Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonzero-Sum Stochastic Differential Portfolio Games under a Markovian Regime Switching Model

We consider a nonzero-sum stochastic differential portfolio game problem in a continuous-time Markov regime switching environment when the price dynamics of the risky assets are governed by a Markov-modulated geometric Brownian motion (GBM). The market parameters, including the bank interest rate and the appreciation and volatility rates of the risky assets, switch over time according to a continuous-time Markov chain. We formulate the nonzero-sum stochastic differential portfolio game problem as two utility maximization problems of the sum process between two investors’ terminal wealth. We derive a pair of regime switching Hamilton-Jacobi-Bellman (HJB) equations and two systems of coupled HJB equations at different regimes. We obtain explicit optimal portfolio strategies and Feynman-Kac representations of the two value functions. Furthermore, we solve the system of coupled HJB equations explicitly in a special case where there are only two states in the Markov chain. Finally we provide comparative statics and numerical simulation analysis of optimal portfolio strategies and investigate the impact of regime switching on optimal portfolio strategies.
INFLUENCE OF CONTROLLED RELEASE FERTILIZERS ON Lilium regale SPECIES GROWTH AND FLOWERING

Our research aims to establish the influence of Osmocote Pro fertilizer on ornamental characteristics, photosynthetic pigments content, superoxide dismutase and peroxidase activities for Lillium regale species. The tests were organized in four variants, each with three replicates, each replicate consisting of 10 bulbs. The growth of plants in the four experimental variants was achieved without fertilization in case of the control variant (M) and under fertilization conditions with Osmocote Pro for the rest of variants (V1 - 4g fertilizer/plant; V2 -6g fertilizer/plant and V3 -8g fertilizer/plant). The influence of fertilization on plant growth and development has been assessed by plant height, number of flowers, cups diameter and length. The growth in height and capacity of plant flowering were stimulated by applying the fertilizer, regardless of the dosage used. The application of fertilization with Osmocote Pro at Lilium regale species caused an increase in plant height, number of flowers per plant, cup diameter, the best results being highlighted for the variant at which we have applied the highest dose of fertilizer. Compared with the control, the increasing of the fertilizer concentration determined a further growth in terms of plant height, number of flowers, diameter and length of the cups. At the physiological level, in the fertilized variants we observed an increase in the total content of assimilating pigments, the content of chlorophyll a, b. The results pointed out that plants fertilization was positively correlated with the enzymatic activity, while a decrease of superoxide dismutase activity compared to the control was observed.
Chlorophyll Fluorescence and Organic Acid Oscillations during Transition from CAM to C3-photosynthesis inClusia minor L. (Clusiaceae)

In species of Clusia, switching from C3-photosynthesis (C3-PS) to crassulacean acid metabolism (CAM) may be a means of optimizing water use, plant carbon balance and photon utilization during periods of stress. We ask whether, in perennial species of Clusia, the switch from CAM back to C3-PS is also of ecophysiological significance. Our objective was to investigate the performance of C. minor L. during a short-term shift from CAM to C3-PS. During the transition from CAM to C3-PS, nocturnal malate and citrate accumulation decreased whereas CO2uptake increased during the daytime. However, after 7 d, marked nocturnal accumulation of citrate and 24 h CO2uptake occurred. In contrast to C3-like photosynthesis, a pronounced reduction in the effective quantum yield of photosystem II, ▵F/F′m, together with a sharp increase in non-photochemical quenching were observed during CAM at the beginning and end of the day. After 7 d, integrated CO2uptake over 24 h approximately doubled; however, water use efficiency was reduced three-fold due to increased rates of daytime transpiration. Despite very similar maximum ▵F/F′mbetween CAM and C3-PS, the results suggest that daily photon utilization increases when there is unrestricted CO2uptake directly from the atmosphere. Thus, under well-watered conditions and with increased rates of transpiration, C. minor performing C3-like photosynthesis may overcome the limitations of the storage capacity of the vacuole for overnight organic acid accumulation, improving its daily carbon balance.
In situ Infrared Spectra for Hydrous Forsterite up to 1243 K: Hydration Effect on Thermodynamic Properties

