Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Institutional Investors on Real Estate Risk

Private real estate markets have experienced signi ficant in inflows of institutional capital over the last couple of decades. In this paper we seek to understand what are the implications of this recent development. Employing a generalized Hamiltonian Monte Carlo Bayesian procedure we find novel empirical evidence that market entry by large institutional investors predicts higher uncertainty and greater noise in real estate prices in the short and medium run, and lower longitudinal risk in the long run. Our findings point to a signi ficant eff ect of institutional capital, which serves as a catalyst for structural changes in real estate market risk.
The Pricing of Acquired Intangibles

This paper investigates the value relevance of acquired intangible assets using a comprehensive hand-collected dataset for 1,647 publicly listed US-firms from 2002 to 2018. This dataset allows us to disentangle acquired intangible assets into different classes (e.g., tech-, customer-, contract-, and marketing-intangible assets) and their respective economic lifetimes (i.e., definite vs indefinite useful lives) to test their relevance for equity investors. We predict and find positive associations for nearly all intangible assets, however with different economic significance. In particular, tech- and customer-related intangible assets are priced by equity investors. Furthermore, we find that definite intangible assets are more relevant than indefinite intangibles. These results are helpful for firms and their equity investors to understand the economic impact of intangible assets. Finally, the findings are particularly important for regulators given the recent proposition of the Financial Accounting Standards Board to subsume customer-related intangible assets and non-compete agreements into goodwill. While our results suggest that customer-related intangible assets are priced significantly by equity investors, this is not the case for non-compete agreements.
Greedy Switching Functions on Time Scales

Submodular functions are an important class of real-valued functions whose domains are the power set of some finite collection of items. An existing result states that, given an monotonic submodular objective function, a greedy algorithm achieves an approximation ratio of at worst 1-1/e. More recently, a similar result was shown for functions whose domain is a class of objects called continuous sequences. We generalize both of existing works by defining a class of objects called switching functions. Suppose that there is a system with a finite number of possible configurations and a set of times at which the configuration may be altered; this set of times may be discrete or continuous. A switching function represents a policy that chooses a configuration at each point in time, and that switches the configuration a finite number of times. We show that, given some regularity conditions reminiscent of submodularity, a greedy algorithm for constructing switching function provides an approximation ratio. This generalizes existing results concerning submodular functions and sequence submodular functions. This provides a unified approach to greedy algorithms with qualities analogous to submodularity in discrete and continuous settings.
Trading Signatures: Investor Trade Allocation in Stock Markets

Investors in stock markets face a huge amount of financial information. For that reason, they must decide how to distribute their trading effort across different securities. We propose a new measure of investor trade allocation between securities, called the trading signature . This measure, which also serves as a proxy for investor’s attention to trade, allows us to statistically analyze whether there are heterogeneous, persistent, and characteristic patterns in the way how investors distribute their trading effort to different securities. Building on a large investor shareholder registration data set from Helsinki Stock Exchange, we find that the ways how investors distribute their trading attention are not only distinctive but invariant with respect to changes in their portfolios. More importantly, investor behavior is surprisingly persistent statistically: it takes at least seven years for an investor to significantly change her trading signatures. These observations are strikingly similar to recent findings on how people manage their social networks. However, in contrast to social relationships, time constraints do not appear to limit investors’ activeness in the markets: the larger is an investor’s portfolio, the more frequently each security is traded on average. A possible explanation is that different trading strategies require different amounts of effort, and an investor can switch between the strategies if she changes the size of her portfolio.
Clientelism and development: Vote-buying meets patronage

Offering employment in the public sector in exchange for electoral support (patronage politics) and vote-buying are clientelistic practices frequently used by political machines. In the literature, these practices are typically studied in isolation. In this paper, we study how the interaction between these two practices (as opposed to having just one tool) affects economic development. We present a theoretical model of political competition, where, before the election, the incumbent chooses the level of state investment that can improve productivity in the private sector. This decision affects the income levels of employees in the private sector, and, thereby, the costs and effectiveness of vote-buying and patronage. We show that when the politician can use both clientelistic instruments simultaneously, his opportunity cost for clientelism in terms of foregone future taxes declines. As a result, the equilibrium amount of public investment is typically lower when both tools are available than otherwise.
The Impact of Bank Liquidity Risk on Risk-Taking and Bank Lending: Evidence from European Bank

