Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Border Transfers of Personal Data after Schrems II: Supplementary Measures and New Standard Contractual Clauses (SCCs)

This article analyses the legal challenges of international data transfers resulting from the recent Court of Justice of the European Union (CJEU) decision in Case C-311/18 Data Protection Commissioner v Facebook Ireland Limited, Maximillian Schrems (Schrems II). This judgement invalidated the EU-US Privacy Shield Framework but upheld the use of standard contractual clauses (SCCs). However, one caveat is that organisations would have to perform a case-by-case assessment on the application of the SCCs and implement ‘supplementary measures’ to compensate for the lack of data protection in the third country, where necessary. Regrettably, the CJEU missed the opportunity to specify what exactly these ‘supplementary measures’ could be. To fill this gap, the European Data Protection Board (EDPB) adopted guidelines on the measures that supplement transfer tools to ensure compliance with the EU level of protection of personal data. In addition, on June 4th, 2021 the European Commission issued new SCCs which replaced the previous SCCs that were adopted under the previous Data Protection Directive 95/46. These new developments have raised the bar for data protection in international data transfers. In this article, we analyse the current regulatory framework for cross-border transfers of EU personal data and examine the practical considerations of the emerging post-Schrems II legal landscape.
Intellectual Capital, and Knowledge Processes for Organizational Innovativeness across Industries: The Case of Poland – the full version

Purpose: This study aims to present the overview of intellectual capital creation micro-mechanisms concerning formal and informal knowledge processes. The organizational culture, transformational leadership, and innovativeness are also included in the investigation as ascendants and consequences of the focal relation of intellectual capital and knowledge processes. Method: The empirical model was developed using the structural equation modeling (SEM) method based on a sample of 1,418 Polish knowledge workers employed in the construction, healthcare, higher education (HE), and information technology (IT) industries. Findings: The study exposes that the essence of transformational leadership innovativeness oriented is developing all intellectual capital components. To do so, leaders must support both formal and informal knowledge processes through the organizational culture of knowledge and learning. Furthermore, for best results of the knowledge transformation into intellectual capital, the learning culture must be shaped by both components: learning climate and acceptance of mistakes. Originality: This study presents the "big picture" of all intellectual capital creation micro- mechanisms linking transformational leadership with organizational innovativeness and explains the "knowledge paradox" identified by Mabey and Zhao (2017). This explanation assumes that intellectual capital components are created informally (i.e., human, and relational ones) and formally (i.e., structural ones). Therefore, for best effects, both formal and informal knowledge processes must be supported. Furthermore, this study exposes that the intensity of all explored micro-mechanisms is industry-specific. Implications: Presented findings can be directly applied to organizations to enhance innovativeness. Namely, leaders who observe that the more knowledge is formally managed in their organizations, the less effective the knowledge exchange is - should put more effort into supporting informal knowledge processes to develop human and relational intellectual capital components smoothly. Shortly, leaders need to implement an authentic learning culture, including the mistakes acceptance component, to use the full organizational potential to achieve intellectual capital growth. Intellectual capital growth is essential for innovativeness.
Machine Learning Methods in Asset Pricing

This paper evaluates the traditional asset pricing models and examines the literature on the most promising machine learning techniques that can be used to price securities. Asset price forecasting is essential to efficient markets. Capital Asset Pricing Models (CAPM), Arbitrage Pricing Theory (APT) and a multitude of Factor Models are used to price securities and to establish mean variance optimal portfolios. An increasing number of scholars and financial practitioners have begun to explore the role of machine learning in asset pricing. We show how these methods have been applied in academia and discuss their results in maximizing the Sharpe Ratio. We also explore the potential use of neural networks in asset pricing. We believe that their capacity to process large amounts of data and their ability to accurately capture non-linear relationships makes them a useful estimation tool.
Community Social Capital and Board Advising: Evidence from the Structure of Board Committees

We investigate how community social capital, captured by the strength of cooperative norms and social networks within a geographical community, affects the internal structure of corporate boards. We find that firms headquartered in high-social-capital US counties have a more advising-intensive board structure, as they are more likely to set up specialised advisory committees and appoint more advisory directors. These findings are robust to endogeneity concerns and a battery of sensitivity tests. Our mediation analysis shows that the increased board advising intensity, induced by community social capital, reduces investment inefficiency. We further reveal that community social capital reduces board monitoring intensity and directors’ monitoring efforts. Overall, our results are consistent with the argument that community social capital serves as a societal monitoring mechanism to reduce firms’ need for board monitoring and, hence, firms’ boards located in high-socialcapital communities focus more on advising.
Do not blame Bellman: It is Koopmans' fault

