As artificial intelligence (AI)-empowered applications become widespread, there is growing awareness and concern for user privacy and data confidentiality. This has contributed to the popularity of federated learning (FL). FL applications often face data distribution and device capability heterogeneity across data owners. This has stimulated the rapid development of Personalized FL (PFL). In this paper, we complement existing surveys, which largely focus on the methods and applications of FL, with a review of recent advances in PFL. We discuss hurdles to PFL under the current FL settings, and present a unique taxonomy dividing PFL techniques into data-based and model-based approaches. We highlight their key ideas, and envision promising future trajectories of research towards new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches.

There has been a paradigm-shift in urban logistic services in the last years; demand for real-time, instant mobility and delivery services grows. This poses new challenges to logistic service providers as the underlying stochastic dynamic vehicle routing problems (SDVRPs) require anticipatory real-time routing actions. Searching the combinatorial action space for efficient routing actions is by itself a complex task of mixed-integer programming (MIP) well-known by the operations research community. This complexity is now multiplied by the challenge of evaluating such actions with respect to their effectiveness given future dynamism and uncertainty, a potentially ideal case for reinforcement learning (RL) well-known by the computer science community. For solving SDVRPs, joint work of both communities is needed, but as we show, essentially non-existing. Both communities focus on their individual strengths leaving potential for improvement. Our survey paper highlights this potential in research originating from both communities. We point out current obstacles in SDVRPs and guide towards joint approaches to overcome them.

Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as a loss signal to train models to produce explanations for their predictions, and as a means to evaluate the quality of model-generated explanations. In this review, we identify three predominant classes of explanations (highlights, free-text, and structured), organize the literature on annotating each type, point to what has been learned to date, and give recommendations for collecting ExNLP datasets in the future.

Despite rising levels of automation through big-data, much of the data-mining and machine learning process still relies on human intervention, introducing different biases. The amount of structured and unstructured data generated has grown exponentially over the last few decades and will continue to do so for years to come. The'big data' analytics could potentially overcome numerous challenges that corporations and governments have faced for centuries while making decisions: the lack of adequate data for formulating policies (e.g., targeting policies for a particular social group) or examining market or consumer expectations (e.g., recommendation system). The descriptive as well as predictive modeling that is driven by the big data paradigm can help decision-makers derive valuable insights for personal, commercial, or collective gains. However, the modern data collection process and algorithms remain susceptible to data mining biases. Without taking appropriate measures, the big data can amplify the negative effect of the existing social issues (e.g., racial discrimination) and render the findings worthless or even counterproductive [1], [2].

Kambhampati, 2020; Etzioni and DeCario, 2020; Vaishya et al., 2020; Wynants and colleagues, 2020; Artificial Intelligence (AI) technologies have long Srivastava, 2020]. Early in the pandemic, authors been positioned as a tool to provide crucial datadriven like [Kambhampati, 2020; Etzioni and DeCario, 2020; decision support to people. In this survey Vaishya et al., 2020] highlighted various scenarios where paper, we look at how AI in general, and collaboration AI could help in tackling COVID19 as well as some of the assistants (CAs or chatbots for short) in particular, potential pitfalls. The AI efforts were helped by different have been used during a true global exigency types of data being freely made available, calls for open - the COVID-19 pandemic. The key observation collaboration [Woodward, 2020] and a sense of urgency. is that chatbots missed their Apollo moment In Table 1, a sample of AI's potential application during when they could have really provided contextual, COVID-19 is shown. They range from decisions to foster personalized, reliable decision support at scale that understanding of the disease and its impact to helping take the state-of-the-art makes possible. We review the actions for individuals, groups and the society at large.

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, the researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different meta-learning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techniques. We survey 100 distinct counterfactual explanation methods reported in the literature. This survey addresses the extent to which these methods have been adequately evaluated, both psychologically and computationally, and quantifies the shortfalls occurring. For instance, only 21% of these methods have been user tested. Five key deficits in the evaluation of these methods are detailed and a roadmap, with standardised benchmark evaluations, is proposed to resolve the issues arising; issues, that currently effectively block scientific progress in this field.

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues.

Variable importance measures are the main tools to analyze the black-box mechanism of random forests. Although the Mean Decrease Accuracy (MDA) is widely accepted as the most efficient variable importance measure for random forests, little is known about its theoretical properties. In fact, the exact MDA definition varies across the main random forest software. In this article, our objective is to rigorously analyze the behavior of the main MDA implementations. Consequently, we mathematically formalize the various implemented MDA algorithms, and then establish their limits when the sample size increases. In particular, we break down these limits in three components: the first two are related to Sobol indices, which are well-defined measures of a variable contribution to the output variance, widely used in the sensitivity analysis field, as opposed to the third term, whose value increases with dependence within input variables. Thus, we theoretically demonstrate that the MDA does not target the right quantity when inputs are dependent, a fact that has already been noticed experimentally. To address this issue, we define a new importance measure for random forests, the Sobol-MDA, which fixes the flaws of the original MDA. We prove the consistency of the Sobol-MDA and show its good empirical performance through experiments on both simulated and real data. An open source implementation in R and C++ is available online.

