropic gap. $T_{\rm c}$ increases monotonically with $x$ from 1.67 K to 5.78 K, the latter of which is the highest among superconductors of the same structural type. This trend in $T_{\rm c}$ correlates well with the variation of the number of valence electrons, and is mainly ascribed to the enhancement of electron-phonon coupling. In addition, band structure calculations reveal that superconducting Mo$_{5-x}$Re$_{x}$Si$_{3}$ exhibits nontrivial band topology characterized by $Z_{2}$ invariants (1;000) or (1;111) depending on the Re doping level. Our results suggest that transition metal silicides are a fertile ground for the exploration of candidate topological superconductors.

cond-mat.supr-con↗

arXiv2020

Type-I superconductivity in noncentrosymmetric NbGe$_{2}$

Single crystals of NbGe$_{2}$ which crystallize in a noncentrosymmetric hexagonal structure with chirality are synthesized and their superconductivity is investigated. Type-I superconductivity is confirmed by dc magnetization, field-induced second-to first-order phase transition in specific heat, and a small Ginzburg-Landau parameter $κ_{GL}=0.12$. The isothermal magnetization measurements show that there is a crossover from type-I to type-II/1 superconductivity with decreasing temperature and an unusually enhanced surface superconducting critical field ($H_{c3}$) is discovered. The band structure calculations indicate the presence of Kramer-Weyl nodes near the Fermi level. These observations demonstrate that NbGe$_{2}$ is an interesting and rare example involving the possible interplay of type-I superconductivity, noncentrosymmetric structure and topological properties.

cond-mat.supr-con↗

arXiv2020

Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model

Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods are supervised and, thus, have two key drawbacks: (1) The data labeling process is often time-consuming and labor-intensive; (2) Current labeling guidelines may not be generalized to future instances because of different language usage and evolving social networks. To address these limitations, this work introduces a principled approach for unsupervised cyberbullying detection. The proposed model consists of two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time. (2) A multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The proposed model jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. Our core contribution is an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.

cs.SI↗

arXiv2020

Variation across Scales: Measurement Fidelity under Twitter Data Sampling

A comprehensive understanding of data quality is the cornerstone of measurement studies in social media research. This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing complete tweet streams, we show that Twitter rate limit message is an accurate indicator for the volume of missing tweets. Sampling also differs significantly across timescales. While the hourly sampling rate is influenced by the diurnal rhythm in different time zones, the millisecond level sampling is heavily affected by the implementation choices. For Twitter entities such as users, we find the Bernoulli process with a uniform rate approximates the empirical distributions well. It also allows us to estimate the true ranking with the observed sample data. For networks on Twitter, their structures are altered significantly and some components are more likely to be preserved. For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect models that rely on these features. This work calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects.

cs.SI↗

arXiv2020

Estimating Attention Flow in Online Video Networks

Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network -- a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention.

cs.SI↗

arXiv2020

Superconductivity in hexagonal Nb-Mo-Ru-Rh-Pd high-entropy alloys

We report the superconducting properties of new hexagonal Nb$_{10+2x}$Mo$_{35-x}$Ru$_{35-x}$Rh$_{10}$Pd$_{10}$ high-entropy alloys (HEAs) (0 $\leq$ $x$ $\leq$ 5). With increasing $x$, the superconducting transition temperature $T_{\rm c}$ shows a maximum of 6.19 K at $x$ = 2.5, while the zero-temperature upper critical field $B_{\rm c2}$(0) increases monotonically, reaching 8.3 T at $x$ = 5. For all $x$ values, the specific heat jump deviates from the Bardeen-Cooper-Schreiffer behavior. In addition, we show that $T_{\rm c}$ of these HEAs is not determined mainly by the density of states at the Fermi level and would be enhanced by lowering the valence electron concentration.

