Miracle-dz/UniFAR-FTUs huggingface dataset:
Solution‐Processable Ultrathin Black Phosphorus as an Effective Electron Transport Layer in Organic Photovoltaics

2D van der Waals crystals, possessing excellent electronic and physical properties, have been intriguing building blocks for organic optoelectronic devices. Most of the 2D materials are served as hole transport layers in organic devices. Here,it is reported that solution exfoliated few layers black phosphorus (BP) can be served as an effective electron transport layer (ETL) in organic photovoltaics (OPVs) for the first time. The power conversion efficiencies (PCEs) of the BP‐incorporated OPVs can be improved to 8.18% in average with the relative enhancement of 11%. The incorporation of BP flakes with the optimum thickness of ≈10 nm can form cascaded band structure in OPVs, which can facilitate electron transport and enhance the PCEs of the devices. This study opens an avenue in using solution exfoliated BP as a highly efficient ETL for organic optoelectronics.
Automatic Construction of Enterprise Knowledge Base

In this paper, we present an automatic knowledge base construction system from large scale enterprise documents with minimal efforts of human intervention. In the design and deployment of such a knowledge mining system for enterprise, we faced several challenges including data distributional shift, performance evaluation, compliance requirements and other practical issues. We leveraged state-of-the-art deep learning models to extract information (named entities and definitions) at per document level, then further applied classical machine learning techniques to process global statistical information to improve the knowledge base. Experimental results are reported on actual enterprise documents. This system is currently serving as part of a Microsoft 365 service.
SDL: New data generation tools for full-level annotated document layout

We present a novel data generation tool for document processing. The tool focuses on providing a maximal level of visual information in a normal type document, ranging from character position to paragraph-level position. It also enables working with a large dataset on low-resource languages as well as providing a mean of processing thorough full-level information of the documented text. The data generation tools come with a dataset of 320000 Vietnamese synthetic document images and an instruction to generate a dataset of similar size in other languages. The repository can be found at: https://github.com/tson1997/SDL-Document-Image-Generation
What Is Consciousness? Artificial Intelligence, Real Intelligence, Quantum Mind, And Qualia

We approach the question "What is Consciousness? " in a new way, not as Descartes' "systematic doubt", but as how organisms find their way in their world. Finding one's way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. "Possible uses of X to accomplish Y" are "Affordances". The number of uses of X is indefinite (or unknown), the different uses are unordered and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: (1) Artificial General Intelligence based on Universal Turing Machines (UTMs) is not possible, since UTMs cannot "find" novel affordances. (2) Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behavior can be isomorphic to "possible uses". (3) Brain mind must be partly quantum - supported by increasing evidence at 6.0 sigma to 7.3 Sigma. (4) Based on Heisenberg's interpretation of the quantum state as "Potentia" converted to "Actuals" by Measurement, a natural hypothesis is that mind actualizes Potentia. This is supported at 5.2 Sigma. Then Mind's actualizations of entangled brain-mind-world states are experienced as qualia and allow "seeing" or "perceiving" of uses of X to accomplish Y. We can and do jury-rig. Computers cannot. (5) Beyond familiar quantum computers, we discuss the potentialities of Trans-Turing-Systems.
Language Lexicons for Hindi-English Multilingual Text Processing

Language identification (LI) in textual documents is the process of automatically detecting the language contained in a document based on its content. The present language identification techniques presume that a document contains text in one of the fixed set of languages. However, this presumption is incorrect when dealing with multilingual document which includes content in more than one possible language. Due to the unavailability of standard corpora for Hindi-English mixed lingual language processing tasks, we propose the language lexicons, a novel kind of lexical database that augments several bilingual language processing tasks. These lexicons are built by learning classifiers over English and transliterated Hindi vocabulary. The designed lexicons possess condensed quantitative characteristics which reflect their linguistic strength in respect of Hindi and English language. On evaluating the lexicons, it is observed that words of the same language tend to cluster together and are separable over language classes. On comparing the classifier performance with existing works, the proposed lexicon models exhibit the better performance.
On exploring practical potentials of quantum auto-encoder with advantages

Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics, celebrated by the ability to extract low-dimensional patterns from quantum states living in the high-dimensional space. Despite its attractive properties, little is known about the practical applications of QAE with provable advantages. To address these issues, here we prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state with the low-rank property. With this regard, we devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks, respectively. Notably, all of these protocols are scalable and can be readily executed on near-term quantum machines. Moreover, we prove that the error bounds of the proposed QAE-based methods outperform those in previous literature. Numerical simulations collaborate with our theoretical analysis. Our work opens a new avenue of utilizing QAE to tackle various quantum physics and quantum information processing problems in a scalable way.
Estimating Incremental Acquisition of Content Launches in a Subscription Service

Subscription services face a difficult problem when estimating the causal impact of content launches on acquisition. Customers buy subscriptions, not individual pieces of content, and once subscribed they may consume many pieces of content in addition to the one(s) that drew them to the service. In this paper, we propose a scalable methodology to estimate the incremental acquisition impact of content launches in a subscription business model when randomized experimentation is not feasible. Our approach uses simple assumptions to transform the problem into an equivalent question: what is the expected consumption rate for new subscribers who did not join due to the content launch? We estimate this counterfactual rate using the consumption rate of new subscribers who joined just prior to launch, while making adjustments for variation related to subscriber attributes, the in-product experience, and seasonality. We then compare our counterfactual consumption to the actual rate in order to back out an acquisition estimate. Our methodology provides top-line impact estimates at the content / day / region grain. Additionally, to enable subscriber-level attribution, we present an algorithm that assigns specific individual accounts to add up to the top-line estimate. Subscriber-level attribution is derived by solving an optimization problem to minimize the number of subscribers attributed to more than one piece of content, while maximizing the average propensity to be incremental for subscribers attributed to each piece of content. Finally, in the absence of definitive ground truth, we present several validation methods which can be used to assess the plausibility of impact estimates generated by these methods.
Trust is not all about performance: trust biases in interaction with humans, robots and computers

Trust is essential for sustaining cooperation among humans. The same principle applies during interaction with computers and robots: if we do not trust them, we will not accept help from them. Extensive evidence has shown that our trust in other agents depends on their performance. However, in uncertain environments, humans may not be able to estimate correctly other agents’ performance, potentially leading to distrust or over-trust in peers and machines. In the current study, we investigate whether humans’ trust towards peers, computers and robots is biased by prior beliefs in uncertain interactive settings. Participants made perceptual judgments and observed the simulated estimates of either a human participant, a computer or a social robot. Participants could modify their judgments based on this feedback. Results show that participants’ belief about the nature of their partner biased their compliance with the partners’ judgments, although the partners’ judgments were identical. Surprisingly, the social robot was trusted more than computer and human partner. Trust in the alleged human partner was not fully predicted by its perceived performance, suggesting the emergence of normative processes in peer interaction. Our findings offer novel insights in the understanding of the mechanisms underlying trust towards peers and autonomous agents.
Privacy Budget Scheduling

Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on this leakage. Each new model trained with DP increases the bound on data leakage and can be seen as consuming part of a global privacy budget that should not be exceeded. This budget is a scarce resource that must be carefully managed to maximize the number of successfully trained models. We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. For example, traditional compute resources are replenishable while privacy is not: a CPU can be regained after a model finishes execution while privacy budget cannot. This distinction forces a re-design of the scheduler. We present DPF (Dominant Private Block Fairness) -- a variant of the popular Dominant Resource Fairness (DRF) algorithm -- that is geared toward the non-replenishable privacy resource but enjoys similar theoretical properties as DRF. We evaluate PrivateKube and DPF on microbenchmarks and an ML workload on Amazon Reviews data. Compared to existing baselines, DPF allows training more models under the same global privacy guarantee. This is especially true for DPF over R\'enyi DP, a highly composable form of DP.
How to Account for Alternatives When Comparing Effects: Revisiting 'Bringing Education to Afghan Girls'

This paper uses a “principal strata” approach to decompose treatment effects and interpret why a schooling intervention that yielded exceptional initial effects yielded substantially smaller effects in a replication years later. The specific application is a set of 2008 and 2015 replications of an intervention aiming to increase primary education for girls in rural Afghanistan. The intervention offers a new schooling option, and as such, its effects depend on how individuals use alternatives that already exist. The principal strata approach accounts variation in use patterns when comparing effects across the replications. Our findings show that even though the share of girls for whom the intervention would be valuable dropped considerably in 2015 as compared to 2008, the intervention was even more efficaciousness for those who continued to benefit from it.
Are conditional GANs explicitly conditional?