Hydrogen substitution has significant effect on the physical properties of olivine, the most abundant mineral in the upper mantle. We collected high-temperature polarized Fourier Transform infrared (FTIR) spectra on hydrous forsterite (Mg-pure olivine) crystals, which were synthesized at 12 GPa, 1473–1673 K. The modes at 3612, 3578, 3566, 3551 cm−1 show comparable negative temperature dependence, and the magnitude of (∂vi/∂T)P decreases dramatically with frequency increasing. Whereas, the peak at 3477 cm−1, which is attributed to protonation along the O1.O2 edge on the Si tetrahedron, has a positive temperature dependence. The absorbance intensities of all these OH bands remained almost the same when quenched to room temperature. On the other hand, we also evaluate the hydration effect on the thermodynamic properties (heat capacities). For the anhydrous forsterite sample, the intrinsic anharmonicity could significantly increase the heat capacity by 5~6% when extrapolated to 2000 K. Hydration further increase such difference to ~9%, in both the cases of M-substitution or Si-substitution. Hence, hydration in olivine has significant impact on the anharmonic contribution to the thermodynamic properties, as well as Equations of State and equilibrium isotope fractionation β-factor at high-P,T conditions in the deep mantle.
Fermi-Level Depinning in Germanium Using Black Phosphorus as an Interfacial Layer

On substrates such as Ge, GaAs, GaSb, InP and a few other materials, Fermi-level pinning (FLP) is a major concern as it inhibits barrier height modulation (BHM) with different work function metals. We demonstrate for the first time a novel and simple mechanical exfoliation of multilayer black phosphorus (BP) as an interfacial layer (IL) to depin the Fermi level on Ge substrate. Multilayer-BP IL was able to alleviate the critical issue of FLP, as it leads to (i) an increase in reverse current by more than two and a half orders on n-Ge (quasi-Ohmic current-voltage(I-V) characteristics) with a decrease in Schottky barrier height (SBH) of 0.14 eV, and (ii) a decrease in reverse current by two orders on p-Ge (Schottky I-V characteristics) with an increase in SBH of 0.52 eV as compared to devices without IL. The device with multilayer-WSe2 IL, having a high conduction band offset (CBO), leads to poor electrical performance compared to a multilayer-BP IL with low CBO. Based on this study, multilayer-BP can be considered as an ideal candidate for an IL material to resolve FLP issue and achieve BHM for various other pinned semiconductors as well.
Opportunistic View Materialization with Deep Reinforcement Learning

Carefully selected materialized views can greatly improve the performance of OLAP workloads. We study using deep reinforcement learning to learn adaptive view materialization and eviction policies. Our insight is that such selection policies can be effectively trained with an asynchronous RL algorithm, that runs paired counter-factual experiments during system idle times to evaluate the incremental value of persisting certain views. Such a strategy obviates the need for accurate cardinality estimation or hand-designed scoring heuristics. We focus on inner-join views and modeling effects in a main-memory, OLAP system. Our research prototype system, called DQM, is implemented in SparkSQL and we experiment on several workloads including the Join Order Benchmark and the TPC-DS workload. Results suggest that: (1) DQM can outperform heuristic when their assumptions are not satisfied by the workload or there are temporal effects like period maintenance, (2) even with the cost of learning, DQM is more adaptive to changes in the workload, and (3) DQM is broadly applicable to different workloads and skews.
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation

Supervised training a deep neural network aims to "teach" the network to mimic human visual perception that is represented by image-and-label pairs in the training data. Superpixelized (SP) images are visually perceivable to humans, but a conventionally trained deep learning model often performs poorly when working on SP images. To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images. In this paper, we propose a new superpixel-based data augmentation (SPDA) method for training deep learning models for biomedical image segmentation. Our method applies a superpixel generation scheme to all the original training images to generate superpixelized images. The SP images thus obtained are then jointly used with the original training images to train a deep learning model. Our experiments of SPDA on four biomedical image datasets show that SPDA is effective and can consistently improve the performance of state-of-the-art fully convolutional networks for biomedical image segmentation in 2D and 3D images. Additional studies also demonstrate that SPDA can practically reduce the generalization gap.
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametrization leading to multiple trade-offs. For example, the bandwidth in FMCW radars is inversely proportional with both the maximum unambiguous range and range resolution. In this work, we introduce a new method for circumventing radar design trade-offs. We propose the use of recent advances in computer vision, more specifically generative adversarial networks (GANs), to enhance low-resolution radar acquisitions into higher resolution counterparts while maintaining the advantages of the low-resolution parametrization. The capability of the proposed method was evaluated on the velocity resolution and rangeazimuth trade-offs in micro-Doppler signatures and FMCW uniform linear array (ULA) radars, respectively.
ORACLE: Order Robust Adaptive Continual LEarning

