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Abstract Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in long-run asset co-movement patterns which expose portfolios to rapid and devastating collapses in value. These disruptions are linked to changes in the structure of market wide stock correlations which increase the risk of high volatility shocks. The structure of these co-movements can be described as a network where companies are represented by nodes while edges capture correlations between their price movements. Co-movement breakdowns then manifest as abrupt changes in the topological structure of this network. Measuring the scale of this change and learning a timely indicator of breakdowns is central in understanding both financial stability and volatility forecasting. We propose to use the edge reconstruction accuracy of a graph auto-encoder as an indicator for how homogeneous connections between assets are, which we use, based on the literature of financial network analysis, as a proxy to infer market volatility. We show, through our experiments on the Standard and Poor’s index over the 2015-2022 period, that the reconstruction errors from our model correlate with volatility spikes and can be used to improve out-of-sample autoregressive modeling of volatility. Our results demonstrate that market instability can be predicted by changes in the homogeneity in connections of the financial network which expands the understanding of instability in the stock market. We discuss the implications of this graph machine learning-based volatility estimation for policy targeted at ensuring financial market stability.

Abstract The photonic Ising machine is a new paradigm of optical computing that takes advantage of the unique properties of light wave propagation, parallel processing, and low-loss transmission. Thus, the process of solving combinatorial optimization problems can be accelerated through photonic/optoelectronic devices, but implementing photonic Ising machines that can solve arbitrary large-scale Ising problems with fast speed remains challenging. In this work, we have proposed and demonstrated the Phase Encoding and Intensity Detection Ising Annealer (PEIDIA) capable of solving arbitrary Ising problems on demand. The PEIDIA employs the heuristic algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation by encoding the Ising spins on the phase term of the optical field and performing intensity detection during the solving process. As a proof of principle, several 20 and 30-spin Ising problems have been solved with high ground state probability (≥0.97/0.85 for the 20/30-spin Ising model).

Objectives: This study aimed to analyze the possible role of rDNA copy number variation in the association between hexavalent chromium [Cr (VI)] exposure and semen quality in semen donors and further confirm this association in mice. Methods: In this cross-sectional study, whole blood and semen samples were collected from 155 semen donors in the Zhejiang Human Sperm Bank from January 1st to April 31st, 2021. Adult C57BL/6 J male mice were treated with different doses of Cr (VI) (0, 10, or 15 mg/kg b.w./day). Semen quality, including semen volume, total spermatozoa count, sperm concentration, progressive motility, and total motility, were analyzed according to the WHO laboratory manual. Cr concentration was detected using inductively coupled plasma mass spectrometry. The rDNA copy number was measured using qPCR. Results: In semen donors, whole blood Cr concentration was negatively associated with semen concentration and total sperm counts. Semen 5 S and 45 S rDNA copy numbers were negatively associated with whole blood Cr concentration and whole blood 5.8 S rDNA copy number was negatively associated with semen Cr concentration. In mice, Cr (VI) damaged testicular tissue, decreased semen quality, and caused rDNA copy number variation. Semen quality was related to the rDNA copy number in whole blood, testicular tissue, and semen samples in mice. Conclusion: Cr (VI) was associated with decreased semen quality in semen donors and mice. Our findings suggest an in-depth analysis of the role of the rDNA copy number variation in the Cr (VI)-induced impairment of semen quality.

Abstract Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

An 1500 nm thick collector layer is adopted in the uni-travelling-carrier photodiode (UTC-PD) to reduce the PD's capacitance, while the PD's diameter is set to be 20 μm for better saturation performance and optical coupling characteristic of a signal from a single mode fiber. By optimizing biasing voltage and input light intensity, the electric field intensity in the collector layer would be controlled to make the photo-generated electron in the UTC-PD transiting at around its peak drift velocity in the collector layer and at the same time to make the UTC-PD obtaining it minimum capacitance. Together with the optimization of the doping distribution in UTC-PD's cliff layer and its collector layer, the UTC-PD's high-speed performance and saturation performance can be further improved. An optimized 20 μm diameter UTC-PD with a simulated 106 GHz 3 dB bandwidth and 21.02 dBm output RF power at 25 GHz is designed. It proposes a discussion to realize high speed, high saturation performance UTC-PD together with high coupling efficiency from a single mode optical fiber by bringing the electric field determined drift velocity performance of its uni-traveling- carrier, the electron, into full play.