We provide a unified approach to stochastic dynamic programming with recursive utility based on an elementary application of Tarski's fixed point theorem. We establish that the exclusive source of multiple values is the presence of multiple recursive utilities consistent with the given aggregator, each yielding a legitimate value of the recursive program. We also present sufficient conditions ensuring a unique value of the recursive program in some circumstances. Overall, acknowledging the unavoidable failure of uniqueness in general, we argue that the greatest fixed point of the Bellman operator should have a privileged position.
Mirco-level Prediction of Outstanding Claim Counts using Neural Networks

Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.
Top-label calibration and multiclass-to-binary reductions

A multiclass classiﬁer is said to be top-label calibrated if the reported probability for the predicted class—the top-label—is calibrated, conditioned on the top-label. This conditioning on the top-label is ab-sent in the closely related and popular notion of conﬁdence calibration, which we argue makes conﬁdence calibration diﬃcult to interpret for decision-making. We propose top-label calibration as a rectiﬁcation of conﬁdence calibration. Further, we outline a multiclass-to-binary (M2B) reduction framework that uniﬁes conﬁdence, top-label, and class-wise calibration, among others. As its name suggests, M2B works by reducing multiclass calibration to numerous binary calibration problems, each of which can be solved using simple binary calibration routines. We instantiate the M2B framework with the well-studied histogram binning (HB) binary calibrator, and prove that the overall procedure is multiclass calibrated without making any assumptions on the underlying data distribution. In an empirical evaluation with four deep net architectures on CIFAR-10 and CIFAR-100, we ﬁnd that the M2B + HB procedure achieves lower top-label and class-wise calibration error than other approaches such as temperature scaling. Code for this work is available at https://github.com/aigen/df-posthoc-calibration .
A Novel Binocular Eye-Tracking SystemWith Stereo Stimuli for 3D Gaze Estimation

Eye-tracking technologies have been widely used in applications like psychological studies and human computer interactions (HCI). However, most current eye trackers focus on 2D point of gaze (PoG) estimation and cannot provide accurate gaze depth.Concerning future applications such as HCI with 3D displays, we propose a novel binocular eye tracking device with stereo stimuli to provide highly accurate 3D PoG estimation. In our device, the 3D stereo imaging system can provide users with a friendly and immersive 3D visual experience without wearing any accessories. The eye capturing system can directly record the users eye movements under 3D stimuli without disturbance. A regression based 3D eye tracking model is built based on collected eye movement data under stereo stimuli. Our model estimates users 2D gaze with features defined by eye region landmarks and further estimates 3D PoG with a multi source feature set constructed by comprehensive eye movement features and disparity features from stereo stimuli. Two test stereo scenes with different depths of field are designed to verify the model effectiveness. Experimental results show that the average error for 2D gaze estimation was 0.66\degree and for 3D PoG estimation, the average errors are 1.85~cm/0.15~m over the workspace volume 50~cm $\times$ 30~cm $\times$ 75~cm/2.4~m $\times$ 4.0~m $\times$ 7.9~m separately.
Streaming Local Community Detection Through Approximate Conductance

Community is a universal structure in various complex networks, and community detection is a fundamental task for network analysis. With the rapid growth of network scale, networks are massive, changing rapidly, and could naturally be modeled as graph streams. Due to the limited memory and access constraint in graph streams, existing non-streaming community detection methods are no longer applicable. This raises an emerging need for online approaches. In this work, we consider the problem of uncovering the local community containing a few query nodes in graph streams, termed streaming local community detection. This new problem raised recently is more challenging for community detection, and only a few works address this online setting. Correspondingly, we design an online single-pass streaming local community detection approach. Inspired by the local property of communities, our method samples the local structure around the query nodes in graph streams and extracts the target community on the sampled subgraph using our proposed metric called approximate conductance. Comprehensive experiments show that our method remarkably outperforms the streaming baseline on both effectiveness and efficiency, and even achieves similar accuracy compared to the state-of-the-art non-streaming local community detection methods that use static and complete graphs.
MACHINE LEARNING OPTIMIZATION OF A NANOPHOTONIC CAVITY FOR NEAR- UNITY PHOTON INDISTINGUISHABILITY AT ROOM TEMPERATURE