The order of the tasks a continual learning model encounters may have large impact on the performance of each task, as well as the task-average performance. This order-sensitivity may cause serious problems in real-world scenarios where fairness plays a critical role (e.g. medical diagnosis). To tackle this problem, we propose a novel order-robust continual learning method, which instead of learning a completely shared set of weights, represent the parameters for each task as a sum of task-shared parameters that captures generic representations and task-adaptive parameters capturing task-specific ones, where the latter is factorized into sparse low-rank matrices in order to minimize capacity increase. With such parameter decomposition, when training for a new task, the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This prevents catastrophic forgetting for old tasks and at the same time make the model less sensitive to the task arrival order. We validate our Order-Robust Adaptive Continual LEarning (ORACLE) method on multiple benchmark datasets against state-of-the-art continual learning methods, and the results show that it largely outperforms those strong baselines with significantly less increase in capacity and training time, as well as obtains smaller performance disparity for each task with different order sequences.
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning

Expert human drivers perform actions relying on traffic laws and their previous experience. While traffic laws are easily embedded into an artificial brain, modeling human complex behaviors which come from past experience is a more challenging task. One of these behaviors is the capability of communicating intentions and negotiating the right of way through driving actions, as when a driver is entering a crowded roundabout and observes other cars movements to guess the best time to merge in. In addition, each driver has its own unique driving style, which is conditioned by both its personal characteristics, such as age and quality of sight, and external factors, such as being late or in a bad mood. For these reasons, the interaction between different drivers is not trivial to simulate in a realistic manner. In this paper, this problem is addressed by developing a microscopic simulator using a Deep Reinforcement Learning Algorithm based on a combination of visual frames, representing the perception around the vehicle, and a vector of numerical parameters. In particular, the algorithm called Asynchronous Advantage Actor-Critic has been extended to a multi-agent scenario in which every agent needs to learn to interact with other similar agents. Moreover, the model includes a novel architecture such that the driving style of each vehicle is adjustable by tuning some of its input parameters, permitting to simulate drivers with different levels of aggressiveness and desired cruising speeds.
Viable Dependency Parsing as Sequence Labeling

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BILSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.
DPOD: Dense 6D Pose Object Detector in RGB images

In this work we propose a new method for simultaneous object detection and 6DoF pose estimation. Unlike most recent techniques for CNN-based object detection and pose estimation, we do not base our approach on the common 2D counterparts, i.e. SSD and YOLO, but propose a new scheme. Instead of regressing 2D or 3D bounding boxes, we output full-sized 2D images containing multiclass object masks and dense 2D-3D correspondences. Having them at hand, a 6D pose is computed for each detected object using the PnP algorithm supplemented with RANSAC. This strategy allows for substantially better pose estimates due to a much higher number of relevant pose correspondences. Furthermore, the method is real-time capable, conceptually simple and not bound to any particular detection paradigms, such as R-CNN, SSD or YOLO. We test our method for singleand multiple-object pose estimation and compare the performance with the former state-of-theart approaches. Moreover, we demonstrate how to use our pipeline when only synthetic renderings are available. In both cases, we outperform the former state-of-the-art by a large margin.
Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation

State-of-the-art singing voice separation is based on deep learning making use of CNN structures with skip connections (like U-Net model, Wave-U-Net model, or MSDENSELSTM). A key to the success of these models is the availability of a large amount of training data. In the following study, we are interested in singing voice separation for mono signals and will investigate into comparing the U-Net and the Wave-U-Net that are structurally similar, but work on different input representations. First, we report a few results on variations of the U-Net model. Second, we will discuss the potential of state of the art speech and music transformation algorithms for augmentation of existing data sets and demonstrate that the effect of these augmentations depends on the signal representations used by the model. The results demonstrate a considerable improvement due to the augmentation for both models. But pitch transposition is the most effective augmentation strategy for the U-Net model, while transposition, time stretching, and formant shifting have a much more balanced effect on the Wave-U-Net model. Finally, we compare the two models on the same dataset.
Near-optimal demand side management in retail electricity markets with coupling constraints via indirect mechanism design