cond-mat.supr-con↗

arXiv2020

Polymorphism and superconductivity in the V-Nb-Mo-Al-Ga high-entropy alloys

High-entropy alloys (HEAs) are at the focus of current research for their diverse properties, including superconductivity and structural polymorphism. However, the polymorphic transition has been observed only in nonsuperconducting HEAs and mostly under high pressure. Here we report the discovery of superconductivity and temperature-driven polymorphism in the (V$_{0.5}$Nb$_{0.5}$)$_{3-x}$Mo$_{x}$Al$_{0.5}$Ga$_{0.5}$ (0.2 $\leq$ $x$ $\leq$ 1.4) HEAs. It is found that the as-cast HEA is of a single body-centered cubic (bcc) structure for $x$ = 0.2 and a mixture of the bcc and A15 structures for higher $x$ values. Upon annealing, the bcc structure undergoes a polymorphic transformation to the A15 one and all HEAs exhibits bulk superconductivity. For $x$ = 0.2, whereas the bcc polymorph is not superconducting down to 1.8 K, the A15 polymorph has a superconducting transition temperature $T_{\rm c}$ of 10.2 K and an estimated zero-temperature upper critical field $B_{\rm c2}$(0) of 20.1 T, both of which are the highest among HEA superconductors. With increasing Mo content $x$, both $T_{\rm c}$ and $B_{\rm c2}$(0) of the A15-type HEAs decrease, yet the large ratio of $B_{\rm c2}$(0)/$T_{\rm c}$ signifies a disorder-induced enhancement of the upper critical field over a wide $x$ range. The decrease in $T_{\rm c}$ is attributed to the decrease in both the electronic specific-heat coefficient and electron-phonon coupling strength. Furthermore, the valence electron count dependence of $T_{\rm c}$, which is different from both the binary A15 and other structurally different HEA superconductors, suggests that $T_{\rm c}$ may be increased further by reducing the number of valence electrons. Our results not only uncover HEA superconductors of a new structural type, but also provide the first example of polymorphism dependent superconductivity in HEAs.

cond-mat.supr-con↗

arXiv2019

Unique Sharp Local Minimum in $\ell_1$-minimization Complete Dictionary Learning

We study the problem of globally recovering a dictionary from a set of signals via $\ell_1$-minimization. We assume that the signals are generated as i.i.d. random linear combinations of the $K$ atoms from a complete reference dictionary $D^*\in \mathbb R^{K\times K}$, where the linear combination coefficients are from either a Bernoulli type model or exact sparse model. First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $\ell_1$ objective function. Our result substantially extends that of Wu and Yu (2015) and allows the combination coefficient to be non-negative. Secondly, we obtain an explicit bound on the region within which the objective value of the reference dictionary is minimal. Thirdly, we show that the reference dictionary is the unique sharp local minimum, thus establishing the first known global property of $\ell_1$-minimization dictionary learning. Motivated by the theoretical results, we introduce a perturbation-based test to determine whether a dictionary is a sharp local minimum of the objective function. In addition, we also propose a new dictionary learning algorithm based on Block Coordinate Descent, called DL-BCD, which is guaranteed to have monotonic convergence. Simulation studies show that DL-BCD has competitive performance in terms of recovery rate compared to many state-of-the-art dictionary learning algorithms.

stat.ML↗

arXiv2018

Beyond Views: Measuring and Predicting Engagement in Online Videos

The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77. Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.

cs.SI↗

arXiv2018

Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos

What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these theoretical results. HIPie -- an interactive visualization system -- is created to fill this gap, by enabling users to reason about the virality and the popularity of online videos. It retrieves the metadata and the past popularity series of Youtube videos, it employs Hawkes Intensity Process, a state-of-the-art online popularity model for explaining and predicting video popularity, and it presents videos comparatively in a series of interactive plots. This system will help both content consumers and content producers in a range of data-driven inquiries, such as to comparatively analyze videos and channels, to explain and predict future popularity, to identify viral videos, and to estimate response to online promotion.

cs.SI↗

arXiv2016

Local identifiability of $l_1$-minimization dictionary learning: a sufficient and almost necessary condition

We study the theoretical properties of learning a dictionary from $N$ signals $\mathbf x_i\in \mathbb R^K$ for $i=1,...,N$ via $l_1$-minimization. We assume that $\mathbf x_i$'s are $i.i.d.$ random linear combinations of the $K$ columns from a complete (i.e., square and invertible) reference dictionary $\mathbf D_0 \in \mathbb R^{K\times K}$. Here, the random linear coefficients are generated from either the $s$-sparse Gaussian model or the Bernoulli-Gaussian model. First, for the population case, we establish a sufficient and almost necessary condition for the reference dictionary $\mathbf D_0$ to be locally identifiable, i.e., a local minimum of the expected $l_1$-norm objective function. Our condition covers both sparse and dense cases of the random linear coefficients and significantly improves the sufficient condition by Gribonval and Schnass (2010). In addition, we show that for a complete $μ$-coherent reference dictionary, i.e., a dictionary with absolute pairwise column inner-product at most $μ\in[0,1)$, local identifiability holds even when the random linear coefficient vector has up to $O(μ^{-2})$ nonzeros on average. Moreover, our local identifiability results also translate to the finite sample case with high probability provided that the number of signals $N$ scales as $O(K\log K)$.

stat.ML↗