Objective – This paper uses a sample of annual observations of European banks to examine whether the liquidity risk affects a bank’s risk-taking behavior and its future loan growth. Methodology – A sample of European banks (27 member countries of the European Union plus U.K.) over the period of 2005 to 2019 are used in this study. Liquidity risk is measured by the ratio of liquid assets to total assets. Given the longitudinal nature of the data, the authors use panel regression with bank fixed effects to control for unobserved characteristics that might affect the dependent variable. Findings – The authors find that banks holding more liquid assets take less risk and show a higher subsequent loan growth rate. These results hold for both small and large banks. Novelty – To the authors’ best knowledge, this is one of the earliest studies to carefully examine the effects of liquidity risk on risk-taking behavior and loan growth rate for European banks. Our research suggests that the current Basel III requirement on liquidity ratio can decrease bank’s risking-taking behavior while not necessarily impact their future loan growth. Type of Paper: Empirical JEL Classification: G21, G01, G18. Keywords: Bank Liquidity Risk; Risk-taking Behavior; Loan Growth; Basel III
When many decisions tire you out: Choice fatigue in proxy voting

We examine ballot order effects in the proxy voting process. Our results show that investors and proxy advisors, confronted with repeated decision making across multiple proxy votes, are subject to choice fatigue that affects their voting patterns when electing (independent) corporate directors. Down-the-ballot directors receive considerably less shareholder opposition and are less likely to receive negative recommendations from the proxy advisor, Institutional Shareholder Services. The results show that the ballot order effect is strongest when the voter is less attentive and the ballot items are more complex. Our analysis focuses on a sample where directors are positioned alphabetically on the proxy ballot. This setting allows for a causal interpretation of our results as alphabetical positions are independent of the directors’ ability or position on the board. The results are also robust to firm-level and director-level controls, as well as fixed-effects that control for unobservable time-varying factors related to the firm and voter. Our findings contribute to recent discussion in the media and among regulators about the use of the proxy voting process as an effective mechanism of shareholder voice. If proxy votes and recommendations suffer systematic bias, then recent regulation aimed at transparency might not help to improve the mechanism.
Bank Influence at a Discount

We study how bankers can elicit lower capital requirements via lobbying: Bankers pledge to politicians a lobbying rate as a fraction of bank revenues, thus relating politicians' welfare to the size of banks. This induces politicians to lower capital requirements, which causes high leverage and excessive investments in risky technologies. We establish a non-monotonic relationship between the lobbying rate and the likelihood that a crisis occurs. We also predict that the lobbying rate increases monotonically as political participation rises and/or government guarantees expand. Finally, we suggest that a lobbying tax or shareholder-welfare maximization can alleviate the inefficiencies created by lobbying.
Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks

Understanding the fundamental principles behind the success of deep neural networks is one of the most important open questions in the current literature. To this end, we study the training problem of deep neural networks and introduce an analytic approach to unveil hidden convexity in the optimization landscape. We consider a deep parallel ReLU network architecture, which also includes standard deep networks and ResNets as its special cases. We then show that pathwise regularized training problems can be represented as an exact convex optimization problem. We further prove that the equivalent convex problem is regularized via a group sparsity inducing norm. Thus, a path regularized parallel ReLU network can be viewed as a parsimonious convex model in high dimensions. More importantly, since the original training problem may not be trainable in polynomial-time, we propose an approximate algorithm with a fully polynomial-time complexity in all data dimensions. Then, we prove strong global optimality guarantees for this algorithm. We also provide experiments corroborating our theory.
Fractional Chern insulators with a non-Landau level continuum limit

Recent developments in fractional quantum Hall (FQH) physics highlight the importance of studying FQH phases of particles partially occupying energy bands that are not Landau levels. FQH phases in the regime of strong lattice effects, called fractional Chern insulators, provide one setting for such studies. As the strength of lattice effects vanishes, the bands of generic lattice models asymptotically approach Landau levels. In this article, we construct lattice models for single-particle bands that are distinct from Landau levels even in this continuum limit. We describe how the distinction between such bands and Landau levels is quantified by band geometry over the magnetic Brillouin zone and reflected in the electromagnetic response. We analyze the localization-delocalization transition in one such model and compute a localization length exponent of 2.57(3). Moreover, we study interactions projected to these bands and find signatures of bosonic and fermionic Laughlin states. Most pertinently, our models allow us to isolate conditions for optimal band geometry and gain further insight into the stability of FQH phases on lattices.
Photodynamical Modeling of the Fascinating Eclipses in the Triple-star System KOI-126

