Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Market Competition, Disclosure and Strategic Delegation: Evidence from Revenue-Based Compensation

Using detailed, grant-level compensation data for large publicly listed firms between 19982013, I document that revenue-based compensation is more prevalent in CEO pay packages when the benefits of committing to aggressive product market behavior are greater. Furthermore, this relation does not emerge until after the introduction of the Compensation Discussion and Analysis section of the proxy statement (CD&A), which serves as a plausibly exogenous shock to contract observability. Lastly, post-CD&A product market outcomes are consistent with the shift towards revenue-based pay fostering more aggressive product market equilibria: higher revenues and expenses, but lower margins and profits. Collectively, these results are consistent with a large body of theoretical literature based on Schelling’s (1960) theory of “strategic delegation. ” Notably, Fershtman and Judd (1987) show that oligopolistic competitors can boost profits by strategically incentivizing managers with revenue-based pay to commit to aggressive product market behavior, and Katz (1991) demonstrates that this approach to commitment is only e↵ective if managers’ pay packages are observable to rivals. ⇤I would like to thank Mark Bagnoli, Simcha Barkai, John Barrios, Phil Berger, Mary Billings, Robert Bloomfield, Matthias Breuer, Ben Charoenwong, Hans Christensen, Carlos Corona, Øystein Daljord, Jean Pierre Dubé, Robbi Fox, Pingyang Gao, Joseph Gerakos, Brandon Gipper, Wayne Guay, Steven Kaplan, Anya Kleymenova, Christian Leuz (my chair), Mike Minnis, Sanjog Misra, Valeri Nikolaev, Canice Prendergast, Robert Sanders, Haresh Sapra, Chad Syverson, Doug Skinner, Abbie Smith, Sorabh Tomar, Rodrigo Verdi, Anastasia Zakolyukina and Frank Zhou for their many helpful insights. I would additionally like to thank workshop participants at The University of Chicago and the 2016 Carnegie Mellon Accounting Mini-Conference for their feedback. †The University of Chicago Booth School of Business. Contact me at mbloomfi@chicagobooth.edu.
Energy-efficient cooperative beamforming for secure data transfer

In energy efficient, present cooperative beam forming for wireless ad-hoc networks via cooperative beamforming, nodes in wireless network are able to transmit a common message over a long distance in an energy efficient manner. To improve the performance, have developed a cooperative beamforming algorithm. The idea of cooperative beamforming is to take the help of neighboring nodes to form a beam of electromagnetic energy i.e., the idea of antenna array is used. Since in this type of network the nodes cannot have antenna array to implement beamforming, so take the help of nearby node to participate in beamforming process. Source node in the network is allowed to transmit and collaborating nodes receive the transmitted packets. Subsequently each collaborating node transmits its received signal. One or multiple beams are formed each focusing on one destination node and reinforcing the signal intended for a particular destination as compared to the other signals. The proposed work achieves higher throughput and improved Signal to Noise Ratio in the ad-hoc network.
The evaluation of spoken dialog management models for multimodal HCIs

The implementation of voice dialogs enables the realization of some of the aims of modern Human Computer Interaction (HCI) services more successfully and efficiently. Sadly the multimodal Lithuanian HCIs carried by the most natural form of communication-speech are still in the prototype stage and no services are provided to end user at the time of writing. This paper describes an experimental evaluation of the possibilities of using the spoken language dialogs as the main modality in modern application control. The recognition accuracy of the tree main types of spoken dialogues (dictation, keyword spotting, isolated utterances) was evaluated and user preference survey was done on proposed multimodal HCIs. The goal of this research was to gather the results by possible everyday future users not familiar with such systems.
Analysis of TCP flow control for high speed networks: Case of two nodes in tandem

In this paper we examine the problem of evaluating the performance of TCP connections in a wide area network. Our approach combines analytical and simulation methods. Our model is represented by two finite buffer queues which are shared by two connections. One being the reference TCP connection and the other representing the exogenous traffic (in simulation we consider the UDP traffic). This model, which is an enlarged model with two nodes in tandem shared, is unlike previous simplified models with a single bottleneck node. We use fluid approximation to analyze the behaviour of the TCP. We derive closed-form expression for the average throughput as a function of buffering and characteristics of exogenous traffic. Finally, we use simulation, by Network Simulator (NS) [16], to validate the model, and we find in general good correlation with the analytic results. This means that we can extend our model at more than two nodes in tandem.
Robot Navigation Taking Advantage of Moving Agents