A matrix-vector multiplication (MVM) optical signal processor based on mode division multiplexing (MDM) was proposed and demonstrated in the current work, which is composed of a mode multiplexer, a multimode beam splitter, a mode demultiplexer, a modulator array and combiners. In addition, the characteristics of MDM obviate the need for multiple wavelengths and therefore multiple laser light sources are unneeded, which greatly reduces the complexity and cost. A 4 × 4 MDM-MVM was realized on a standard silicon-on-insulator (SOI) platform. Combined with the off-chip light source and photodetectors (PDs), 4-level modulation has been demonstrated, and each level of the output signal could represent 2 bits of information.

We have designed the MUTC-PD with an optimized thickness of cliff layer to pre-distort the electric field at the front side of the collection layer. With the optimized MUTC-PD design, the collapse of the electric field will be greatly suppressed, and the electrons in its collection layer will gradually reach their peak velocity with the growing incident light power. Moreover, as the incident light intensity increases, the differential capacitance also declines, thus the total bandwidth grows. It will make the MUTC-PD achieve high-speed and high-power response performance simultaneously. Based on simulation, for 16μm MUTC-PD with a 70 nm cliff layer, the maximum 3 dB bandwidth at −5 V is 137 GHz, compared with 64 GHz for the MUTC-PD with a 30 nm cliff layer. The saturation RF output power is 27.4 dBm at 60 GHz.

Large amounts of interaction data are collected by messaging apps, mobile phone carriers, and social media. Creţu et al. propose a behavioral profiling attack model and show that the stability of people’s interaction networks over time can allow individuals to be re-identified in interaction datasets.

The framework provides a novel pipeline for action recognition. The action recognition task classifies the action label of the scene. High-speed cameras are commonly used to generate high frame-rate videos for capturing sufficient motion information. However, the data volume would be the bottleneck of the system. With the insight that the discrete cosine transform (DCT) of video signals reveals the motion information remarkably, instead of obtaining video data as with traditional cameras, the proposed method directly captures a DCT spectrum of video in a single shot through optical pixel-wise encoding. Considering that video signals are sparsely distributed in the DCT domain, a learning-based frequency selector is designed for pruning the trivial frequency channels of the spectrum. An opto-electronic neural network is designed for action recognition from a single coded spectrum. The optical encoder generates the DCT spectrum, and the rest of the network jointly optimizes the frequency selector and classification model simultaneously. Compared to conventional video-based action recognition methods, the proposed method achieves higher accuracy with less data, less communication bandwidth, and less computational burden. Both simulations and experiments demonstrate that the proposed method has superior action recognition performance. To the best of our knowledge, this is the first work investigating action recognition in the DCT domain.

Abstract In recent years, the explosive development of artificial intelligence implementing by artificial neural networks (ANNs) creates inconceivable demands for computing hardware. However, conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules. Fortunately, analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks. In this article, the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1. In Chapter 2, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine. Each direction has been explicitly summarized and discussed. The last chapter explains the prospects and the new challenges of analog optical computing.

Urban income segregation is often discussed in terms of where people live. Here, the authors show that the way people experience income segregation is also associated with their mobility patterns and the places they visit.

