Miracle-dz/UniFAR-FTUs huggingface dataset:
Semimartingale theory of monotone mean–variance portfolio allocation

We study dynamic optimal portfolio allocation for monotone mean–variance preferences in a general semimartingale model. Armed with new results in this area we revisit the work of Cui et al. (2012) and fully characterize the circumstances under which one can set aside a non-negative cash flow while simultaneously improving the mean– variance efficiency of the left-over wealth. The paper analyzes, for the first time, the monotone hull of the Sharpe ratio and highlights its relevance to the problem at hand.
Modelling the light curve of ‘Oumuamua: evidence for torque and disc-like shape

We present the first attempt to fit the light curve of the interstellar visitor ‘Oumuamua using a physical model that includes optional torque. We consider both conventional (Lommel–Seeliger triaxial ellipsoid) and alternative (‘black-and-white ball’, ‘solar sail’) brightness models. With all the brightness models, some torque is required to explain the timings of the most conspicuous features – deep minima – of the asteroid’s light curve. Our best-fitting models are a thin disc (aspect ratio 1:6) and a thin cigar (aspect ratio 1:8) that are very close to being axially symmetric. Both models are tumbling and require some torque that has the same amplitude in relation to ‘Oumuamua’s linear non-gravitational acceleration as in Solar system comets whose dynamics is affected by outgassing. Assuming random orientation of the angular momentum vector, we compute probabilities for our best-fitting models. We show that cigar-shaped models suffer from a fine-tuning problem and have only 16 per cent probability to produce light-curve minima as deep as the ones present in ‘Oumuamua’s light curve. Disc-shaped models, on the other hand, are very likely (at 91 per cent) to produce minima of the required depth. From our analysis, the most likely model for ‘Oumuamua is a thin disc (slab) experiencing moderate torque from outgassing.
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling

Motivated by the many real-world applications of reinforcement learning (RL) that require safe-policy iterations, we consider the problem of off-policy evaluation (OPE) -- the problem of evaluating a new policy using the historical data obtained by different behavior policies -- under the model of nonstationary episodic Markov Decision Processes (MDP) with a long horizon and a large action space. Existing importance sampling (IS) methods often suffer from large variance that depends exponentially on the RL horizon $H$. To solve this problem, we consider a marginalized importance sampling (MIS) estimator that recursively estimates the state marginal distribution for the target policy at every step. MIS achieves a mean-squared error of $$ \frac{1}{n} \sum\nolimits_{t=1}^H\mathbb{E}_{\mu}\left[\frac{d_t^\pi(s_t)^2}{d_t^\mu(s_t)^2} \mathrm{Var}_{\mu}\left[\frac{\pi_t(a_t|s_t)}{\mu_t(a_t|s_t)}\big( V_{t+1}^\pi(s_{t+1}) + r_t\big) \middle| s_t\right]\right] + \tilde{O}(n^{-1.5}) $$ where $\mu$ and $\pi$ are the logging and target policies, $d_t^{\mu}(s_t)$ and $d_t^{\pi}(s_t)$ are the marginal distribution of the state at $t$th step, $H$ is the horizon, $n$ is the sample size and $V_{t+1}^\pi$ is the value function of the MDP under $\pi$. The result matches the Cramer-Rao lower bound in \citet{jiang2016doubly} up to a multiplicative factor of $H$. To the best of our knowledge, this is the first OPE estimation error bound with a polynomial dependence on $H$. Besides theory, we show empirical superiority of our method in time-varying, partially observable, and long-horizon RL environments.
FairLedger: A Fair Blockchain Protocol for Financial Institutions

Financial institutions are currently looking into technologies for permissioned blockchains. A major effort in this direction is Hyperledger, an open source project hosted by the Linux Foundation and backed by a consortium of over a hundred companies. A key component in permissioned blockchain protocols is a byzantine fault tolerant (BFT) consensus engine that orders transactions. However, currently available BFT solutions in Hyperledger (as well as in the literature at large) are inadequate for financial settings; they are not designed to ensure fairness or to tolerate selfish behavior that arises when financial institutions strive to maximize their own profit. We present FairLedger, a permissioned blockchain BFT protocol, which is fair, designed to deal with rational behavior, and, no less important, easy to understand and implement. The secret sauce of our protocol is a new communication abstraction, called detectable all-to-all (DA2A), which allows us to detect participants (byzantine or rational) that deviate from the protocol, and punish them. We implement FairLedger in the Hyperledger open source project, using Iroha framework, one of the biggest projects therein. To evaluate FairLegder's performance, we also implement it in the PBFT framework and compare the two protocols. Our results show that in failure-free scenarios FairLedger achieves better throughput than both Iroha's implementation and PBFT in wide-area settings.
Inflammatory and Senescent Phenotype of Pancreatic Stellate Cells Induced by Sqstm1 Downregulation Facilitates Pancreatic Cancer Progression