Abstract—A crucial requirement for service robots is to beable to move in dynamic environments shared with humans aswell as interact with them. Navigation in such environments isa challenging task, as the environment is constantly changing,future states have to be predicted and planning and executionmust be carried on-line.However, even in very complex situations, humans can easilyﬁnd a path that avoid both dynamic agents and static obstacles.This paper proposes a technique to take advantage of thehuman movement in such populated environments, selectinga leader to be followed in a probabilistic fashion, according tothe robot’s desired destination.In this way, the robot can take advantage of the pathstraveled by humans, effortlessly avoiding dynamic and staticfeatures as the human leader does, relieving the robot from theburden of having to generate its own path. I. INTRODUCTIONWith advances in mobile robotics and lowering costs ofcomputers, it is becoming more and more common for us toﬁnd robots among groups of people. Service robots (homecare, hospital, museum guides) are real example cases whererobots have to be able to move and interact with humans inan ever changing environment. The success of interactionsand human acceptance of service robots is directly relatedto the way they behave and approach others, as well as theircapability to adapt to the environment.However, navigation in dynamic environments is still anopen and challenging issue for the robotic community. Insuch environments, sensor’s measurements are prone to noiseand the measurements have a short lifespan, being valid onlyfor small time periods. Static features, that could guide anavigation algorithm, might not be detected. There are alsolimitations in the time spent by the navigation algorithm toprovide solutions, and optimal approaches are unsuited forthis task, as generated paths might be valid only for fewtime-steps.Despite these difﬁculties, humans can easily navigatein dynamic environments. Moreover, their movement canprovide indirect information of the environment, as theyusually do not move at random. Instead of that, they moveaccording to typical patterns, and their movements are relatedto features that they are interested in, such as doors, elevators,stairs or other people [1].
A Rigidity Detection System for Automated Credibility Assessment

Credibility assessment is an area in which information systems research can make a major impact. This paper reports on two studies investigating a system solution for automatic, noninvasive detection of rigidity for automated interviewing. Kinesic rigidity has long been a phenomenon of interest in the credibility assessment literature, but until now was infeasible as a veracity indicator in practical use cases. An initial study unexpectedly revealed the occurrence of rigidity in a highly controlled concealed information test setting, prompting the design and implementation of an automated rigidity detection system for interviewing. A unique experimental evaluation supported the system concept. The results of the second study confirmed the kinesic rigidity found in the first, and provided further theoretical insights explaining the rigidity phenomenon. Although additional research is needed, the evidence from this investigation suggests that credibility assessment can benefit from a rigidity detection system.
‘Specialists without spirit and sensualities without heart’? On control in ICT-driven interaction

Control, for a long time, has been an essential part of organisational sociology. The concept has largely been analysed from different epistemological standpoints. These standpoints are informed by the functional and institutional character of control. However, it is the position of this paper that both the functional and institutional identity of control do not capture the nuances of the concept from the viewpoint of its manifestation in day-to-day organisational strategising. It is therefore about time a reconstruction of the concept is considered to include discretion as a means of broadening our conceptualisation on the subject.
Probabilistic Latent Semantic Analysis for Multichannel Biomedical Signal Clustering

This letter extends probabilistic latent semantic analysis (pLSA) for multichannel biomedical signal clustering. The proposed multichannel pLSA (M-pLSA) models a multichannel signal as a generative process of local segments. It directly represents a biomedical signal as a mixture of latent topics based on the assumption that local segments extracted from each channel are conditionally independent given the topics. The categories of biomedical signals are automatically discovered in an unsupervised way. Experimental results demonstrate that the proposed M-pLSA model outperforms previous state-of-the-art methods and is robust to noise contamination.
Energy efficient small cell activation mechanism for heterogeneous networks