Human-made physical random number generators (RNGs) can be traced back 5000 years or more. Early examples such as knucklebones, two-sided throwsticks, or dice have been found in the Middle East, India, and China. RNGs were used for fortune telling and games of chance, with the oldest known board games of similar age as those of the number generators. Today, RNGs are vital for services and state-of-the-art technologies such as cryptographically secured communication, blockchain technologies, and quantum key distribution. Moreover, RNGs are needed in machine learning and scientific applications such as Monte Carlo numerical methods. On page 948 of this issue, Kim et al. ([ 1 ][1]) demonstrate an ultrafast RNG based on a broad-area laser with a multispot beam that is analogous to generating random numbers by using many dice at once. Random numbers are often generated by using a software algorithm running on a computer, called “pseudo”-random because the sequence eventually repeats. Moreover, relations among the numbers can exist that reveal that the numbers are not uniformly random. Hence, true RNGs (TRNGs) are of great interest, providing random numbers based on physical measurements that involve some noisy or stochastic process. All TRNGs have some nonidealities, such as generating zeroes more frequently than ones for a binary-output device, which must be mitigated by carefully engineering the device and postprocessing the data to improve the randomness quality ([ 2 ][2]). ![Figure][3] Creating bits with laser intensity Ultrafast random bits are generated from a broad-area laser with a bow-tie cavity. In order to generate the random bits, first intensities separated by ∼6 ps are subtracted from each other for the same positions on the detector. This creates bits of either ones or zeroes, which then undergo the exclusive-OR (XOR) logic operation with bits of another spot separated by half the width of the aperture. The XOR operation produces a one if the two inputs are different or a zero if the two inputs are the same. The broad-area laser allows for many different positions on the detector to be used simultaneously, allowing for fast generation of bits. GRAPHIC: N.DESAI/ SCIENCE Some applications require generating random numbers at very high rates, such as encrypting data in cloud-computing data centers, high-speed communication networks, or massive simulations. Photonic devices are a natural fit for these applications because of their potential for high-speed operation, compact size for chip-scale devices, and low power consumption. Recently, Marangon et al. ([ 3 ][4]) developed a TRNG that is based on interfering two different lasers on a beam splitter and detecting the resulting powers that emanate from its two output ports. The randomness comes about from quantum fluctuations in a laser due to a process known as spontaneous emission of photons. This process randomizes the phase of the light emitted by each laser, and this phase variation is converted to an intensity variation through the interference effect. Measuring which output port of the interferometer has the higher or lower intensity can be used to generate a one or a zero, respectively, at random. A compact device can be realized by generating random numbers in real time at a rate of 8 Gb/s for days at a time, passing tests that are used to assess the quality of the bit stream. The bottleneck in reaching higher speeds is that the lasers are single-mode and only generate a Gaussian beam–like spot at a single frequency. Kim et al. overcome this bottleneck by using a broad-area laser that simultaneously emits a plethora of modes, resulting in a multispot beam. The patterns undergo a complex dance, writhing and growing bright and dim because of phase and amplitude variation of the light within the laser (see the figure). For a good TRNG, engineering the broad-area laser cavity is especially necessary so that spatial and temporal correlations are minimized. The authors do so, which is a major achievement. Common broad-area lasers are known to exhibit irregular intensity pulsations in space and time because of the nonlinear interaction of light and the laser medium ([ 4 ][5]). Such instabilities result in correlations of their emission with characteristic spatial and temporal scales and are exceedingly difficult to avoid. This situation has plagued attempts to apply broad-area lasers more widely. Bittner et al. ([ 5 ][6]) showed that they could largely suppress the onset of spatiotemporal instabilities by using a cavity with a D shape, inspired by chaotic billiards; the balls on a D-shaped billiard table follow chaotic trajectories ([ 6 ][7]). Kim et al. introduce another approach based on adapting the shape of the cavity. After performing extensive numerical modeling, the authors chose a bow-tie shape and precisely microfabricated a laser chip. The authors managed to boost the number of modes, avoiding their locking, and thereby