Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting to misspecification in contextual bandits with offline regression oracles

Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is not well-specified, the bandit algorithm may incur unexpected regret, so recent work has focused on algorithms that are robust to misspecification. We propose a simple family of contextual bandit algorithms that adapt to misspecification error by reverting to a good safe policy when there is evidence that misspecification is causing a regret increase. Our algorithm requires only an offline regression oracle to ensure regret guarantees that gracefully degrade in terms of a measure of the average misspecification level. Compared to prior work, we attain similar regret guarantees, but we do no rely on a master algorithm, and do not require more robust oracles like online or constrained regression oracles (e.g., Foster et al. (2020a); Krishnamurthy et al. (2020)). This allows us to design algorithms for more general function approximation classes.
The ensemble conditional variance estimator for sufficient dimension reduction

: Ensemble Conditional Variance Estimation ( ECVE ) is a novel suﬃcient dimension reduction (SDR) method in regressions with continuous response and predictors. ECVE applies to general non-additive error regression models and operates under the assumption that the predictors can be replaced by a lower dimensional projection without loss of infor- mation. It is a semiparametric forward regression model-based exhaustive suﬃcient dimension reduction estimation method that is shown to be con- sistent under mild assumptions. ECVE outperforms central subspace mean average variance estimation ( csMAVE ), its main competitor, under several simulation settings and in a benchmark data set analysis.
GraphSense: A General-Purpose Cryptoasset Analytics Platform

There is currently an increasing demand for cryptoasset analysis tools among cryptoasset service providers, the financial industry in general, as well as across academic fields. At the moment, one can choose between commercial services or low-level open-source tools providing programmatic access. In this paper, we present the design and implementation of another option: the GraphSense Cryptoasset Analytics Platform, which can be used for interactive investigations of monetary flows and, more importantly, for executing advanced analytics tasks using a standard data science tool stack. By providing a growing set of open-source components, GraphSense could ultimately become an instrument for scientific investigations in academia and a possible response to emerging compliance and regulation challenges for businesses and organizations dealing with cryptoassets.
Q-VR: system-level design for future mobile collaborative virtual reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.
Nested-block self-attention for robust radiotherapy planning segmentation

Although deep convolutional networks have been widely studied for head and neck (HN) organs at risk (OAR) segmentation, their use for routine clinical treatment planning is limited by a lack of robustness to imaging artifacts, low soft tissue contrast on CT, and the presence of abnormal anatomy. In order to address these challenges, we developed a computationally efficient nested block self-attention (NBSA) method that can be combined with any convolutional network. Our method achieves computational efficiency by performing non-local calculations within memory blocks of fixed spatial extent. Contextual dependencies are captured by passing information in a raster scan order between blocks, as well as through a second attention layer that causes bidirectional attention flow. We implemented our approach on three different networks to demonstrate feasibility. Following training using 200 cases, we performed comprehensive evaluations using conventional and clinical metrics on a separate set of 172 test scans sourced from external and internal institution datasets without any exclusion criteria. NBSA required a similar number of computations (15.7 gflops) as the most efficient criss-cross attention (CCA) method and generated significantly more accurate segmentations for brain stem (Dice of 0.89 vs. 0.86) and parotid glands (0.86 vs. 0.84) than CCA. NBSA’s segmentations were less variable than multiple 3D methods, including for small organs with low soft-tissue contrast such as the submandibular glands (surface Dice of 0.90). Our method has been in routine clinical use since May, 2020. It reduced contouring effort by 61% compared to manual contouring and the dose distribution metrics derived from our method were strongly correlated (R2 > 0.9, r = 1) with those derived from manual segmentations. In conclusion, we developed and clinically implemented a new self-attention based OAR segmentation method that is robust to routinely seen clinical conditions for HN radiation therapy treatments. © 2021 Elsevier B. V. All rights reserved. ar X iv :2 10 2. 13 54 1v 1 [ cs . C V ] 2 6 Fe b 20 21 2 / Medical Image Analysis (2021)
Structure and reactivity of V2O5: bulk solid, nanosized clusters, species supported on silica and alumina, cluster cations and anions