Recently there have been several historical changes in electricity networks that necessitate the development of Demand Side Management (DSM). The main objective of DSM is to achieve an aggregated consumption pattern that is efficient in terms of energy cost reduction, welfare maximization and satisfaction of network constraints. This is generally envisaged by encouraging electricity use at low-peak times. In this paper, we consider a system with strategic, price anticipating consumers with private preferences that choose their electricity consumption patterns so as to maximize their own benefit. In this context, we take on the problem of coordinating the consumers' consumption behavior without sacrificing their welfare (Quality of Experience). In order to tackle this problem, we draw on concepts of indirect mechanism design and propose a DSM architecture that is able to fulfill specific system-wide constraints (e.g. energy cost reduction) and simultaneously achieve welfare that is very close to optimal. The proposed billing rule preserves both the budget-balance and the individual rationality properties. According to our evaluation, the proposed DSM architecture achieves a close to optimal allocation (1%-3% gap), compared to an "optimal" system that would use central optimization of user loads without user consensus or protection of their privacy
Condition-Invariant Multi-View Place Recognition

Visual place recognition is particularly challenging when places suffer changes in its appearance. Such changes are indeed common, e.g., due to weather, night/day or seasons. In this paper we leverage on recent research using deep networks, and explore how they can be improved by exploiting the temporal sequence information. Specifically, we propose 3 different alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep networks to use several frames of a sequence. We show that our approaches produce more compact and best performing descriptors than single- and multi-view baselines in the literature in two public databases.
Dijkstra monads for all

This paper proposes a general semantic framework for verifying programs with arbitrary monadic side-effects using Dijkstra monads, which we define as monad-like structures indexed by a specification monad. We prove that any monad morphism between a computational monad and a specification monad gives rise to a Dijkstra monad, which provides great flexibility for obtaining Dijkstra monads tailored to the verification task at hand. We moreover show that a large variety of specification monads can be obtained by applying monad transformers to various base specification monads, including predicate transformers and Hoare-style pre- and postconditions. For defining correct monad transformers, we propose a language inspired by Moggi's monadic metalanguage that is parameterized by a dependent type theory. We also develop a notion of algebraic operations for Dijkstra monads, and start to investigate two ways of also accommodating effect handlers. We implement our framework in both Coq and F*, and illustrate that it supports a wide variety of verification styles for effects such as exceptions, nondeterminism, state, input-output, and general recursion.
Using Deep Object Features for Image Descriptions

Inspired by recent advances in leveraging multiple modalities in machine translation, we introduce an encoder-decoder pipeline that uses (1) specific objects within an image and their object labels, (2) a language model for decoding joint embedding of object features and the object labels. Our pipeline merges prior detected objects from the image and their object labels and then learns the sequences of captions describing the particular image. The decoder model learns to extract descriptions for the image from scratch by decoding the joint representation of the object visual features and their object classes conditioned by the encoder component. The idea of the model is to concentrate only on the specific objects of the image and their labels for generating descriptions of the image rather than visual feature of the entire image. The model needs to be calibrated more by adjusting the parameters and settings to result in better accuracy and performance.
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

RGB images differentiate from depth as they carry more details about the color and texture information, which can be utilized as a vital complement to depth for boosting the performance of 3D semantic scene completion (SSC). SSC is composed of 3D shape completion (SC) and semantic scene labeling while most of the existing approaches use depth as the sole input which causes the performance bottleneck. Moreover, the state-of-the-art methods employ 3D CNNs which have cumbersome networks and tremendous parameters. We introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Our method demonstrates excellent performance on two public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters and 16.6% FLOPs employed compared with that of SSCNet.
Robust Decision Trees Against Adversarial Examples

Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and develop a novel algorithm to learn robust trees. At its core, our method aims to optimize the performance under the worst-case perturbation of input features, which leads to a max-min saddle point problem. Incorporating this saddle point objective into the decision tree building procedure is non-trivial due to the discrete nature of trees --- a naive approach to finding the best split according to this saddle point objective will take exponential time. To make our approach practical and scalable, we propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples.