Miracle-dz/UniFAR-FTUs huggingface dataset:
Arsenic — a Review. Part I: Occurrence, Toxicity, Speciation, Mobility

In natural waters arsenic concentrations up to a few milligrams per litre were measured. The natural content of arsenic found in soils varies between 0.01 mg/kg and a few hundred milligrams per kilogram. Anthropogenic sources of arsenic in the environment are the smelting of ores, the burning of coal, and the use of arsenic compounds in many products and production processes in the past. A lot of arsenic compounds are toxic and cause acute and chronic poisoning. In aqueous environment the inorganic arsenic species arsenite (As(III)) and arsenate (As(V)) are the most abundant species. The mobility of these species is influenced by the pH value, the redox potential, and the presence of adsorbents such as oxides and hydroxides of Fe(III), Al(III), Mn(III/IV), humic substances, and clay minerals.
Compact ultrafast reflective Fabry-Perot tunable lasers for OCT imaging applications

We demonstrate a novel reflective Fabry-Perot tunable laser (RFPTL) for high speed swept-source optical coherence tomography (OCT) imaging applications. This external cavity semiconductor laser uses a silicon MEMS tunable Fabry- Perot filter in a novel reflective mode of operation. The laser is packaged in a compact 25x15 mm fiber-pigtailed butterfly package. Lasers at 1060 and 1300 nm wavelengths have been demonstrated with tuning ranges up to 140 nm, fast scan rates of 100 kHz, and coherence lengths greater than 13 mm. We also describe OCT imaging with these lasers. RFPTL lasers with 1-2 μm wavelengths and tuning ranges of 250 nm have also been demonstrated for a wide range of applications.
The 2:1 vanadyl acetylacetonate:1,4-dioxan complex. A nuclear magnetic resonance and X-ray crystal structure study

Vanadyl acetylacetonate interacts with 1,4-dioxan in methylene dichloride solution, and a complex [VO(acac)2]2(dioxan), C24H40O12V2, has been isolated; a dioxan molecule bridges two VO(acac)2 groups by co-ordination to the sixth ‘unoccupied’ site of the vanadium atom.
A Phase Field Crystal Method for Multilayer Graphene

Bilayer graphene has been a subject of intense study in recent years. We extend a structural phase field crystal method to include an external potential from adjacent layer(s), which is generated by the corresponding phase field and changes over time. Moreover, multiple layers can be added into the structure. Using the thickness of the boundaries between different stacking variants of the bilayer structure as the key parameter, we quantify the strength of the adjacent layer potential by comparing it with atomistic simulation results. Then, we test the multiple graphene structures, including bilayers, triple layers, and up to 6 layers. We find that in addition to the initial conditions, the manner in which new layers are added into the structure affects the layout of the atomic configuration. Finally, we simulate the case of twisted stacking bilayer graphene under the “magic angle 1.1° ”, and our results help identify the key physical ingredients to predict what relaxations may occur.
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization

In this work, we introduce EQ-Net: the first holistic framework that solves both the tasks of log-likelihood ratio (LLR) estimation and quantization using a data-driven method. We motivate our approach with theoretical insights on two practical estimation algorithms at the ends of the complexity spectrum and reveal a connection between the complexity of an algorithm and the information bottleneck method: simpler algorithms admit smaller bottlenecks when representing their solution. This motivates us to propose a two-stage algorithm that uses LLR compression as a pretext task for estimation and is focused on low-latency, high-performance implementations via deep neural networks. We carry out extensive experimental evaluation and demonstrate that our single architecture achieves state-of-the-art results on both tasks when compared to previous methods, with gains in quantization efficiency as high as $20\%$ and reduced estimation latency by up to $60\%$ when measured on general purpose and graphical processing units (GPU). In particular, our approach reduces the GPU inference latency by more than two times in several multiple-input multiple-output (MIMO) configurations. Finally, we demonstrate that our scheme is robust to distributional shifts and retains a significant part of its performance when evaluated on 5G channel models, as well as channel estimation errors.
Compliance Generation for Privacy Documents under GDPR: A Roadmap for Implementing Automation and Machine Learning

