Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Wins in International Investment Arbitration Disputes? Evidence from Latin American and Caribbean Countries Cases Under ICSID

During the nineties, an important group of Latin American and the Caribbean (LAC) countries adhered to the International Center for Settlement of Investment Disputes (ICSID) as part of a program of measures that sought to attract foreign investment to the region. Using a dataset of 161 concluded disputes until 2019 corresponding to investments in LAC countries, we find evidence on the influence of parties’ characteristics, the subject discussed in cases, and characteristics of the tribunal on the arbitration outcomes. We find that 50% of tribunal decisions have been favorable to either claimant investors or host countries. However, this result may be subject to sample bias if information of settlements and discontinued cases is not taken under consideration. We also find evidence in favor of selection and party capability theories that helps to explain the determinants of international investment arbitration dispute outcomes. In particular, the results reveal that disputes related to direct expropriations have a relatively higher probability of being considered founded by tribunals than other legal controversies. Likewise, the indicators of the relative strength of parties, such as experience in international arbitration system, have an important influence on tribunal awards. Furthermore, country’s time of experience within ICSID is found to have an important influence on the selection of disputes.
Market solutions and the equity principle in tax administration

ABSTRACT Market solutions have increasingly been utilized at all levels of government to reduce costs, increase efficiency, and improve quality. However, scholars have raised concerns that business-like approaches may eclipse or even discredit the other core values of public administration, such as equity, in the pursuit of the values of the market. As equity is often the central question of taxation, this study explores how market-oriented administrative reforms affect equity focusing on the effect of outsourcing in property tax administration. The findings indicate that outsourcing can preserve equity in taxation and even improve the outcome in certain service areas.
Storms and Startups: Local Natural Hazards as External Enablers of New Venture Growth

This paper examines whether local natural hazards can function as external enablers of new venture growth. To do so, I use U.S. county-level employment data for firms in narrowly defined age categories combined with a novel measure for local hurricane exposure between 2000 and 2015. I find that startup employment in affected regions rises significantly after a hurricane, unlike employment in established firms. This is mainly because startups account for a disproportionate share of excess job creation in sectors associated with recovery and rebuilding. These startups also remain larger than their counterparts in unaffected regions years later. In nearby unaffected regions, only old firms hire more workers. This suggests that the spatial scope of natural hazards for entrepreneurial activity is limited.
Organizational Innovation in Medium and Large Sized Businesses in Thailand’s Food Manufacturing Industry

This research investigates the effect of transformational leadership and organizational learning on organizational innovation occurring in medium and large-sized businesses in Thailand’s food manufacturing industry. In total 200 businesses constituted the sample. PROCESS Macro Model 4 technique as devised by Hayes was utilized to verify the hypotheses. It was found that organizational learning plays a crucial role in fully mediating the effects of transformational leadership on organizational innovation. On this basis, a transformational leadership style of management is important for the firms in the food manufacturing industry to be innovative. These firms’ owners/managers must concentrate on organizational learning for innovation to lead to viable outcomes in the food industry.
An essentially decentralized interior point method for control

Distributed and decentralized optimization are key for the control of networked systems. Application examples include distributed model predictive control and distributed sensing or estimation. Non-linear systems, however, lead to problems with non-convex constraints for which classical decentralized optimization algorithms lack convergence guarantees. Moreover, classical decentralized algorithms usually exhibit only linear convergence. This paper presents an essentially de-centralized primal-dual interior point method with convergence guarantees for non-convex problems at a superlinear rate. We show that the proposed method works reliably on a numerical example from power systems. Our results indicate that the proposed method outperforms ADMM in terms of computation time and computational complexity of the subproblems.
Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images

Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is quite limited. To address the issue, we propose a novel multitask learning network with a bending loss regularizer to separate overlapped nuclei accurately. The newly proposed multitask learning architecture enhances the generalization by learning shared representation from three tasks: instance segmentation, nuclei distance map prediction, and overlapped nuclei distance map prediction. The proposed bending loss defines high penalties to concave contour points with large curvatures, and applies small penalties to convex contour points with small curvatures. Minimizing the bending loss avoids generating contours that encompass multiple nuclei. In addition, two new quantitative metrics, Aggregated Jaccard Index of overlapped nuclei (AJIO) and Accuracy of overlapped nuclei (ACCO), are designed for the evaluation of overlapped nuclei segmentation. We validate the proposed approach on the CoNSeP and MoNuSegv1 datasets using seven quantitative metrics: Aggregate Jaccard Index, Dice, Segmentation Quality, Recognition Quality, Panoptic Quality, AJIO, and ACCO. Extensive experiments demonstrate that the proposed Bend-Net outperforms eight state-of-the-art approaches.
Dr Jekyll and Mr Hyde: the Strange Case of Off-Policy Policy Updates

The policy gradient theorem states that the policy should only be updated in states that are visited by the current policy, which leads to insufficient planning in the off-policy states, and thus to convergence to suboptimal policies. We tackle this planning issue by extending the policy gradient theory to policy updates with respect to any state density. Under these generalized policy updates, we show convergence to optimality under a necessary and sufficient condition on the updates' state densities, and thereby solve the aforementioned planning issue. We also prove asymptotic convergence rates that significantly improve those in the policy gradient literature. To implement the principles prescribed by our theory, we propose an agent, Dr Jekyll&Mr Hyde (JH), with a double personality: Dr Jekyll purely exploits while Mr Hyde purely explores. JH's independent policies allow to record two separate replay buffers: one on-policy (Dr Jekyll's) and one off-policy (Mr Hyde's), and therefore to update JH's models with a mixture of on-policy and off-policy updates. More than an algorithm, JH defines principles for actor-critic algorithms to satisfy the requirements we identify in our analysis. We extensively test on finite MDPs where JH demonstrates a superior ability to recover from converging to a suboptimal policy without impairing its speed of convergence. We also implement a deep version of the algorithm and test it on a simple problem where it shows promising results.
Estimation of Ebola’s spillover infection exposure in Sierra Leone based on sociodemographic and economic factors

Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors. Here we inquire into this phenomenon and aim at determining, quantitatively, the Ebola spillover infection exposure map and try to link it to SDE factors. To that end, we designed and conducted a survey in Sierra Leone and implement a pipeline to analyze data using regression and machine learning techniques. Our methodology is able (1) to identify the features that are best predictors of an individual’s tendency to partake in behaviors that can expose them to Ebola infection, (2) to develop a predictive model about the spillover risk statistics that can be calibrated for different regions and future times, and (3) to compute a spillover exposure map for Sierra Leone. Our results and conclusions are relevant to identify the regions in Sierra Leone at risk of EVD spillover and, consequently, to design and implement policies for an effective deployment of resources (e.g., drug supplies) and other preventative measures (e.g., educational campaigns).
On the principal eigenvalue of the truncated Laplacian, and submanifolds with bounded mean curvature

In this paper, we study the principal eigenvalue $\mu(\mathscr{F}_k^-,E)$ of the fully nonlinear operator \[ \mathscr{F}_k^-[u] = \mathcal{P}_k^-(\nabla^2 u) - h |\nabla u| \] on a set $E \Subset \mathbb{R}^n$, where $h \in [0,\infty)$ and $\mathcal{P}_k^-(\nabla^2 u)$ is the sum of the smallest $k$ eigenvalues of the Hessian $\nabla^2 u$. We prove a lower estimate for $\mu(\mathscr{F}_k^-,E)$ in terms of a generalized Hausdorff measure $\mathscr{H}_\Psi(E)$, for suitable $\Psi$ depending on $k$, moving some steps in the direction of the conjecturally sharp estimate \[ \mu(\mathscr{F}_k^-,E) \ge C \mathscr{H}^k(E)^{-2/k}. \] The theorem is used to study the spectrum of bounded submanifolds in $\mathbb{R}^n$, improving on our previous work in the direction of a question posed by S.T. Yau. In particular, the result applies to solutions of Plateau's problem for CMC surfaces.
Reflection and transmission of a Kelvin–Helmholtz wave incident on a shock in a jet

Screech tones in supersonic jets are underpinned by resonance between downstream-travelling Kelvin–Helmholtz waves and upstream-travelling acoustic waves. Specifically, recent works suggest that the relevant acoustic waves are guided within the jet and are described by a discrete mode of the linearised Euler equations. However, the reflection mechanism that converts downstream-travelling waves into upstream-travelling waves, and vice versa, has not been thoroughly addressed, leading to missing physics within most resonance models. In this work, we investigate the reflection and transmission of waves generated by the interaction between a Kelvin–Helmholtz wave and a normal shock in an under-expanded jet using a mode-matching approach. Both vortex-sheet and finite-thickness shear-layer models are explored, quantifying the impact of the shear layer in the reflection process. This approach could enable more quantitative predictions of resonance phenomena in jets and other fluid systems.
The Eccentric and Accelerating Stellar Binary Black Hole Mergers in Galactic Nuclei: Observing in Ground and Space Gravitational-wave Observatories

We study the stellar binary black holes (BBHs) inspiraling/merging in galactic nuclei based on our numerical method GNC. We find that 3%–40% of all newborn BBHs will finally merge due to various dynamical effects. In a five-year mission, up to 104, 105, and ∼100 of BBHs inspiraling/merging in galactic nuclei can be detected with signal-to-noise ration >8 in Advanced LIGO (aLIGO), Einstein/DECIGO, and TianQin/LISA/TaiJi, respectively. Roughly tens are detectable in both LISA/TaiJi/TianQin and aLIGO. These BBHs have two unique characteristics. (1) Significant eccentricities: 1%–3%, 2%–7%, or 30%–90% of them have e i > 0.1 when they enter into aLIGO, Einstein, or space observatories, respectively. Such high eccentricities provide a possible explanation for that of GW190521. Most highly eccentric BBHs are not detectable in LISA/Tianqin/TaiJi before entering into aLIGO/Einstein, as their strain becomes significant only at f GW ≳ 0.1 Hz. DECIGO becomes an ideal observatory to detect those events, as it can fully cover the rising phase. (2) Up to 2% of BBHs can inspiral/merge at distances ≲103 r SW from the massive black hole, with significant accelerations, such that the Doppler phase drift of ∼10–105 of them can be detected with signal-to-noise ratio >8 in space observatories. The energy density of the gravitational-wave backgrounds (GWBs) contributed by these BBHs deviates from the power-law slope of 2/3 at f GW ≲ 1 mHz. The high eccentricity, significant accelerations, and the different profile of the GWB of these sources make them distinguishable, and thus interesting for future gravitational-wave detections and tests of relativities.
Jet propagation through inhomogeneous media and shock ionization

In this contribution we present the first numerical simulations of a relativistic outflow propagating through the inner hundreds of parsecs of its host galaxy, including atomic and ionised hydrogen, and the cooling effects of ionisation. Our results are preliminary, but we observe efficient shock ionization of atomic hydrogen in interstellar clouds. Themean density of the interstellar medium in these initial simulations is lower than that expected in typical galaxies, which makes cooling times longer and thus no recombination is observed inside the shocked region. The velocities achieved by the shocked gas in the simulations are in agreement with observational results, although with a wide spectrum of values.
Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models

An outstanding challenge with safety methods for human-robot interaction is reducing their conservatism while maintaining robustness to variations in human behavior. In this work, we propose that robots use confidence-aware game-theoretic models of human behavior when assessing the safety of a human-robot interaction. By treating the influence between the human and robot as well as the human's rationality as unobserved latent states, we succinctly infer the degree to which a human is following the game-theoretic interaction model. We leverage this model to restrict the set of feasible human controls during safety verification, enabling the robot to confidently modulate the conservatism of its safety monitor online. Evaluations in simulated human-robot scenarios and ablation studies demonstrate that imbuing safety monitors with confidence-aware game-theoretic models enables both safe and efficient human-robot interaction. Moreover, evaluations with real traffic data show that our safety monitor is less conservative than traditional safety methods in real human driving scenarios.
Symbolic substitution systems beyond abelian groups

Symbolic substitution systems are an important source of aperiodic Delone sets in abelian locally compact groups. In this article, we consider a large class of non-abelian nilpotent Lie groups with dilation structures, which we refer to as rationally homogeneous Lie groups with rational spectrum (RAHOGRASPs). We show, by explicit construction, that every RAHOGRASP admits a lattice with a primitive symbolic substitution system and hence contains a weakly aperiodic linearly repetitive Delone set. These are the first examples of weakly aperiodic linearly repetitive Delone sets in non-abelian Lie groups. Building on our previous work, we establish unique ergodicity of the corresponding Delone dynamical systems. Our construction applies in particular to all two-step nilpotent Lie groups defined over Q such as the Heisenberg group. In this case, the Delone sets in question are in fact strongly aperiodic, i.e. the underlying action of the corresponding Delone dynamical system is free.
Impact of Channel Variation on One-Class Learning for Spoof Detection

The value of Spooﬁng detection in increasing the reliability of the ASV system is unparalleled. In reality, however, the performance of countermeasure systems (CMs) degrades signiﬁcantly due to channel variation. Multi-conditional training(MCT) is a well-established technique to handle such scenarios. However, which data-feeding strategy is optimal for MCT? ” is not known in the case of spoof detection. In this paper, various codec simulations were used to modify ASVspoof 2019 dataset, and assessments were done using data-feeding and mini-batching strategies to help address this question. Our experiments aim to test the efﬁcacy of the various margin-based losses for training Resnet based models with LFCC front-end feature extractor to correctly classify the spoofed and bonaﬁde samples degraded using codec simulations. Contrastingly to most of the works that focus mainly on architectures, this study highlights the relevance of the deemed-of-low-importance process of data-feeding and mini-batching to raise awareness of the need to reﬁne it for better performance.
Twofold saddle-point formulation of Biot poroelasticity with stress-dependent diffusion

We introduce a stress/total-pressure formulation for poroelasticity that includes the coupling with steady nonlinear diffusion modified by stress. The nonlinear problem is written in mixed-primal form, coupling a perturbed twofold saddle-point system with an elliptic problem. The continuous formulation is analysed in the framework of abstract fixed-point theory and Fredholm alternative for compact operators. A mixed finite element method is proposed and its stability and convergence analysis are carried out. We also include a few illustrative numerical tests. The resulting model can be used to study waste removal in the brain parenchyma, where diffusion of a tracer alone or a combination of advection and diffusion are not sufficient to explain the alterations in rates of filtration observed in porous media samples.
A Chemical Master Equation Model for Synaptic Molecular Communication

In synaptic molecular communication, the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process and, hence, inherently random. It is currently not fully understood how this randomness impacts downstream signaling in the target cell and, ultimately, neural computation and learning. The statistical characterization of the reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs and receptors renders the system nonlinear. Consequently, existing models for the receptor occupancy in the synaptic cleft rely on simplifying assumptions and approximations which limit their practical applicability. In this work, we propose a novel statistical model for the reaction-diffusion process governing synaptic signal transmission in terms of the chemical master equation (CME). We show how to compute the CME efficiently and verify the accuracy of the obtained results with stochastic particle-based computer simulations (PBSs). Furthermore, we compare the proposed model to two benchmark models proposed in the literature and show that it provides more accurate results when compared to PBSs. Finally, the proposed model is used to study the impact of the system parameters on the statistical dependence between binding events of NTs and receptors. In summary, the proposed model provides a step forward towards a complete statistical characterization of synaptic signal transmission.
GW200115: A Nonspinning Black Hole–Neutron Star Merger

GW200115 was the second merger of a black hole and a neutron star confidently detected through gravitational waves. Inference on the signal allows for a large black hole spin misaligned with the orbital angular momentum, but shows little support for aligned spin values. We show that this is a natural consequence of measuring the parameters of a black hole–neutron star binary with nonspinning components while assuming the priors used in the LIGO–Virgo–KAGRA analysis. We suggest that, a priori, a nonspinning binary is more consistent with current astrophysical understanding.
Robustness of subwavelength devices: a case study of cochlea-inspired rainbow sensors

We derive asymptotic formulae describing how the properties of subwavelength devices are changed by the introduction of errors and imperfections. As a demonstrative example, we study a class of cochlea-inspired rainbow sensors. These are graded metamaterials which have been designed to mimic the frequency separation performed by the cochlea. The device considered here has similar dimensions to the cochlea and has a resonant spectrum that falls within the range of audible frequencies. We show that the device’s properties (including its role as a signal filtering device) are stable with respect to small imperfections in the positions and sizes of the resonators. Additionally, under suitable assumptions, if the number of resonators is sufficiently large, then the device’s properties are stable under the removal of a resonator.
SlovakBERT: Slovak Masked Language Model

We introduce a new Slovak masked language model called SlovakBERT. This is to our best knowledge the first paper discussing Slovak transformers-based language models. We evaluate our model on several NLP tasks and achieve state-of-the-art results. This evaluation is likewise the first attempt to establish a benchmark for Slovak language models. We publish the masked language model, as well as the fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems

The Transformer and its variants have been proven to be efficient sequence learners in many different domains. Despite their staggering success, a critical issue has been the enormous number of parameters that must be trained (ranging from $10^7$ to $10^{11}$) along with the quadratic complexity of dot-product attention. In this work, we investigate the problem of approximating the two central components of the Transformer -- multi-head self-attention and point-wise feed-forward transformation, with reduced parameter space and computational complexity. We build upon recent developments in analyzing deep neural networks as numerical solvers of ordinary differential equations. Taking advantage of an analogy between Transformer stages and the evolution of a dynamical system of multiple interacting particles, we formulate a temporal evolution scheme, TransEvolve, to bypass costly dot-product attention over multiple stacked layers. We perform exhaustive experiments with TransEvolve on well-known encoder-decoder as well as encoder-only tasks. We observe that the degree of approximation (or inversely, the degree of parameter reduction) has different effects on the performance, depending on the task. While in the encoder-decoder regime, TransEvolve delivers performances comparable to the original Transformer, in encoder-only tasks it consistently outperforms Transformer along with several subsequent variants.
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.