Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of Tax Sheltering: Corporate Governance as Moderation

This research aimed to analyze the effect of a tax haven, transfer pricing, environmental uncertainty, and capital structure on tax sheltering with corporate governance as a moderating. Measurement of tax sheltering uses a tax shelter score with seven predictor models such as: book-tax differences (BTD), discretionary accruals performance-matched (DAP), leverage, asset size (Size), profitability (ROA), foreign income (FI), and research and development expense (R&D). The population of this study was the multinational companies not included in the financial service industry sector listed on the Indonesia Stock Exchange for the period 20162020. The research sample used was eighty-one companies in the multinational companies excluded in the financial service industry sector which were selected based on the purposive sampling method. This study used panel data regression analysis. The results obtained are that tax havens, environmental uncertainty, and capital structure has a positive effect on tax sheltering, while transfer pricing has a negative effect on tax sheltering. Corporate governance as a moderator can only have a positive effect on moderating the effect of tax havens, environmental uncertainty, and capital structure on tax sheltering. Keywords;Capital Structure, Environmental Uncertainty, Tax Sheltering, Tax Haven, Transfer Pricing.
AP-XHPJ210056 1329..1347

Current cognitive control accounts view goal-directed behavior as striking a balance between two antagonistic control demands: Stability, on the one hand, reflects a rigid, focused state of control and flexibility, while on the other, reflects a relaxed, distractible state, whereby goals can be rapidly updated to meet unexpected changes in demands. In the current study, we sought to test whether the avoidance of cognitive demand could motivate people to dynamically regulate control along the stability–flexibility continuum. In both cued (Experiment 1) and voluntary (Experiment 2) task-switching paradigms, we selectively associated either task-switches or task-repetitions with high cognitive demand (independent of task identity), and measured changes in performance in a following phase after the demand manipulation was removed. Contrasting performance with a control group, across both experiments, we found that selectively associating cognitive demand with task repetitions increased flexibility, but selectively associating cognitive demand with task switches failed to increase stability. The results of the current study provide novel evidence for avoidance-driven modulations of control regulation along the stability–flexibility continuum, while also highlighting some limitations in using task-switching paradigms to examine motivational influences on control adaptation. Data, analysis code, experiment code, and preprint available at osf.io/7rct9/.
Yield, growth and grain nitrogen response to elevated CO2 in six lentil (Lens culinaris) cultivars grown under Free Air CO2 Enrichment (FACE) in a semi-arid environment

Atmospheric CO2 concentrations ([CO2]) are predicted to increase from current levels of about 400 ppm to reach 550 ppm by 2050. The direct benefits of elevated [CO2] (e[CO2]) to plant growth appear to be greater under low rainfall conditions, but there are few field (Free Air CO2 Enrichment or FACE) experimental set-ups that directly address semi-arid conditions. The objectives of this study were to investigate the following research questions: 1) What are the effects of e[CO2] on the growth and grain yield of lentil (Lens culinaris) grown under semi-arid conditions under FACE? 2) Does e[CO2] decrease grain nitrogen in lentil? and 3) Is there genotypic variability in the response to e[CO2] in lentil cultivars? Elevated [CO2] increased yields by approximately 0.5 t ha (relative increase ranging from 18 to 138%) by increasing both biomass accumulation (by 32%) and the harvest index (by up to 60%). However, the relative response of grain yield to e[CO2] was not consistently greater under dry conditions and might depend on water availability post-flowering. Grain nitrogen concentration was significantly reduced by e[CO2] under the conditions of this experiment. No differences were found between the cultivars selected in the response to elevated [CO2] for grain yield or any other parameters observed despite well expressed genotypic variability in many traits of interest. Biomass accumulation from flowering to maturity was considerably increased by elevated [CO2] (a 50% increase) which suggests that the indeterminate growth habit of lentils provides vegetative sinks in addition to reproductive sinks during the grain-filling period.
Effect of Naphthalene Acetic Acid on Sprouting and Rooting of Stem Cuttings of Grapes