Pancreatic ductal adenocarcinoma (PDAC) has unique microenvironment with extensive infiltration of fibroblasts, which are mainly derived from the resident pancreatic stellate cells (PaSCs). As activated PaSCs constitute a major contributor to pancreatic cancer progression, the mechanisms underlying their activation have been being intensively studied. Previous studies showed that Sequestosome-1 (sqstm1) can modulate the functional status of fibroblasts in cancer. Here, we further delineated the role of sqstm1 in PaSCs. The analysis of PDAC patient samples revealed reduction of sqstm1 expression in activated PaSCs in both mRNA and protein level. Downregulated sqstm1 via shRNA in PaSCs led to an inflammatory and senescent phenotype with increased IL8, CXCL1, and CXCL2 expression. Further analysis demonstrated that increased intracellular reactive oxygen species level contributed to the senescence in sqstm1-downregulated PaSCs. This was mediated via impaired NRF2 activity since reduced sqstm1 resulted in accumulation of KEAP1. Meanwhile, we found that sqstm1 degradation caused by enhanced autophagy was not associated with transformation of senescent phenotype. At last, the data revealed that sqstm1-downregulated PaSCs promoted pancreatic tumor cell growth, invasion, and macrophage phenotype transformation. Collectively, the current study indicated that sqstm1 controlled transformation of senescent phenotype of PaSCs, which in turn is pro-tumorigenic.
Absorption and emission modulation in a MoS2–GaN (0001) heterostructure by interface phonon–exciton coupling

Semiconductor heterostructures based on layered two-dimensional transition metal dichalcogenides (TMD) interfaced to gallium nitride (GaN) are excellent material systems to realize broadband light emitters and absorbers. The surface properties of the polar semiconductor, such as GaN are dominated by interface phonons, thus the optical properties of the vertical heterostructure depend strongly on the interface exciton-phonon coupling. The origin and activation of different Raman modes in the heterostructure due to coupling between interfacial phonons and optically generated carriers in a monolayer MoS2-GaN (0001) heterostructure was observed. This coupling strongly influences the non-equilibrium absorption properties of MoS2 and the emission properties of both semiconductors. Density functional theory (DFT) calculations were performed to study the band alignment of the interface, which revealed a type-I heterostructure. The optical excitation with interband transition in MoS2 at K-point strongly modulates the C excitonic band in MoS2. The overlap of absorption and emission bands of GaN with the absorption bands of MoS2 induces the energy and charge transfer across the interface with an optical excitation at {\Gamma}-point. A strong modulation of the excitonic absorption states is observed in MoS2 on GaN substrate with transient optical pump-probe spectroscopy. The interaction of carriers with phonons and defect states leads to the enhanced and blue shifted emission in MoS2 on GaN substrate. Our results demonstrate the relevance of interface coupling between phonons and carriers for the development of optical and electronic applications.
On the Insufficiency of the Large Margins Theory in Explaining the Performance of Ensemble Methods

Boosting and other ensemble methods combine a large number of weak classifiers through weighted voting to produce stronger predictive models. To explain the successful performance of boosting algorithms, Schapire et al. (1998) showed that AdaBoost is especially effective at increasing the margins of the training data. Schapire et al. (1998) also developed an upper bound on the generalization error of any ensemble based on the margins of the training data, from which it was concluded that larger margins should lead to lower generalization error, everything else being equal (sometimes referred to as the ``large margins theory''). Tighter bounds have been derived and have reinforced the large margins theory hypothesis. For instance, Wang et al. (2011) suggest that specific margin instances, such as the equilibrium margin, can better summarize the margins distribution. These results have led many researchers to consider direct optimization of the margins to improve ensemble generalization error with mixed results. We show that the large margins theory is not sufficient for explaining the performance of voting classifiers. We do this by illustrating how it is possible to improve upon the margin distribution of an ensemble solution, while keeping the complexity fixed, yet not improve the test set performance.
Char-RNN for Word Stress Detection in East Slavic Languages

We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russian, Ukranian, Belarusian languages. We present new datasets, annotated with the word stress, for the three languages and compare several RNN models trained on three languages and explore possible applications of the transfer learning for the task. We show that it is possible to train a model in a cross-lingual setting and that using additional languages improves the quality of the results.
Testing dark energy models with a new sample of strong-lensing systems