Abstract In an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period of 7 years. We investigate the propagation of such sentiment in company networks and evaluate the associated market movements in terms of stock price and volatility. Our results suggest that, in certain sectors, strong media sentiment towards one company may indicate a significant change in media sentiment towards related companies measured as neighbours in a financial network constructed from news co-occurrence. Furthermore, there exists a weak but statistically significant association between strong media sentiment and abnormal market return as well as volatility. Such an association is more significant at the level of individual companies, but nevertheless remains visible at the level of sectors or groups of companies.

Abstract Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications.

We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing. As the choice of kernel function and associated bandwidth heavily affect the resulting output, our approach mitigates these issues. Furthermore, we prove that kernel smoothing biases the moments of the spectral density. Our approach can be seen as an information-theoretically optimal approach to learning a smooth graph spectral density, which fully respects moment information. The proposed method has a computational cost linear in the number of edges, and hence can be applied even to large networks with millions of nodes. We showcase the approach on problems of graph similarity learning and counting cluster number in the graph, where the proposed method outperforms existing iterative spectral approaches on both synthetic and real-world graphs.

Abstract Customer retention is crucial in a variety of businesses as acquiring new customers is often more costly than keeping the current ones. As a consequence, churn prediction has attracted great attention from both the business and academic worlds. Traditional efforts in the financial domain mainly focus on domain specific variables such as product ownership or service usage aggregation, however, without considering dynamic behavioral patterns of customers’ financial transactions. In this paper, we attempt to fill in this gap by investigating the spatio-temporal patterns and entropy of choices underlying the customers’ financial decisions, and their relations to customer churning activities. Inspired by previous works in the emerging field of computational social science, we built a prediction model based on spatio-temporal and choice behavioral traits using individual transaction records. Our results show that proposed dynamic behavioral models could predict churn decisions significantly better than traditionally considered factors such as demographic-based features, and that this effect remains consistent across multiple data sets and various churn definitions. We further study the relative importance of the various behavioral features in churn prediction, and how the predictive power varies across different demographic groups. More generally, the proposed features can also be applied to churn prediction in other domains where spatio-temporal behavioral data are available.

Abstract Societal unrest and similar events are important for societies, but it is often difficult to quantify their effects on individuals, hindering a timely and effective policy-making in emergencies and in particular localized social shocks such as protests. Traditionally, effects are assessed through economic indicators or surveys with relatively low temporal and spatial resolutions. In this work, we compute two behavioral indexes, based on the use of credit card transaction data, for measuring the economic effects of a series of protests on consumer actions and personal consumption. Using data from a metropolitan area in an OECD country, we show that protests affect consumers’ shopping frequency and spending, but in noticeably different ways. The effects show strong temporal and spatial patterns, vary between neighborhoods and customers of different socio-demographical characteristics as well as between merchants of different categories, and suggest interesting subtleties in purchase behavior such as displaced or delayed shopping activities. Our method can generally serve for the real-time monitoring of the effects of major social shocks or events on urban economy and consumer sentiment, providing high-resolution and cost-effective measurement tools to complement traditional economic indicators.

The smart factory of Industry 4.0 has been regarded as a solution for handling the increasing production complexity caused by growing global economy and demand for customized products. Besides, it will make the interactions between humans, machines, and products become a highly competitive area for market capitalization in the near feature. Nowadays, cloud computing with the high performance of computing and self-service access plays an important role in realizing smart factor. To minimize the overall cost of company in a heterogeneous cloud environment, including multiple public clouds, while ensuring a proper level of quality-of-service, task placement across multiple public clouds is a critical problem, where task deadlines and long-haul data transmission costs between smart factory and different clouds must be considered. We formulate this task placement problem as an integer linear program (ILP) to minimize company cost under the task deadline constraint. With extensive simulations, we evaluate the performance of our proposed ILP model in heterogeneous public clouds with finite and infinite resources.

Social behaviours emerge from the exchange of information among individuals—constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.

Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its equivalence to constrained Bayesian variational inference and demonstrate its superiority over existing approaches in two applications, namely, fast log determinant estimation and information-theoretic Bayesian optimisation.