Room-temperature (RT), on-chip deterministic generation of indistinguishable photons coupled to photonic integrated circuits is key for quantum photonic applications. Nevertheless, high indistinguishability (I) at RT is difficult to obtain due to the intrinsic dephasing of most deterministic single-photon sources (SPS). Here we present the design, fabrication and optimization of a hybrid slot-Bragg nanophotonic cavity that achieves nearunity I and high efficiency (β) at RT for a variety of single-photon emitters. Our cavity provides modal volumes in the order of 10(λ/2n), allowing for strong coupling of quantum photonic emitters that can be heterogeneously integrated. We show that high I and β should be possible by fine-tuning the quality factor (Q) depending on the intrinsic properties of the single-photon emitter. Furthermore, we perform a machine learning optimization based on the combination of a deep neural network and a genetic algorithm (GA) to further decrease the modal volume by almost three times while relaxing the tight dimensions required for strong coupling.
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model

Recovering dense human poses from images plays a critical role in establishing an image-to-surface correspondence between RGB images and the 3D surface of the human body, serving the foundation of rich real-world applications, such as virtual humans, monocular-to-3d reconstruction. However, the popular DensePose-COCO dataset relies on a sophisticated manual annotation system, leading to severe limitations in acquiring the denser and more accurate annotated pose resources. In this work, we introduce a new 3D human-body model with a series of decoupled parameters that could freely control the generation of the body. Furthermore, we build a data generation system based on this decoupling 3D model, and construct an ultra dense synthetic benchmark UltraPose, containing around 1.3 billion corresponding points. Compared to the existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has ultra dense image-to-surface correspondences without annotation cost and error. Our proposed UltraPose provides the largest benchmark and data resources for lifting the model capability in predicting more accurate dense poses. To promote future researches in this field, we also propose a transformer-based method to model the dense correspondence between 2D and 3D worlds. The proposed model trained on synthetic UltraPose can be applied to real-world scenarios, indicating the effectiveness of our benchmark and model.1
Efficient Placard Discovery for Semantic Mapping During Frontier Exploration

Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorithms by attaching semantics to locations. The system presented in this work reads door placards to annotate the locations of offices. Whereas prior work on this system developed hand-crafted detectors, this system leverages YOLOv2 for detection and a segmentation network for segmentation. Placards are localized by computing their pose from a homography computed from a segmented quadrilateral outline. This work also introduces an Interruptable Frontier Exploration algorithm, enabling the robot to explore its environment to construct its SLAM map while pausing to inspect placards observed during this process. This allows the robot to autonomously discover room placards without human intervention while speeding up significantly over previous autonomous exploration methods.
Guided Evolution for Neural Architecture Search

Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. In this paper, we propose G-EA, a novel approach for guided evolutionary NAS. The rationale behind G-EA, is to explore the search space by generating and evaluating several architectures in each generation at initialization stage using a zero-proxy estimator, where only the highest-scoring network is trained and kept for the next generation. This evaluation at initialization stage allows continuous extraction of knowledge from the search space without increasing computation, thus allowing the search to be efficiently guided. Moreover, G-EA forces exploitation of the most performant networks by descendant generation while at the same time forcing exploration by parent mutation and by favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method, showing that G-EA achieves state-of-the-art results in NAS-Bench-201 search space in CIFAR-10, CIFAR-100 and ImageNet16-120, with mean accuracies of 93.98%, 72.12% and 45.94% respectively.
Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT

In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can collaboratively train a shared model without compromising data privacy. However, due to limited resources in the industrial IoT networks, including computational power, bandwidth, and channel state, it is challenging for many devices to accomplish local training and upload weights to the edge server in time. To address this issue, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework, where the deep model can be divided into several sub-models with different depths and output prediction from the exit in the corresponding sub-model. In this way, the devices with insufficient computational power can choose the earlier exits and avoid training the complete model, which can help reduce computational latency and enable devices to participate into aggregation as much as possible within a latency threshold. Moreover, we propose a greedy approach-based exit selection and bandwidth allocation algorithm to maximize the total number of exits in each communication round. Simulation experiments are conducted on the classical Fashion-MNIST dataset under a non-independent and identically distributed (non-IID) setting, and it shows that the proposed strategy outperforms the conventional FL. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
Resonance condensation in the high-Tc cuprates