Inspired by a new compilation of strong lensing systems, which consist of 204 points in the redshift range $0.0625 1$ or high velocity dispersion $\sigma > 276$ km s$^{-1}$). However, we obtained a fiduciary sample with 143 systems which improves the constraints on each tested cosmological model.
Time-dependent 
PT
-symmetric quantum mechanics in generic non-Hermitian systems

A conceptual framework extending (time-independent) $\mathcal{PT}$-symmetric quantum mechanics into the time-dependent domain is presented. It is built upon a nontrivial time-dependent metric operator identified here and works for generic finite-dimensional non-Hermitian systems. All the ingredients of our framework, such as the time-dependent Hilbert space, the observable, and the measurement postulate, can be ``realized'' by means of dilating and reinterpreting the non-Hermitian system in question as a part of a larger Hermitian system. Aided by our metric operator, we formulate the concepts of stable and unstable phases for generic non-Hermitian systems and argue that they, respectively, generalize the notions of unbroken and broken phases in time-independent $\mathcal{PT}$-symmetric systems. Possible applications of our framework are illustrated with well-known examples in quantum thermodynamics.
A Simple Coupled-Bloch-Mode Approach to Study Active Photonic Crystal Waveguides and Lasers

By applying a coupled-Bloch-mode approach, we have derived a simple expression for the transmission properties of photonic crystal (PhC) line-defect waveguides with a complex refractive index perturbation. We have provided physical insights on the coupling mechanism by analyzing the frequency dependence and relative strength of the coupling coefficients. We have shown the impact of the perturbation on the waveguide dispersion relation and how the gain-induced distributed feedback limits the maximum attainable slow-light enhancement of the gain itself. We have then applied our approach to analyze the threshold behavior of various PhC laser cavities and proved the significant impact of coherent distributed feedback effects in these lasers. Importantly, our approach also reveals that a structure simply consisting of an active region with zero back reflections from the passive output waveguides can achieve lasing oscillation with reasonable threshold gain.
DetNAS: Backbone Search for Object Detection

Object detectors are usually equipped with backbone networks designed for image classification. It might be sub-optimal because of the gap between the tasks of image classification and object detection. In this work, we present DetNAS to use Neural Architecture Search (NAS) for the design of better backbones for object detection. It is non-trivial because detection training typically needs ImageNet pre-training while NAS systems require accuracies on the target detection task as supervisory signals. Based on the technique of one-shot supernet, which contains all possible networks in the search space, we propose a framework for backbone search on object detection. We train the supernet under the typical detector training schedule: ImageNet pre-training and detection fine-tuning. Then, the architecture search is performed on the trained supernet, using the detection task as the guidance. This framework makes NAS on backbones very efficient. In experiments, we show the effectiveness of DetNAS on various detectors, for instance, one-stage RetinaNet and the two-stage FPN. We empirically find that networks searched on object detection shows consistent superiority compared to those searched on ImageNet classification. The resulting architecture achieves superior performance than hand-crafted networks on COCO with much less FLOPs complexity.
The Degree of Alignment between Circumbinary Disks and Their Binary Hosts

All four circumbinary (CB) protoplanetary disks orbiting short-period (P < 20 days) double-lined spectroscopic binaries (SB2s)—a group that includes UZ Tau E, for which we present new Atacama Large Millimeter/Submillimeter Array data—exhibit sky-plane inclinations idisk that match, to within a few degrees, the sky-plane inclinations i⋆ of their stellar hosts. Although for these systems the true mutual inclinations θ between disk and binary cannot be directly measured because relative nodal angles are unknown, the near coincidence of idisk and i⋆ suggests that θ is small for these most compact of systems. We confirm this hypothesis using a hierarchical Bayesian analysis, showing that 68% of CB disks around short-period SB2s have θ < 3. °0. Near coplanarity of CB disks implies near coplanarity of CB planets discovered by Kepler, which in turn implies that the occurrence rate of close-in CB planets is similar to that around single stars. By contrast, at longer periods ranging from 30 to 105 days (where the nodal degeneracy can be broken via, e.g., binary astrometry), CB disks exhibit a wide range of mutual inclinations, from coplanar to polar. Many of these long-period binaries are eccentric, as their component stars are too far separated to be tidally circularized. We discuss how theories of binary formation and disk–binary gravitational interactions can accommodate all these observations.
Defending against Adversarial Attacks through Resilient Feature Regeneration