This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture. The first main contribution is an analysis of cGANs to show that they are not explicitly conditional. In particular, it will be shown that the discriminator and subsequently the cGAN does not automatically learn the conditionality between inputs. The second contribution is a new method, called a contrario cGAN, that explicitly models conditionality for both parts of the adversarial architecture via a novel a contrario loss that involves training the discriminator to learn unconditional (adverse) examples. This leads to a novel type of data augmentation approach for GANs (a contrario learning) which allows to restrict the search space of the generator to conditional outputs using adverse examples. Extensive experimentation is carried out to evaluate the conditionality of the discriminator by proposing a probability distribution analysis. Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation, monocular depth prediction and"single label"-to-image using different metrics including Fr\'echet Inception Distance (FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB).
Magnetic Bloch-point hopping in multilayer skyrmions and associated emergent electromagnetic signatures

Magnetic multilayers are promising tuneable systems for hosting magnetic skyrmions at/above room temperature. Revealing their intriguing switching mechanisms and associated inherent electrical responses are prerequisites for developing skyrmionic devices. In this work, we theoretically demonstrate the annihilation of single skyrmions occurring through a multilayer structure, which is mediated by hopping dynamics of topological hedgehog singularities known as Bloch points. The emerging intralayer dynamics of Bloch points are dominated by the Dzyaloshinskii-Moriya interaction, and their propagation can give rise to solenoidal emergent electric fields in the vicinity. Moreover, as the topology of spin textures can dominate their emergent magnetic properties, we show that the Bloch-point hopping through the multilayer will modulate the associated topological Hall response, with the magnitude proportional to the effective topological charge. We also investigate the thermodynamic stability of these states regarding the layer-dependent magnetic properties. This study casts light on the emergent electromagnetic signatures of skyrmion-based spintronics, rooted in magnetic-multilayer systems.
The algebra $U^+_q$ and its alternating central extension $\mathcal U^+_q$

Let U+ q denote the positive part of the quantized enveloping algebra Uq(ŝl2). The algebra U+ q has a presentation involving two generators W0, W1 and two relations, called the q-Serre relations. In 1993 I. Damiani obtained a PBW basis for U+ q , consisting of some elements {Enδ+α0} ∞ n=0, {Enδ+α1} ∞ n=0, {Enδ} ∞ n=1. In 2019 we introduced the alternating central extension U+ q of U + q . We defined U + q by generators and relations. The generators, said to be alternating, are denoted {W−k} ∞ k=0, {Wk+1} ∞ k=0, {Gk+1} ∞ k=0, {G̃k+1} ∞ k=0. Let 〈W0,W1〉 denote the subalgebra of U + q generated by W0, W1. It is known that there exists an algebra isomorphism U + q → 〈W0,W1〉 that sends W0 7→ W0 and W1 7→ W1. Via this isomorphism we identify U + q with 〈W0,W1〉. In our main result, we express the Damiani PBW basis elements in terms of the alternating generators. We give the answer in terms of generating functions.
Penrose process for a charged black hole in a uniform magnetic field

Spinning black holes create electromagnetic storms when immersed in ambient magnetic fields, illuminating the otherwise epically dark terrain. In an electromagnetic extension of the Penrose process, tremendous energy can be extracted, boosting the energy of radiating particles far more efficiently than the mechanical Penrose process. We locate the regions from which energy can be mined and demonstrate explicitly that they are no longer restricted to the ergosphere. We also show that there can be toroidal regions that trap negative energy particles in orbit around the black hole. We find that the effective charge coupling between the black hole and the super-radiant particles decreases as energy is extracted, much like the spin of a black hole decreases in the mechanical analogue. While the effective coupling decreases, the actual charge of the black hole increases in magnitude reaching the energetically-favored Wald value, at which point energy extraction is impeded. We demonstrate the array of orbits for products from the electromagnetic Penrose process. ar X iv :2 10 6. 15 01 0v 3 [ gr -q c] 1 5 N ov 2 02 1
When standard network measures fail to rank journals: A theoretical and empirical analysis