substantially reduced the spatial and temporal correlation scales to 1.5 µm and 2.8 ps, respectively. Another advantage of using the spatial degree of freedom of the special laser design is avoiding the two separate lasers and interference on an auxiliary beam splitter ([ 3 ][4]). Random numbers can be “harvested” from the complex emitted pattern by measuring the intensity at 254 spatial positions on ultrafast time scales (on the order of 1 ps) by using a special high-speed camera. This strategy is truly an ultrafast Demeter meeting chance. Through this effort, they achieved a random bit generation rate of 250 Tb/s, which is much more than an order of magnitude greater than previous efforts. A full technical implementation of such an ultrafast TRNG still faces several challenges that need to be overcome. The high-speed camera could only capture data over a limited time (∼2 ns), so they had to collect and concatenate multiple records to generate the more than 109 random numbers needed for the various statistical tests of randomness. Replacing the camera with a multitude of integrated photodetectors is yet to be achieved. Also, the required postprocessing of the measured intensities to ensure randomness is, at such speed, a task for the future. Looking beyond, the innovative approach to tailor the spatial and temporal emission properties of broad-area lasers and manipulating the nonlinear interaction of light with the laser medium opens other applications that require many degrees of freedom. Several machine-learning approaches are based on a random mapping of low-dimensional input data onto a high-dimensional-state space, which might be accomplished by injecting a data-encoded beam into a tailored laser. Hence, broad-area lasers may become attractive photonic integrated circuits for ultrafast information processing ([ 7 ][8], [ 8 ][9]). 1. [↵][10]1. K. Kim et al ., Science 371, 948 (2021). [OpenUrl][11][CrossRef][12] 2. [↵][13]1. J. D. Hart et al ., Appl. Phys. Lett. Photonics 2, 090901 (2017). [OpenUrl][14] 3. [↵][15]1. D. G. Marangon et al ., J. Lightwave Technol. 36, 3778 (2018). [OpenUrl][16] 4. [↵][17]1. I. Fischer, 2. O. Hess, 3. W. Elsäßer, 4. E. Göbel , Europhys. Lett. 35, 579 (1996). [OpenUrl][18][CrossRef][19] 5. [↵][20]1. S. Bittner et al ., Science 361, 1225 (2018). [OpenUrl][21][Abstract/FREE Full Text][22] 6. [↵][23]1. H. Cao, 2. J. Wiersig , Rev. Mod. Phys. 87, 61 (2015). [OpenUrl][24][CrossRef][25][PubMed][26] 7. [↵][27]1. P. R. Prucnal, 2. B. J. Shastri , Neuromorphic Photonics (CRC Press, 2017). 8. [↵][28]1. D. Brunner, 2. M. C. Soriano, 3. G. Van der Sande , Eds., Photonic Reservoir Computing (De Gruyter, 2019). [1]: #ref-1 [2]: #ref-2 [3]: pending:yes [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #ref-6 [8]: #ref-7 [9]: #ref-8 [10]: #xref-ref-1-1 "View reference 1 in text" [11]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DKim%26rft.auinit1%253DK.%26rft.volume%253D371%26rft.issue%253D6532%26rft.spage%253D948%26rft.epage%253D952%26rft.atitle%253DMassively%2Bparallel%2Bultrafast%2Brandom%2Bbit%2Bgeneration%2Bwith%2Ba%2Bchip-scale%2Blaser%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abc2666%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [12]: /lookup/external-ref?access_num=10.1126/science.abc2666&link_type=DOI [13]: #xref-ref-2-1 "View reference 2 in text" [14]: {openurl}?query=rft.jtitle%253DAppl.%2BPhys.%2BLett.%2BPhotonics%26rft.volume%253D2%26rft.spage%253D090901%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [15]: #xref-ref-3-1 "View reference 3 in text" [16]: {openurl}?query=rft.jtitle%253DJ.%2BLightwave%2BTechnol.%26rft.volume%253D36%26rft.spage%253D3778%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: #xref-ref-4-1 "View reference 4 in text" [18]: {openurl}?query=rft.jtitle%253DEurophys.%2BLett.%26rft.volume%253D35%26rft.spage%253D579%26rft_id%253Dinfo%253Adoi%252F10.1209%252Fepl%252Fi1996-00154-7%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.1209/epl/i1996-00154-7&link_type=DOI [20]: #xref-ref-5-1 "View reference 5 in text" [21]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aas9437%26rft_id%253Dinfo%253Apmid%252F30115744%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [22]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNjEvNjQwOC8xMjI1IjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcxLzY1MzIvODg5LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [23]: #xref-ref-6-1 "View reference 6 in text" [24]: {openurl}?query=rft.jtitle%253DRev.%2BMod.%2BPhys.%26rft.volume%253D87%26rft.spage%253D61%26rft_id%253Dinfo%253Adoi%252F10.1103%252FRevModPhys.87.61%26rft_id%253Dinfo%253Apmid%252F25739324%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: /lookup/external-ref?access_num=10.1103/RevModPhys.87.61&link_type=DOI [26]: /lookup/external-ref?access_num=25739324&link_type=MED&atom=%2Fsci%2F371%2F6532%2F889.atom [27]: #xref-ref-7-1 "View reference 7 in text" [28]: #xref-ref-8-1 "View reference 8 in text"