The cold atomic Fermi gas has become the essential paradigm for understanding the crossover between the weak coupling Bardeen-Schrieffer-Cooper (BCS) and strong coupling Bose-Einstein Condensation (BEC) regimes of paired fermion condensates. On tuning the pairing interactions through the crossover, the superfluid condensate becomes maximally robust between the BCS and BEC regimes at what is referred to as the unitary point, giving rise to a maximally peaked phase transition anomaly in the specific heat. We find here, by tracing the jump ∆γ in the electronic specific heat coefficient γ at the transition temperature Tc as a function of the gap ratio 2∆/kBTc, that this same thermodynamic signature occurs in the high-Tc superconducting cuprates. On taking ∆ as the pairing gap in a cold atomic Fermi gas and as the antinodal pseudogap energy in the cuprates, we find ∆γ to be asymmetrically peaked at the same value of 2∆/kBTc ≈ 6.5. We show that the observation of this peak in the cuprates, and its coincidence with a normal state maximum in γ, provides a previously overlooked thermodynamic signature of condensation at a unitary point.
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection

Anomaly detection or outlier detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing works mainly focus on structured data such as numerical or categorical data; however, anomaly detection on unstructured textual data is less attended. In this work, we target the textual anomaly detection problem and propose a deep anomaly-injected support vector data description (AI-SVDD) framework. AI-SVDD not only learns a more compact representation of the data hypersphere but also adopts a small number of known anomalies to increase the discriminative power. To tackle text input, we employ a multilayer perceptron (MLP) network in conjunction with BERT to obtain enriched text representations. We conduct experiments on three text anomaly detection applications with multiple datasets. Experimental results show that the proposed AI-SVDD is promising and outperforms existing works.
Learning to Ground Multi-Agent Communication with Autoencoders

Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process, but it may require many generations of trial and error. Alternatively, the lingua franca can be given by the environment, where agents ground their language in representations of the observed world. We demonstrate a simple way to ground language in learned representations, which facilitates decentralized multi-agent communication and coordination. We find that a standard representation learning algorithm -- autoencoding -- is sufficient for arriving at a grounded common language. When agents broadcast these representations, they learn to understand and respond to each other's utterances and achieve surprisingly strong task performance across a variety of multi-agent communication environments.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence

Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning methods to a particular problem set has become an established and valuable modus operandi to advance a particular field. In this article we argue that such an approach does not straightforwardly extended to robotics -- or to embodied intelligence more generally: systems which engage in a purposeful exchange of energy and information with a physical environment. In particular, the purview of embodied intelligent agents extends significantly beyond the typical considerations of main-stream machine learning approaches, which typically (i) do not consider operation under conditions significantly different from those encountered during training; (ii) do not consider the often substantial, long-lasting and potentially safety-critical nature of interactions during learning and deployment; (iii) do not require ready adaptation to novel tasks while at the same time (iv) effectively and efficiently curating and extending their models of the world through targeted and deliberate actions. In reality, therefore, these limitations result in learning-based systems which suffer from many of the same operational shortcomings as more traditional, engineering-based approaches when deployed on a robot outside a well defined, and often narrow operating envelope. Contrary to viewing embodied intelligence as another application domain for machine learning, here we argue that it is in fact a key driver for the advancement of machine learning technology. In this article our goal is to highlight challenges and opportunities that are specific to embodied intelligence and to propose research directions which may significantly advance the state-of-the-art in robot learning.
Three-dimensional turbulence generated homogeneously by magnetic particles

Three-dimensional turbulence is usually studied experimentally by using a spatially localized forcing at large scales (e.g. via rotating blades or oscillating grids), often in a deterministic way. Here, we report an original technique where the fluid is forced in volume, randomly in space and time, using small magnetic particles remotely driven. Such a forcing generates almost no mean flow and is closer to those of direct numerical simulations of isotropic homogeneous turbulence. We compute the energy spectra and structure functions using local and spatiotemporal flow velocity measurements. The energy dissipation rate is also evaluated consistently in five different ways. Our experimental results confirm the stationary, homogeneous and isotropic features of such turbulence, and in particular the Tennekes’ model for which the Tennekes’ constant is experimentally estimated.