Abstract Journal rankings are widely used and are often based on citation data in combination with a network approach. We argue that some of these network-based rankings can produce misleading results. From a theoretical point of view, we show that the standard network modeling approach of citation data at the journal level (i.e., the projection of paper citations onto journals) introduces fictitious relations among journals. To overcome this problem, we propose a citation path approach, and empirically show that rankings based on the network and the citation path approach are very different. Specifically we use MEDLINE, the largest open-access bibliometric data set, listing 24,135 journals, 26,759,399 papers, and 323,356,788 citations. We focus on PageRank, an established and well-known network metric. Based on our theoretical and empirical analysis, we highlight the limitations of standard network metrics and propose a method to overcome them.
Model Predictive Control for Trajectory Tracking on Differentiable Manifolds

We consider the problem of bridging the gap between geometric tracking control theory and implementation of model predictive control (MPC) for robotic systems operating on manifolds. We propose a generic on-manifold MPC formulation based on a canonical representation of the system evolving on manifolds. Then, we present a method that solves the on-manifold MPC formulation by linearizing the system along the trajectory under tracking. There are two main advantages of the proposed scheme. The first is that the linearized system leads to an equivalent error system represented by a set of minimal parameters without any singularity. Secondly, the process of system modeling, error-system derivation, linearization and control has the manifold constraints completely decoupled from the system descriptions, enabling the development of a symbolic MPC framework that naturally encapsulates the manifold constraints. In this framework, users need only to supply system-specific descriptions without dealing with the manifold constraints. We implement this framework and test it on a quadrotor unmanned aerial vehicle (UAV) operating on $SO(3) \times \mathbb{R}^n$ and an unmanned ground vehicle (UGV) moving on a curved surface. Real-world experiments show that the proposed framework and implementation achieve high tracking performance and computational efficiency even in highly aggressive aerobatic quadrotor maneuvers.
Locally correct confidence intervals for a binomial proportion: A new criteria for an interval estimator

Well-recommended methods of forming `confidence intervals' for a binomial proportion give interval estimates that do not actually meet the definition of a confidence interval, in that their coverages are sometimes lower than the nominal confidence level. The methods are favoured because their intervals have a shorter average length than the Clopper-Pearson (gold-standard) method, whose intervals really are confidence intervals. Comparison of such methods is tricky -- the best method should perhaps be the one that gives the shortest intervals (on average), but when is the coverage of a method so poor that it should not be classed as a means of forming confidence intervals? As the definition of a confidence interval is not being adhered to, another criterion for forming interval estimates for a binomial proportion is needed. In this paper we suggest a new criterion; methods which meet the criterion are said to yield $\textit{locally correct confidence intervals}$. We propose a method that yields such intervals and prove that its intervals have a shorter average length than those of any other method that meets the criterion. Compared with the Clopper-Pearson method, the proposed method gives intervals with an appreciably smaller average length. The mid-$p$ method also satisfies the new criterion and has its own optimality property.
Model-Centric Volumetric Point Cloud Attributes

Point clouds have recently gained interest, especially for real-time applications and for 3D-scanned material, such as is used in autonomous driving, architecture, and engineering, to model real estate for renovation or display. Point clouds are associated with geometry information and attributes such as color. Be the color unique or direction-dependent (in the case of plenoptic point clouds), it reflects the colors observed by cameras displaced around the object. Hence, not only are the viewing references assumed, but the illumination spectrum and illumination geometry is also implicit. We propose a model-centric description of the 3D object, that is independent of the illumination and of the position of the cameras. We want to be able to describe the objects themselves such that, at a later stage, the rendering of the model may decide where to place illumination, from which it may calculate the image viewed by a given camera. We want to be able to describe transparent or translucid objects, mirrors, fishbowls, fog and smoke. Volumetric clouds may allow us to describe the air, however ``empty'', and introduce air particles, in a manner independent of the viewer position. For that, we rely on some eletromagnetic properties to arrive at seven attributes per voxel that would describe the material and its color or transparency. Three attributes are for the transmissivity of each color, three are for the attenuation of each color, and another attribute is for diffuseness. These attributes give information about the object to the renderer, with whom lies the decision on how to render and depict each object.