Vanadyl bond dissociation energies are calculated by density functional theory (DFT). While the hybrid (B3LYP) functional results are close to the available reference data, gradient corrected functionals (BP86, PBE) yield large errors (about 50 to 100 kJ mol−1), but reproduce trends correctly. PBE calculations on a V20O62H24 cluster model for the (001) surface of V2O5 crystals virtually reproduce periodic slab calculations. The low bond dissociation energy (formation of oxygen surface defect) of 113 kJ mol−1 (B3LYP) is due to substantial structure relaxations leading to formation of V–O–V bonds between the V2O5 layers of the crystal. This relaxation cannot occur in polyhedral (V2O5)n clusters and also not for V2O5 species supported on silica or alumina (represented by cage-type models) for which bond dissociation energies of 250–300 kJ mol−1 are calculated. The OV(OCH3)3 molecule and its dimer are also considered. Radical cations V2O5+ and V4O10+ have very low bond dissociation energies (22 and 14 kJ mol−1, respectively), while the corresponding radical anions have higher dissociation energies (about 330 kJ mol−1) than the neutral clusters. The bond dissociation energies of the closed shell V3O7+ cation (165 kJ mol−1) and the closed shell V3O8− anion (283 kJ mol−1) are closest to the values of the neutral clusters. This makes them suitable for gas phase studies which aim at comparisons with V2O5 species on supporting oxides.
Deep extragalactic visible legacy survey (DEVILS): stellar mass growth by morphological type since z = 1

Using high-resolution Hubble Space Telescope imaging data, we perform a visual morphological classification of ∼ 36, 000 galaxies at z < 1 in the DEVILS/COSMOS region. As the main goal of this study, we derive the stellar mass function (SMF) and stellar mass density (SMD) sub-divided by morphological types. We find that visual morphological classification using optical imaging is increasingly difficult at z > 1 as the fraction of irregular galaxies and merger systems (when observed at rest-frame UV/blue wavelengths) dramatically increases. We determine that roughly two-thirds of the total stellar mass of the Universe today was in place by z ∼ 1. Double-component galaxies dominate the SMD at all epochs and increase in their contribution to the stellar mass budget to the present day. Elliptical galaxies are the second most dominant morphological type and increase their SMD by ∼ 2.5 times, while by contrast, the pure-disk population significantly decreases by ∼ 85%. According to the evolution of both highand low-mass ends of the SMF, we find that mergers and in-situ evolution in disks are both present at z < 1, and conclude that double-component galaxies are predominantly being built by the in-situ evolution in disks (apparent as the growth of the low-mass end with time), while mergers are likely responsible for the growth of ellipticals (apparent as the increase of intermediate/high-mass end).
Joint Resource Block and Beamforming Optimization for Cellular-Connected UAV Networks: A Hybrid D3QN-DDPG Approach

Integrating unmanned aerial vehicles (UAVs) into the existing cellular networks faces lots of challenges, in which one of the most striking concerns is how to adopt UAV into cellular networks with less adverse effects to ground users. In this paper, a cellular-connected UAV network is considered, where multiple UAVs receive messages from terrestrial base stations (BSs) in the down-link, while BSs are serving ground users in their cells. To enhance wireless transmission quality for the UAVs while protecting the ground users from being interfered, a joint time-frequency resource block (RB) and beamforming optimization problem minimizing the ergodic outage duration (EOD) of UAV is investigated. To solve the proposed optimization problem, a deep reinforcement learning (DRL) solution is proposed, where deep double duelling Q network (D3QN) and deep deterministic policy gradient (DDPG) are invoked to deal with RB allocation in discrete action domain and beamforming design in continuous action regime, respectively. The hybrid D3QN-DDPG solution is applied to solve the outer Markov decision process (MDP) and the inner MDP interactively so that it can achieve the sub-optimal result for the considered optimization problem. Simulation results illustrate the effectiveness of the proposed hybrid D3QN-DDPG algorithm, compared to exhaustive/random search-based benchmarks.
New Techniques that Improve ENIGMA-style Clause Selection Guidance

We re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a Recursive Neural Network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of smt-lib in a real time evaluation.
Moreau-Yosida f-divergences

Variational representations of f -divergences are central to many machine learning algorithms, with Lipschitz constrained variants recently gaining attention. Inspired by this, we deﬁne the Moreau-Yosida approximation of f -divergences with respect to the Wasserstein- 1 metric. The corresponding variational formulas provide a generalization of a number of recent results, novel special cases of interest and a relaxation of the hard Lipschitz constraint. Additionally, we prove that the so-called tight variational representation of f - divergences can be to be taken over the quotient space of Lipschitz functions, and give a characterization of functions achieving the supremum in the variational representation. On the practical side, we propose an algorithm to calculate the tight convex conjugate of f -divergences compatible with automatic differentiation frameworks. As an application of our results, we propose the Moreau-Yosida f -GAN, providing an implementation of the variational formulas for the Kullback-Leibler, reverse Kullback-Leibler, χ 2 , reverse χ 2 , squared Hellinger, Jensen-Shannon, Jeffreys, triangular discrimination and total variation divergences as GANs trained on CIFAR-10, leading to competitive results and a simple solution to the problem of uniqueness of the optimal critic.
ANEA: Distant Supervision for Low-Resource Named Entity Recognition

Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant supervision must be easy to obtain. In this work, we present ANEA, a tool to automatically annotate named entities in text based on entity lists. It spans the whole pipeline from obtaining the lists to analyzing the errors of the distant supervision. A tuning step allows the user to improve the automatic annotation with their linguistic insights without having to manually label or check all tokens. In six low-resource scenarios, we show that the F1-score can be increased by on average 18 points through distantly supervised data obtained by ANEA.
Non-equilibrium quadratic measurement-feedback squeezing in a micromechanical resonator

Measurement and feedback control of stochastic dynamics has been actively studied for not only stabilizing the system but also for generating additional entropy flows originating in the information flow in the feedback controller. In particular, a micromechanical system offers a great platform to investigate such non-equilibrium dynamics under measurement-feedback control owing to its precise controllability of small fluctuations. Although various types of measurementfeedback protocols have been demonstrated with linear observables (e.g., displacement and velocity), extending them to the nonlinear regime, i.e., utilizing nonlinear observables in both measurement and control, retains non-trivial phenomena in its non-equilibrium dynamics. Here, we demonstrate measurement-feedback control of a micromechanical resonator by driving the second-order nonlinearity (i.e., parametric squeezing) and directly measuring quadratic observables, which are given by the Schwinger representation of pseudo angular momentum (referred as Schwinger angular momentum). In contrast to that the parametric divergence occurs when the secondorder nonlinearity is blindly driven, our measurement-feedback protocol enables us to avoid such a divergence and to achieve a strong noise reduction at the level of −5.1± 0.2 dB. This strong noise reduction originates in the effective cooling included in our measurement-feedback protocol, which is unveiled by investigating entropy production rates in a coarse-grained model. Our results open up the possibility of not only improving noise-limited sensitivity performance but also investigating entropy production in information thermodynamic machines with nonlinear measurement and feedback.
Shunt removal and patching for crystalline silicon solar cells using infrared imaging and laser cutting

Shunts in crystalline silicon solar cells can be physically removed and replaced with good cells to eliminate their influences, which is proved by the experiments in this paper. By infrared imaging and laser cutting, the shunted regions near the edges of cell A and in the middle of cell B were identified and removed with their efficiencies increased by 6·8% and 3·0%, respectively. After shunt removal, cell B was patched up with good cells and its final work current further increased from 3·71 to 4·11 A. The result implies that this work could improve the output power and current matching in a module for the repaired cell. Copyright © 2009 John Wiley & Sons, Ltd.
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks

In recent years, convolutional neural networks (CNNs) have enabled ubiquitous image processing applications. As such, CNNs require fast forward propagation runtime to process high-resolution visual streams in real time. This is still a challenging task even with state-of-the-art graphics and tensor processing units. The bottleneck in computational efficiency primarily occurs in the convolutional layers. Performing convolutions in the Fourier domain is a promising way to accelerate forward propagation since it transforms convolutions into elementwise multiplications, which are considerably faster to compute for large kernels. Furthermore, such computation could be implemented using an optical 4f system with orders of magnitude faster operation. However, a major challenge in using this spectral approach, as well as in an optical implementation of CNNs, is the inclusion of a nonlinearity between each convolutional layer, without which CNN performance drops dramatically. Here, we propose a spectral CNN linear counterpart (SCLC) network architecture and its optical implementation. We propose a hybrid platform with an optical front end to perform a large number of linear operations, followed by an electronic back end. The key contribution is to develop a knowledge distillation (KD) approach to circumvent the need for nonlinear layers between the convolutional layers and successfully train such networks. While the KD approach is known in machine learning as an effective process for network pruning, we adapt the approach to transfer the knowledge from a nonlinear network (teacher) to a linear counterpart (student), where we can exploit the inherent parallelism of light. We show that the KD approach can achieve performance that easily surpasses the standard linear version of a CNN and could approach the performance of the nonlinear network. Our simulations show that the possibility of increasing the resolution of the input image allows our proposed 4f optical linear network to perform more efficiently than a nonlinear network with the same accuracy on two fundamental image processing tasks: (i) object classification and (ii) semantic segmentation.
Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels

In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives. We first introduce LAW, an efficient batch acquisition method based on determinantal point processes using the acquisition weighted kernel. Relying on multiple parallel evaluations, LAW enables accelerated search on combinatorial spaces. We then apply the framework to permutation problems, which have so far received little attention in the Bayesian Optimization literature, despite their practical importance. We call this method LAW2ORDER. On the theoretical front, we prove that LAW2ORDER has vanishing simple regret by showing that the batch cumulative regret is sublinear. Empirically, we assess the method on several standard combinatorial problems involving permutations such as quadratic assignment, flowshop scheduling and the traveling salesman, as well as on a structure learning task.
PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning

Even though named entity recognition (NER) has seen tremendous development in recent years, some domain-specific use-cases still require tagging of unique entities, which is not well handled by pre-trained models. Solutions based on enhancing pre-trained models or creating new ones are efficient, but creating reliable labeled training for them to learn on is still challenging. In this paper, we introduce PharmKE, a text analysis platform tailored to the pharmaceutical industry that uses deep learning at several stages to perform an in-depth semantic analysis of relevant publications. The proposed methodology is used to produce reliably labeled datasets leveraging cutting-edge transfer learning, which are later used to train models for specific entity labeling tasks. By building models for the well-known text-processing libraries spaCy and AllenNLP, this technique is used to find Pharmaceutical Organizations and Drugs in texts from the pharmaceutical domain. The PharmKE platform also incorporates the NER findings to resolve co-references of entities and examine the semantic linkages in each phrase, creating a foundation for further text analysis tasks, such as fact extraction and question answering. Additionally, the knowledge graph created by DBpedia Spotlight for a specific pharmaceutical text is expanded using the identified entities. The obtained results with the proposed methodology result in about a 96% F1-score on the NER tasks, which is up to 2% better than those of the fine-tuned BERT and BioBERT models developed using the same dataset. The ultimate benefits of the platform are that pharmaceutical domain specialists may more easily identify the knowledge extracted from the input texts thanks to the platform’s visualization of the model findings. Likewise, the proposed techniques can be integrated into mobile and pervasive systems to give patients more relevant and comprehensive information from scanned medication guides. Similarly, it can provide preliminary insights to patients and even medical personnel on whether a drug from a different vendor is compatible with the patient’s prescription medication.
PASSAT: Single Password Authenticated Secret-Shared Intrusion-Tolerant Storage with Server Transparency

In this paper, we introduce PASSAT, a practical system to boost the security assurance delivered by the current cloud architecture without requiring any changes or cooperation from the cloud service providers. PASSAT is an application transparent to the cloud servers that allows users to securely and efficiently store and access their files stored on public cloud storage based on a single master password. Using a fast and light-weight XOR secret sharing scheme, PASSAT secret-shares users' files and distributes them among n publicly available cloud platforms. To access the files, PASSAT communicates with any k out of n cloud platforms to receive the shares and runs a secret-sharing reconstruction algorithm to recover the files. An attacker (insider or outsider) who compromises or colludes with less than k platforms cannot learn the user's files or modify the files stealthily. To authenticate the user to multiple cloud platforms, PASSAT crucially stores the authentication credentials, specific to each platform on a password manager, protected under the user's master password. Upon requesting access to files, the user enters the password to unlock the vault and fetches the authentication tokens using which PASSAT can interact with cloud storage. Our instantiation of PASSAT based on (2, 3)-XOR secret sharing of Kurihara et al., implemented with three popular storage providers, namely, Google Drive, Box, and Dropbox, confirms that our approach can efficiently enhance the confidentiality, integrity, and availability of the stored files with no changes on the servers.