Most prominent research today addresses compliance with data protection laws through consumer-centric and public-regulatory approaches. We shift this perspective with the Privatech project to focus on corporations and law firms as agents of compliance. To comply with data protection laws, data processors must implement accountability measures to assess and document compliance in relation to both privacy documents and privacy practices. In this paper, we survey, on the one hand, current research on GDPR automation, and on the other hand, the operational challenges corporations face to comply with GDPR, and that may benefit from new forms of automation. We attempt to bridge the gap. We provide a roadmap for compliance assessment and generation by identifying compliance issues, breaking them down into tasks that can be addressed through machine learning and automation, and providing notes about related developments in the Privatech project.
Superconductivity in dilute system of sites with strong electron-electron attraction

We consider the effect of strong electron-electron attraction on superconductivity in the dilute system of the negative U-centers. They couple to the conducting electronic states and mediate attraction between electrons. We predict the formation of the local Cooper pairs provided attraction exceeds a certain threshold value. The global coherent superconducting state appears only due to the Andreev scattering between the centers at low temperatures. The dependence of the superconducting transition temperature and the second critical magnetic field on attraction strength is calculated.
Differential subordinations for functions with positive real part using admissibility conditions

Some sufficient conditions on certain constants which are involved in some first, second and third order differential subordinations associated with certain functions with positive real part like modified Sigmoid function, exponential function and Janowski function are obtained so that the analytic function p normalized by the condition p(0) = 1, is subordinate to Janowski function. The admissibility conditions for Janowski function are used as a tool in the proof of the results. As application, several sufficient conditions are also computed for Janowski starlikeness.
Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network

Wireless Virtual Reality (VR) users are able to enjoy immersive experience from anywhere at anytime. However, providing full spherical VR video with high quality under limited VR interaction latency is challenging. If the viewpoint of the VR user can be predicted in advance, only the required viewpoint is needed to be rendered and delivered, which can reduce the VR interaction latency. Therefore, in this paper, we use offline and online learning algorithms to predict viewpoint of the VR user using real VR dataset. For the offline learning algorithm, the trained learning model is directly used to predict the viewpoint of VR users in continuous time slots. While for the online learning algorithm, based on the VR user's actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy. To guarantee the reliability of the uplink transmission, we integrate the Proactive retransmission scheme into our proposed online learning algorithm. Simulation results show that our proposed online learning algorithm for uplink wireless VR network with the proactive retransmission scheme only exhibits about 5% prediction error.
Estimation of Driver’s Gaze Region From Head Position and Orientation Using Probabilistic Confidence Regions

Visual attention is one of the most important aspects related to driver distraction. Estimating the driver’s visual attention can help a vehicle understand the awareness state of the driver, providing important contextual information. While estimating the exact gaze direction is difficult in the car environment, a coarse estimation of the visual attention can be obtained by tracking the head pose. Since the relation between head pose and gaze direction is not one-to-one, this paper proposes a formulation based on probabilistic models to create salient regions describing the driver’s visual attention. The area of the estimated region is small when the model has high confidence, which is directly learned from the data. We use Gaussian process regression (GPR) to implement the framework, comparing the performance with different regression formulations such as linear regression and neural network based methods. We evaluate these frameworks by studying the tradeoff between spatial resolution and accuracy of the probability map using naturalistic recordings collected with the UTDrive platform. We observe that the GPR method produces the best result creating accurate estimations with localized salient regions. For example, the 95% confidence region is defined by an area covering 3.77% region of a sphere surrounding the driver.
Superhuman Surgical Peg Transfer Using Depth-Sensing and Deep Recurrent Neural Networks

We consider the automation of the well-known peg-transfer task from the Fundamentals of Laparoscopic Surgery (FLS). While human surgeons teleoperate robots to perform this task with great dexterity, it remains challenging to automate. We present an approach that leverages emerging innovations in depth sensing, deep learning, and Peiper’s method for computing inverse kinematics with time-minimized joint motion. We use the da Vinci Research Kit (dVRK) surgical robot with a Zivid depth sensor, and automate three variants of the peg-transfer task: unilateral, bilateral without handovers, and bilateral with handovers. We use 3D-printed ﬁducial markers with depth sensing and a deep recurrent neural network to improve the precision of the dVRK to less than 1 mm. We report experimental results for 1800 block transfer trials. Results suggest that the fully automated system can outperform an experienced human surgical resident, who performs far better than untrained humans, in terms of both speed and success rate. For the most diﬃcult variant of peg transfer (with handovers) we compare the performance of the surgical resident with performance of the automated system over 120 trials for each. The experienced surgical resident achieves success rate 93.2 % with mean transfer time of 8.6 seconds. The automated system achieves success rate 94.1 % with mean transfer time of 8.1 seconds. To our knowledge this is the ﬁrst fully automated system to achieve “superhuman” performance in both speed and success on peg transfer. Supplementary material is available at https://sites.google. com/view/surgicalpegtransfer .
BENN: Bias Estimation Using Deep Neural Network