A field trial was laid to investigate the effect of naphthalene acetic acid (NAA) on sprouting and rooting of stem cuttings of grapes during 2015 at the experimental area of the Horticulture Department, Sindh Agriculture University, Tando Jam, Pakistan. The field trial was laid out in a three replicated completely randomized design (CRD) factorial. The stem cuttings of two varieties of grapes (Thompson seedless and Crimson seedless) about 6 inches in length with 3-4 buds were obtained from three-year-old plants. The basal ends of the cuttings were dipped in NAA solutions viz. 1000, 2000 and 3000 mg L -1 plus control treatment by the quick dip method for 10 seconds before planting them in the growth medium. The results revealed that all the traits related to sprouting and rooting potential of stem cuttings of grapes were significantly influenced by various NAA concentrations. However, the interaction between varieties and NAA concentrations was observed at par for all sprouting and rooting-related parameters. The maximum mean number of sprouts cutting -1 (6.33), leaves cutting -1 (20.50), roots cutting -1 (95.83) and fresh weight of roots (13.43 g) was achieved in response to NAA1 (1000 mg L -1 ). However, there was no significant effect of NAA on sprouting and rooting percentages. In varietal comparison, Crimson seedless produced significantly higher number of leaves (18.91) and number of roots cutting -1 (82.25). Research Article 2017 | Volume 5 | Issue 3 | Pages 225-232 Science Letters ISSN 2345-5463 – An International Triannually Journal
Filter Pruning by Switching to Neighboring CNNs With Good Attributes

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose meta-attribute-based filter pruning (MFP). First, to expand the existing magnitude information-based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.
Permutation invariant matrix statistics and computational language tasks

The Linguistic Matrix Theory programme introduced by Kartsaklis, Ramgoolam and Sadrzadeh is an approach to the statistics of matrices that are generated in type-driven distributional semantics, based on permutation invariant polynomial functions which are regarded as the key observables encoding the significant statistics. In this paper we generalize the previous results on the approximate Gaussianity of matrix distributions arising from compositional distributional semantics. We also introduce a geometry of observable vectors for words, defined by exploiting the graph-theoretic basis for the permutation invariants and the statistical characteristics of the ensemble of matrices associated with the words. We describe successful applications of this unified framework to a number of tasks in computational linguistics, associated with the distinctions between synonyms, antonyms, hypernyms and hyponyms.
An Improved EPA-Based Receiver Design for Uplink LDPC Coded SCMA System

Sparse code multiple access (SCMA) is an emerging paradigm for efficient enabling of massive connectivity in future machine-type communications (MTC). In this letter, we conceive the uplink transmissions of the low-density parity check (LDPC) coded SCMA system. Traditional receiver design of LDPC-SCMA system, which is based on message passing algorithm (MPA) for multiuser detection followed by individual LDPC decoding, may suffer from the drawback of the high complexity and large decoding latency, especially when the system has large codebook size and/or high overloading factor. To address this problem, we introduce a novel receiver design by applying the expectation propagation algorithm (EPA) to the joint detection and decoding (JDD) involving an aggregated factor graph of LDPC code and sparse codebooks. Our numerical results demonstrate the superiority of the proposed EPA based JDD receiver over the conventional Turbo receiver in terms of both significantly lower complexity and faster convergence rate without noticeable error rate performance degradation.
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification

Traffic classification, i.e., the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing). This task faces some critical challenges that current deep learning approaches do not address. The design of current approaches do not take into consideration the fact that networking hardware (e.g., routers) often runs with limited computational resources. Further, they do not meet the need for faithful explainability highlighted by regulatory bodies. Finally, these traffic classifiers are evaluated on small datasets which fail to reflect the diversity of applications in real-world settings. Therefore, this paper introduces a new Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification, which relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability). Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network, while providing the additional features previously mentioned. Moreover, we illustrate the explainability feature of our approach, which stems from the communication of detected application prototypes to the end-user, and we highlight the faithfulness of LEXNet explanations through a comparison with post hoc methods.
Approximating transmission and reflection spectra near isolated nondegenerate resonances

A linear scattering problem for which incoming and outgoing waves are restricted to a ﬁnite number of radiation channels can be precisely described by a frequency-dependent scattering matrix. The entries of the scattering matrix, as functions of the frequency, give rise to the transmission and reﬂection spectra. To ﬁnd the scattering matrix rigorously, it is necessary to solve numerically the partial diﬀerential equations governing the relevant waves. In this paper, we consider resonant structures with an isolated nondegenerate resonant mode of complex frequency ω ⋆ , and show that for real frequencies near ω 0 = Re( ω ⋆ ), the transmission and reﬂection spectra can be approximated using only the scattering matrix at ω 0 and information about the resonant mode. We also present a revised temporal coupled-mode theory that produces the same approximate formulas for the transmission and reﬂection spectra. Numerical examples for diﬀraction of plane waves by periodic structures are presented to validate our theory.
Turbulent flame speed based on mass flow rate of reactants

Starting with an integral formulation of mass flow rate of reactants through an ensemble of isotherms within a statistically planar, turbulent premixed flame, a scaling for the corresponding turbulent flame speed is derived. Closure relations for the local flame displacement speed and scalar dissipation rate anomaly are invoked and a new length scale quantifying the fluctuation distance of the isotherms within the premixed flame structure is introduced. Damköhler’s hypotheses are not used at any stage. Eventually, the scaling is extended to expanding turbulent premixed flames.
Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks

We hypothesize that due to the greedy nature of learning in multi-modal deep neural networks, these models tend to rely on just one modality while under-fitting the other modalities. Such behavior is counter-intuitive and hurts the models' generalization, as we observe empirically. To estimate the model's dependence on each modality, we compute the gain on the accuracy when the model has access to it in addition to another modality. We refer to this gain as the conditional utilization rate. In the experiments, we consistently observe an imbalance in conditional utilization rates between modalities, across multiple tasks and architectures. Since conditional utilization rate cannot be computed efficiently during training, we introduce a proxy for it based on the pace at which the model learns from each modality, which we refer to as the conditional learning speed. We propose an algorithm to balance the conditional learning speeds between modalities during training and demonstrate that it indeed addresses the issue of greedy learning. The proposed algorithm improves the model's generalization on three datasets: Colored MNIST, ModelNet40, and NVIDIA Dynamic Hand Gesture.
Rotational $K^{\alpha}$-translators in Minkowski Space

A spacelike surface in Minkowski space $\mathbb{R}_1^3$ is called a $K^\alpha$-translator of the flow by the powers of Gauss curvature if satisfies $K^\alpha= \langle N,\vec{v}\rangle$, $\alpha \neq 0$, where $K$ is the Gauss curvature, $N$ is the unit normal vector field and $\vec{v}$ is a direction of $\mathbb{R}_1^3$. In this paper, we classify all rotational $K^\alpha$-translators. This classification will depend on the causal character of the rotation axis. Although the theory of the $K^\alpha$-flow holds for spacelike surfaces, the equation describing $K^\alpha$-translators is still valid for timelike surfaces. So we also investigate the timelike rotational surfaces that satisfy the same prescribing Gauss curvature equation.
Colored vertex models and $k$-tilings of the Aztec diamond

We study k-tilings (k-tuples of domino tilings) of the Aztec diamond of rank m. We assign a weight to each k-tiling, depending on the number of dominos of certain types and the number of “interactions” between the tilings. Employing the colored vertex models introduced in [12] to study supersymmetric LLT polynomials, we compute the generating polynomials of the ktilings. We then prove some combinatorial results about k-tilings, including a bijection between k-tilings with no interactions and 1-tilings, and we compute the arctic curves of the tilings for t = 0 and t→∞. We also present some lozenge k-tilings of the hexagon and compute the arctic curves of the tilings for t = 0.
Determination of the solution of a stochastic parabolic equation by the terminal value

This paper studies the inverse problem of determination the history for a stochastic diffusion process, by means of the value at the final time T. By establishing a new Carleman estimate, the conditional stability of the problem is proven. Based on the idea of Tikhonov method, a regularized solution is proposed. The analysis of the existence and uniqueness of the regularized solution, and proof for error estimate under an a priori assumption are present. Numerical verification of the regularization, including numerical algorithm and examples are also illustrated.
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection

Recent anomaly detection algorithms have shown powerful performance by adopting frame predicting autoencoders. However, these methods face two challenging circumstances. First, they are likely to be trained to be excessively powerful, generating even abnormal frames well, which leads to failure in detecting anomalies. Second, they are distracted by the large number of objects captured in both foreground and background. To solve these problems, we propose a novel superpixel-based video data transformation technique named Random Superpixel Erasing on Moving Objects (RandomSEMO) and Moving Object Loss (MOLoss), built on top of a simple lightweight autoencoder. RandomSEMO is applied to the moving object regions by randomly erasing their superpixels. It enforces the network to pay attention to the foreground objects and learn the normal features more effectively, rather than simply predicting the future frame. Moreover, MOLoss urges the model to focus on learning normal objects captured within RandomSEMO by amplifying the loss on the pixels near the moving objects. The experimental results show that our model outperforms state-of-the-arts on three benchmarks.
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality

Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal theoretical formulation for the problem. In this paper, we propose such a formulation for deployment-efficient RL (DE-RL) from an"optimization with constraints"perspective: we are interested in exploring an MDP and obtaining a near-optimal policy within minimal \emph{deployment complexity}, whereas in each deployment the policy can sample a large batch of data. Using finite-horizon linear MDPs as a concrete structural model, we reveal the fundamental limit in achieving deployment efficiency by establishing information-theoretic lower bounds, and provide algorithms that achieve the optimal deployment efficiency. Moreover, our formulation for DE-RL is flexible and can serve as a building block for other practically relevant settings; we give"Safe DE-RL"and"Sample-Efficient DE-RL"as two examples, which may be worth future investigation.
An Epidemic Compartment Model for Economic Policy Directions for Managing Future Pandemic (preprint)

In this research, we develop a framework to analyze the interaction between the economy and the Covid-19 pandemic using an extension of SIR epidemic model. At the outset, we assume there are two health related investments including general medical expenditures and the other for a direct investment for controlling the pandemic. We incorporate the learning dynamics associated with the management of the virus into our model. Given that the labor force in a society depends on the state of the epidemic, we allow birth, death, and vaccination to occur in our model and assume labor force consists of the susceptible, vaccinated, and recovered individuals. We also assume parameters in our epidemic compartmental model depend on investment amount for directly controlling the epidemic, the health stock of individual representative agents in the society, and the knowledge or learning about the epidemic in the community. By controlling consumption, the general medical expenditure, and the direct investment of funds for controlling the epidemic, we optimize the utility realized by the representative individuals because of consumption. This problem is nontrivial since the disease dynamics results in a non-convex optimization problem.
On limiting spectral distribution and joint convergence of some patterned random matrices

Abstract. This article deals with the limiting spectral distribution and joint convergence of reverse circulant and symmetric circulant matrices with independent entries. These results are already proved in articles Bose and Sen (2008) [6], and Bose, Hazra and Saha (2011) [4], respectively. But this article provides alternate proofs of these results which are shorter than the existing proofs. Our method is mainly based on moment method.
Beyond NaN: Resiliency of Optimization Layers in The Face of Infeasibility

Prior work has successfully incorporated optimization layers as the last layer in neural networks for various problems, thereby allowing joint learning and planning in one neural network forward pass. In this work, we identify a weakness in such a set-up where inputs to the optimization layer lead to undefined output of the neural network. Such undefined decision outputs can lead to possible catastrophic outcomes in critical real time applications. We show that an adversary can cause such failures by forcing rank deficiency on the matrix fed to the optimization layer which results in the optimization failing to produce a solution. We provide a defense for the failure cases by controlling the condition number of the input matrix. We study the problem in the settings of synthetic data, Jigsaw Sudoku, and in speed planning for autonomous driving. We show that our proposed defense effectively prevents the framework from failing with undefined output. Finally, we surface a number of edge cases which lead to serious bugs in popular optimization solvers which can be abused as well.
On Payment Channels in Asynchronous Money Transfer Systems

Money transfer is an abstraction that realizes the core of cryptocurrencies. It has been shown that, contrary to common belief, money transfer in the presence of Byzantine faults can be implemented in asynchronous networks and does not require consensus. Nonetheless, existing implementations of money transfer still require a quadratic message complexity per payment, making attempts to scale hard. In common blockchains, such as Bitcoin and Ethereum, this cost is mitigated by payment channels implemented as a second layer on top of the blockchain allowing to make many off-chain payments between two users who share a channel. Such channels only require on-chain transactions for channel opening and closing, while the intermediate payments are done off-chain with constant message complexity. But payment channels in-use today require synchrony, therefore they are inadequate for asynchronous money transfer systems. In this paper, we provide a series of possibility and impossibility results for payment channels in asynchronous money transfer systems. We first prove a quadratic lower bound on the message complexity of on-chain transfers. Then, we explore two types of payment channels, unidirectional and bidirectional. We define them as shared memory abstractions and prove that in certain cases they can be implemented as a second layer on top of an asynchronous money transfer system whereas in other cases it is impossible.
Picking the low-hanging fruit: testing new physics at scale with active learning

Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. Using machine learning, the technique obtained comparable results to previous methods (>90% precision and recall) with only a fraction of their computing resources (<10%). This makes it possible to test models that were impossible to probe before, and allows for large-scale testing of new physics theories.