We explore the fascinating eclipses and dynamics of the compact hierarchical triple-star system KOI-126 (KIC 5897826). This system is composed of a pair of M-dwarf stars (KOI-126 B and C) in a 1.74 day orbit that revolve around an F star (KOI-126 A) every 34 days. Complex eclipse shapes are created as the M stars transit the F star, due to two effects: (1) the duration of the eclipse is a significant fraction of the M-star orbital period, so the prograde or retrograde motion of the M stars in their orbit lead to unusually short or long duration eclipses; (2) due to 3-body dynamics, the M-star orbit precesses with an astonishingly quick timescale of 1.74 yr for the periastron (apsidal) precession, and 2.73 yr for the inclination and nodal angle precession. Using the full Kepler data set, supplemented with ground-based photometry, plus 29 radial velocity measurements that span 6 yr, our photodynamical modeling yields masses of M A = 1.2713 ± 0.0047 M ⊙ (0.37%), M B = 0.23529 ± 0.00062 M ⊙ (0.26%), and M C = 0.20739 ± 0.00055 M ⊙ (0.27%) and radii of R A = 1.9984 ± 0.0027 R ⊙ (0.14%), R B = 0.25504 ± 0.00076 R ⊙ (0.3%), and R C = 0.23196 ± 0.00069 R ⊙ (0.3%). We also estimate the apsidal motion constant of the M dwarfs, a parameter that characterizes the internal mass distribution. Although it is not particularly precise, we measure a mean apsidal motion constant, k2¯ , of 0.046−0.028+0.046 , which is approximately 2σ lower than the theoretical model prediction of 0.150. We explore possible causes for this discrepancy.
The Galaxy Replacement Technique (GRT): A New Approach to Study Tidal Stripping and Formation of Intracluster Light in a Cosmological Context

We introduce the Galaxy Replacement Technique (GRT) that allows us to model tidal stripping of galaxies with very high mass (m star = 5.4 × 104 M ⊙ h −1) and high spatial resolution (10 pc h −1), in a fully cosmological context, using an efficient and fast technique. The technique works by replacing multiple low-resolution dark-matter (DM) halos in the base cosmological simulation with high-resolution models, including a DM halo and stellar disk. We apply the method to follow the hierarchical buildup of a cluster since redshift ∼8 to now, through the hierarchical accretion of galaxies, individually or in substructures such as galaxy groups. We find we can successfully reproduce the observed total stellar masses of observed clusters since redshift ∼1. The high resolution allows us to accurately resolve the tidal stripping process and well describe the formation of ultralow surface brightness features in the cluster (μ V < 32 mag arcsec−2) such as the intracluster light (ICL), shells, and tidal streams. We measure the evolution of the fraction of light in the ICL and brightest cluster galaxy using several different methods. While their broad response to the cluster-mass growth history is similar, the methods show systematic differences, meaning we must be careful when comparing studies that use distinct methods. The GRT represents a powerful new tool for studying tidal effects on galaxies and exploring the formation channels of the ICL in a fully cosmological context and with large samples of simulated groups and clusters.
Towards toxic and narcotic medication detection with rotated object detectors

Background Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry. Intelligent devices for specific medication management could alleviate workload of medical staff by providing assistance services to identify drug specifications and locations. Methods In this work, object detectors based on the you only look once (YOLO) algorithm are tailored for toxic and narcotic medication detection tasks in which there are always numerous of arbitrarily oriented small bottles. Specifically, we propose a flexible annotation process that defines a rotated bounding box with a degree ranging from 0° to 90° without worry about the long-short edges. Moreover, a mask-mapping-based non-maximum suppression method has been leveraged to accelerate the post-processing speed and achieve a feasible and efficient medication detector that identifies arbitrarily oriented bounding boxes. Results Extensive experiments have demonstrated that rotated YOLO detectors are highly suitable for identifying densely arranged drugs. Six thousand synthetic data and 523 hospital collected images have been taken for training of the network. The mean average precision of the proposed network reaches 0.811 with an inference time of less than 300 ms. Conclusions This study provides an accurate and fast drug detection solution for the management of special medications. The proposed rotated YOLO detector outperforms its YOLO counterpart in terms of precision.
A Survey on Machine Learning Techniques for Source Code Analysis

The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landscape. This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we conducted an extensive literature search and identified 479 primary studies published between 2011 and 2021. We summarize our observations and findings with the help of the identified studies. Our findings suggest that the use of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task and summarize machine learning techniques employed. We identify a comprehensive list of available datasets and tools useable in this context. Finally, the paper discusses perceived challenges in this area, including the availability of standard datasets, reproducibility and replicability, and hardware resources.
Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems

Full-duplex (FD) systems have been introduced to provide high data rates for beyond fifth-generation wireless networks through simultaneous transmission of information over the same frequency resources. However, the operation of FD systems is practically limited by self-interference (SI), and efficient SI cancelers are sought to make the FD systems realizable. Typically, polynomial-based cancelers are employed to mitigate the SI; nevertheless, they suffer from high complexity. This article proposes two novel hybrid-layers neural network (NN) architectures to cancel the SI with low complexity. The first architecture is referred to as hybrid-convolutional recurrent NN (HCRNN), whereas the second is termed as hybrid-convolutional recurrent dense NN (HCRDNN). In the HCRNN, a convolutional layer is employed to extract the input data features using a reduced network scale. Moreover, a recurrent layer is then applied to assist in learning the temporal behavior of the input signal from the localized feature map of the convolutional layer. In the HCRDNN, an additional dense layer is exploited to add another degree of freedom for adapting the NN settings in order to achieve the best compromise between the cancellation performance and computational complexity. The complexity analysis of the proposed NN architectures is provided, and the optimum settings for their training are selected. The simulation results demonstrate that the proposed HCRNN and HCRDNN-based cancelers attain the same cancellation of the polynomial and the state-of-the-art NN-based cancelers with an astounding computational complexity reduction. Furthermore, the proposed cancelers show high design flexibility for hardware implementation, depending on the system demands.
Dynamic pricing and assortment under a contextual MNL demand

We consider dynamic multi-product pricing and assortment problems under an unknown demand over T periods, where in each period, the seller decides on the price for each product or the assortment of products to offer to a customer who chooses according to an unknown Multinomial Logit Model (MNL). Such problems arise in many applications, including online retail and advertising. We propose a randomized dynamic pricing policy based on a variant of the Online Newton Step algorithm (ONS) that achieves a $O(d\sqrt{T}\log(T))$ regret guarantee under an adversarial arrival model. We also present a new optimistic algorithm for the adversarial MNL contextual bandits problem, which achieves a better dependency than the state-of-the-art algorithms in a problem-dependent constant $\kappa_2$ (potentially exponentially small). Our regret upper bound scales as $\tilde{O}(d\sqrt{\kappa_2 T}+ \log(T)/\kappa_2)$, which gives a stronger bound than the existing $\tilde{O}(d\sqrt{T}/\kappa_2)$ guarantees.
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters

Adapter layers are lightweight, learnable units inserted between transformer layers. Recent work explores using such layers for neural machine translation (NMT), to adapt pre-trained models to new domains or language pairs, training only a small set of parameters for each new setting (language pair or domain). In this work we study the compositionality of language and domain adapters in the context of Machine Translation. We aim to study, 1) parameter-efficient adaptation to multiple domains and languages simultaneously (full-resource scenario) and 2) cross-lingual transfer in domains where parallel data is unavailable for certain language pairs (partial-resource scenario). We find that in the partial resource scenario a naive combination of domain-specific and language-specific adapters often results in ‘catastrophic forgetting’ of the missing languages. We study other ways to combine the adapters to alleviate this issue and maximize cross-lingual transfer. With our best adapter combinations, we obtain improvements of 3-4 BLEU on average for source languages that do not have in-domain data. For target languages without in-domain data, we achieve a similar improvement by combining adapters with back-translation. Supplementary material is available at https://tinyurl.com/r66stbxj.
Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks

Footsteps are among the most ubiquitous sound effects in multimedia applications. There is substantial research into understanding the acoustic features and developing synthesis models for footstep sound effects. In this paper, we present a first attempt at adopting neural synthesis for this task. We implemented two GAN-based architectures and compared the results with real recordings as well as six traditional sound synthesis methods. Our architectures reached realism scores as high as recorded samples, showing encouraging results for the task at hand.
Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition

Recognizing the feelings of human beings plays a critical role in our daily communication. Neuroscience has demonstrated that different emotion states present different degrees of activation in different brain regions, EEG frequency bands and temporal stamps. In this paper, we propose a novel structure to explore the informative EEG features for emotion recognition. The proposed module, denoted by PST-Attention, consists of Positional, Spectral and Temporal Attention modules to explore more discriminative EEG features. Specifically, the Positional Attention module is to capture the activate regions stimulated by different emotions in the spatial dimension. The Spectral and Temporal Attention modules assign the weights of different frequency bands and temporal slices respectively. Our method is adaptive as well as efficient which can be fit into 3D Convolutional Neural Networks (3D-CNN) as a plug-in module. We conduct experiments on two real-world datasets. 3D-CNN combined with our module achieves promising results and demonstrate that the PST-Attention is able to capture stable patterns for emotion recognition from EEG.