Deep neural network (DNN) predictions have been shown to be vulnerable to carefully crafted adversarial perturbations. Specifically, so-called universal adversarial perturbations are image-agnostic perturbations that can be added to any image and can fool a target network into making erroneous predictions. Departing from existing adversarial defense strategies, which work in the image domain, we present a novel defense which operates in the DNN feature domain and effectively defends against such universal adversarial attacks. Our approach identifies pre-trained convolutional features that are most vulnerable to adversarial noise and deploys defender units which transform (regenerate) these DNN filter activations into noise-resilient features, guarding against unseen adversarial perturbations. The proposed defender units are trained using a target loss on synthetic adversarial perturbations, which we generate with a novel efficient synthesis method. We validate the proposed method for different DNN architectures, and demonstrate that it outperforms existing defense strategies across network architectures by more than 10% in restored accuracy. Moreover, we demonstrate that the approach also improves resilience of DNNs to other unseen adversarial attacks.
Factorization Bandits for Online Influence Maximization

In this paper, we study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with the network, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including influence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper confidence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online influence maximization algorithm. Extensive empirical evaluations on two real-world networks showed the effectiveness of our proposed solution.
Mass-energy equivalence in harmonically trapped particles

Precise understanding of the dynamics of trapped particles is crucial for nascent quantum technologies, including atomic clocks and quantum simulators. Here we present a framework to systematically include quantum effects arising from the mass-energy equivalence in harmonically trapped systems. We find that the mass-energy equivalence leads to squeezing, displacement and frequency changes of harmonic modes associated with different internal energies. The framework predicts new phenomena, notably, the existence of a lower bound to the fractional frequency shift in atomic clocks arising from the interplay between gravitational effects and so-called time dilation shifts. Analogous effects will arise in other trapping potentials, especially in periodic lattices, and may play a role in correlation dynamics and thermalisation process in many-body systems and cold gases.
Predicting TED Talk Ratings from Language and Prosody

We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers. Our dataset contains over 2200 TED Talk transcripts (includes over 200 thousand sentences), audio features and the associated meta information including about 5.5 Million ratings from spontaneous visitors of the website. We propose three neural network architectures and compare with statistical machine learning. Our experiments reveal that it is possible to predict all the 14 different ratings with an average AUC of 0.83 using the transcripts and prosody features only. The dataset and the complete source code is available for further analysis.
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer the classifier learned from the source domain to the target domain in an unsupervised manner. With the help of target pseudo-labels, aligning class-level distributions and learning the classifier in the target domain are two widely used objectives. Existing methods often separately optimize these two individual objectives, which makes them suffer from the neglect of the other. However, optimizing these two aspects together is not trivial. To alleviate the above issues, we propose a novel method that jointly optimizes semantic domain alignment and target classifier learning in a holistic way. The joint optimization mechanism can not only eliminate their weaknesses but also complement their strengths. The theoretical analysis also verifies the favor of the joint optimization mechanism. Extensive experiments on benchmark datasets show that the proposed method yields the best performance in comparison with the state-of-the-art unsupervised domain adaptation methods.
A 5D view of the α Per, Pleiades, and Praesepe clusters

Aims. Our scientific goal is to provide revised membership lists of the α Per, Pleiades, and Praesepe clusters exploiting the second data release of Gaia and produce five-dimensional maps (α, δ, π, μα cos δ, μδ) of these clusters. Methods. We implemented the kinematic method combined with the statistical treatment of parallaxes and proper motions to identify astrometric member candidates of three of the most nearby and best studied open clusters in the sky. Results. We cross-correlated the Gaia catalogue with large-scale public surveys to complement the astrometry of Gaia with multi-band photometry from the optical to the mid-infrared. We identified 517, 1248, and 721 bona fide astrometric member candidates inside the tidal radius of α Per, the Pleiades, and Praesepe, respectively. We cross-matched our final samples with catalogues from previous surveys to address the level of completeness. We update the main physical properties of the clusters, including mean distance and velocity, as well as core, half-mass, and tidal radii. We infer updated ages from the white dwarf members of the Pleiades and Praesepe. We derive the luminosity and mass functions of the three clusters and compare them to the field mass function. We compute the positions in space of all member candidates in the three regions to investigate their distribution in space. Conclusions. We provide updated distances and kinematics for the three clusters. We identify a list of members in the α Per, Pleiades, and Praesepe clusters from the most massive stars all the way down to the hydrogen-burning limit with a higher confidence and better astrometry than previous studies. We produce complete 5D maps of stellar and substellar bona fide members in these three regions. The photometric sequences derived in several colour–magnitude diagrams represent benchmark cluster sequences at ages from 90 to 600 Myr. We note the presence of a stream around the Pleiades cluster extending up to 40 pc from the cluster centre.