The need to detect bias in machine learning (ML) models has led to the development of multiple bias detection methods, yet utilizing them is challenging since each method: i) explores a different ethical aspect of bias, which may result in contradictory output among the different methods, ii) provides an output of a different range/scale and therefore, can't be compared with other methods, and iii) requires different input, and therefore a human expert needs to be involved to adjust each method according to the examined model. In this paper, we present BENN -- a novel bias estimation method that uses a pretrained unsupervised deep neural network. Given a ML model and data samples, BENN provides a bias estimation for every feature based on the model's predictions. We evaluated BENN using three benchmark datasets and one proprietary churn prediction model used by a European Telco and compared it with an ensemble of 21 existing bias estimation methods. Evaluation results highlight the significant advantages of BENN over the ensemble, as it is generic (i.e., can be applied to any ML model) and there is no need for a domain expert, yet it provides bias estimations that are aligned with those of the ensemble.
Commission Fee is not Enough: A Hierarchical Reinforced Framework for Portfolio Management

Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error. Existing methods are impractical since they usually assume each reallocation can be finished immediately and thus ignoring the price slippage as part of the trading cost. To address these issues, we propose a hierarchical reinforced stock trading system for portfolio management (HRPM). Concretely, we decompose the trading process into a hierarchy of portfolio management over trade execution and train the corresponding policies. The high-level policy gives portfolio weights at a lower frequency to maximize the long-term profit and invokes the low-level policy to sell or buy the corresponding shares within a short time window at a higher frequency to minimize the trading cost. We train two levels of policies via a pre-training scheme and an iterative training scheme for data efficiency. Extensive experimental results in the U.S. market and the China market demonstrate that HRPM achieves significant improvement against many state-of-the-art approaches.
Astrophysical properties of newly discovered Magellanic Cloud star clusters

New star cluster candidates projected toward the Large and Small Magellanic Clouds (L/SMC) have been recently discovered from relatively deep imaging surveys. We here conduct a sound analysis of 24 star cluster candidates located in the outer regions of the L/SMC using PSF photometry produced by the Survey of the Magellanic Stellar History. With only one exception, the studied objects resulted to be genuine stellar aggregates. We conclude on their physical reality once their observed color-magnitude diagrams (CMDs) were statistically decontaminated by the presence of field stars, and the resulting cleaned CMDs for stars with assigned membership probabilities higher than 50% were compared with synthetic CMDs generated for thousand combinations of ages, distances, metallicities, star cluster mass and binary fractions. The parameter of the best-matched synthetic CMDs obtained from a likelihood approach were adopted as the star cluster astrophysical properties. The present star cluster sample spans a wide range of distances, from those star clusters located in front of the LMC, to those along the onset of the Magellanic Bridge, up to those behind the SMC. Their ages reveal different formation episodes that took place along the galaxy formation and others as a consequence of the galaxy interactions. From their estimated metallicities and ages, we speculate with the possibility that relatively metal-deficient gaseous flows have existed between these galaxies during nearly the last one Gyr (log(age /yr) ≈ 9.0), that made possible the formation of young star clusters in the galaxy peripheries. Despide the L/SMC interactions, the studied star clusters are similar or more massive than their counterparts in the Milky Way, which suggests that tidal effects are relatively more important in our Galaxy.
A Flat-Combining-Based Persistent Stack for Non-Volatile Memory

Flat combining (FC) is a synchronization paradigm in which a single thread, holding a global lock, collects requests by multiple threads for accessing a concurrent data structure and applies their combined requests to it. Although FC is sequential, it significantly reduces synchronization overheads and cache invalidations and thus often provides better performance than that of lock-free implementations. The recent emergence of non-volatile memory (NVM) technologies increases the interest in the development of persistent (a.k.a. durable or recoverable ) objects. These are objects that are able to recover from system failures and ensure consistency by retaining their state in NVM and fixing it, if required, upon recovery. Of particular interest are detectable objects that, in addition to ensuring consistency, allow recovery code to infer if a failed operation took effect before the crash and, if it did, obtain its response. In this work, we present the first FC-based persistent object. Specifically, we introduce a detectable FC-based implementation of a concurrent LIFO stack object. Our empirical evaluation establishes that thanks to the usage of flat combining, the novel stack algorithm requires a much smaller number of costly persistence instructions than competing algorithms and is therefore able to significantly outperform them.
Model checking and model synthesisfrom partial models: a logic-based perspective

I consider the following generic scenario: an abstract model M of some ‘real’ system is only partially presented, or partially known to us, and we have to ensure that the actual system satisfies a given specification, formalised in some logical language. This scenario has at least two essentially different interpretations, leading to two, essentially different, formal logical and algorithmic problems: “Model Synthesis from Partial Models”, where some ‘admissible’ extension of M to a full model must satisfy the specification, and “Model Checking of Partial Models”, where all ‘admissible’ extensions of M to a full model must satisfy the specification. These problems naturally extend the classical logical decision problems of Satisfiability, Validity, and Model Checking. Here I briefly discuss both problems in the contexts of classical, modal and temporal logics. I make some observations, state some open questions, and outline a general tableaux-style procedure that solves the problem of unconstrained model synthesis from finite partial models for several well-known modal and temporal logics, incl. K, LTL, CTL, ATL.
Existence and Computation of Maximin Fair Allocations Under Matroid-Rank Valuations

We study fair and economically efficient allocation of indivisible goods among agents whose valuations are rank functions of matroids. Such valuations constitute a well-studied class of submodular functions (i.e., they exhibit a diminishing returns property) and model preferences in several resource-allocation settings. We prove that, for matroid-rank valuations, a social welfare-maximizing allocation that gives each agent her maximin share always exists. Furthermore, such an allocation can be computed in polynomial time. We establish similar existential and algorithmic results for the pairwise maximin share guarantee as well. To complement these results, we show that if the agents have binary XOS valuations or weighted-rank valuations, then maximin fair allocations are not guaranteed to exist. Both of these valuation classes are immediate generalizations of matroid-rank functions.
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous work. Pit30M is captured under diverse conditions (i.e., season, weather, time of the day, traffic), and provides accurate localization ground truth. We also automatically annotate our dataset with historical weather and astronomical data, as well as with image and LiDAR semantic segmentation as a proxy measure for occlusion. We benchmark multiple existing methods for image and LiDAR retrieval and, in the process, introduce a simple, yet effective convolutional network-based LiDAR retrieval method that is competitive with the state of the art. Our work provides, for the first time, a benchmark for sub-metre retrieval-based localization at city scale.The dataset, additional experimental results, as well as more information about the sensors, calibration, and metadata, are available on the project website: https://uber.com/atg/datasets/pit30m
Superconducting properties of the non-centrosymmetric superconductors TaXSi (X = Re, Ru)

Ternary noncentrosymmetric superconductors TaXSi (X = Re, Ru) investigated by magnetization, resistivity, and specific heat measurements. It crystallize in the orthorhombic TiFeSi-type structure with superconducting transition Tc = 5.32 K and 3.91 K for TaReSi and TaRuSi, respectively. A low value of specific heat jump and the upper critical field concave nature suggests a nontrivial superconducting gap.
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [1] demonstrates that random shuffling amplifies differential privacy guarantees of locally randomized data. Such amplification implies substan-tially stronger privacy guarantees for systems in which data is contributed anonymously [2] and has lead to significant interest in the shuffle model of privacy [3], [1]. We give a characterization of the privacy guarantee of the random shuffling of $\mathbf{n}$ data records input to epsilon-differentially private local randomizers that significantly im-proves over previous work and achieves the asymptotically optimal dependence in epsilon. Our result is based on a new approach that is simpler than previous work and extends to approximate differential privacy with nearly the same guarantees. Importantly, our work also yields an algorithm for deriving tighter bounds on the resulting epsilon and delta as well as Rényi differential privacy guarantees. We show numerically that our algorithm gets to within a small constant factor of the optimal bound. As a direct corollary of our analysis we derive a simple and nearly optimal algorithm for frequency estimation in the shuffle model of privacy. We also observe that our result implies the first asymptotically optimal privacy analysis of noisy stochastic gradient descent that applies to sampling without replacement.
Learned Indexes for a Google-scale Disk-based Database

There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees. In this paper, we work to remove some of that uncertainty by demonstrating how a learned index can be integrated in a distributed, disk-based database system: Google's Bigtable. We detail several design decisions we made to integrate learned indexes in Bigtable. Our results show that integrating learned index significantly improves the end-to-end read latency and throughput for Bigtable.