In this paper, we present a novel small cell activation mechanism for heterogeneous networks, which considers the energy saving gains obtained based on the amount of traffic load off-loaded from the macro cell to small cells, while avoiding UE QoS degradation. The main idea is to off-load traffic to small cells in energy saving mode, only when there is significant energy saving gains achievable from the process, thereby reducing the total energy consumption of the network. The mechanism is evaluated using LTE-Advanced heterogeneous network system setting, in terms of energy saving gains as well as user throughput. The results show that with the considered scheme, significant power savings can be achieved with acceptable tradeoffs in off-loading performance, compared to an optimized proximity-based small cell activation scheme.
Impact of limited backhaul capacity on user scheduling in heterogeneous networks

In this paper, we investigate the impact of limited backhaul capacity on user scheduling in the context of a heterogeneous network comprising a macro base station overlaid with small-cells. Under a global proportional fairness (PF) criteria, we show that this limited capacity has a fundamental impact on user scheduling. When user association and channel allocation are given, like in the infinite-capacity backhaul case, the global PF user scheduling problem can be decomposed into a set of independent local PF user scheduling problems. However, unlike the case with infinite backhaul where the local PF scheme is equivalent to giving equal time to each user, a local PF scheme with finite backhaul can be of one of three types. We completely characterize these three types and the conditions under which to use them. The results show that a backhaul-aware scheduling scheme is simple to implement and necessary to obtain the best performance.
DIPC : A Heterogeneous Distr ibuted Programming System

Distr ibuted Inter-Process Communication (DIPC) is a software-only solution to enable people to build and program Multi-Computers. Among other things, DIPC provides the programmers with Transparent Distr ibuted Shared Memory. DIPC is not concerned with the incompatible executable code problem. I t also does not change user’s data, but DIPC has to understand data produced by its own peer processes in different machines. This paper br iefly describes the effor ts done to make it a heterogeneous system by enabling it to function under the L inux operating system running on both Intel x86 and Motorola 680x0 processors. Here, different par ts of the same application could execute in machines with different architectures
Compressed Indexes for String Searching in Labeled Graphs

Storing and searching large labeled graphs is indeed becoming a key issue in the design of space/time efficient online platforms indexing modern social networks or knowledge graphs. But, as far as we know, all these results are limited to design compressed graph indexes which support basic access operations onto the link structure of the input graph, such as: given a node u, return the adjacency list of u. This paper takes inspiration from the Facebook Unicorn's platform and proposes some compressed-indexing schemes for large graphs whose nodes are labeled with strings of variable length - i.e., node's attributes such as user's (nick-)name - that support sophisticated search operations which involve both the linked structure of the graph and the string content of its nodes. An extensive experimental evaluation over real social networks will show the time and space efficiency of the proposed indexing schemes and their query processing algorithms.
IR issues for digital ecosystems users

The purpose of this research is to discuss some challenges of information retrieval, especially Web information retrieval, in digital ecosystems from a userpsilas perspective. As a dominant search tool, search engines usually return millions of search results in a long flat list in which many or even most of the results can be irrelevant. The long flat list conveys nothing about knowledge structure related to the retrieved results and personal search preferences and interests are not explored. Although some search engines try to cluster the Web results, the automatically formed titles and knowledge hierarchy is prone to mismatching the searcherpsilas human mental model. In digital ecosystems, while many different search tools are available, they are not integrated. To address these issues, a search framework which combines categorization, clustering, ontology, and personalization is proposed, and thus the quality of search results in digital ecosystems is expected to be boosted.
A collaborative botnets suppression system based on overlay network

Botnets are extremely versatile programs used in many network attacks, such as sending large volumes of spam or launching Distributed Denial-of-Service DDoS attacks. Botnets can switch command-and-control servers automatically, which makes completely suppressing botnets very challenging. In this paper, we present a collaborative botnet suppression system based on an overlay network, with one control center node and several suppression nodes. The suppression nodes automatically collect network traffic information and deploy suppression rules; the control center node gathers all collected data, and processes this data by using a botnet detection algorithm. Once botnets are detected, the control center node generates and distributes suppression rules. In order to prevent an excessive growth of the rules set, the system automatically identifies and removes invalid rules through an efficient feedback mechanism.
"hello - Is Anybody at Home?" - about the Minimum Word Accuracy of a Smart Home Spoken Dialogue System

This paper presents the assessment of the minimum word accuracy of an automatic speech recognizer within a spoken dialogue system, which is required to reach acceptable quality. The spoken dialogue system has been developed in the context of the European INSPIRE project [3]. It is used to control a smart home environment. The assessment of the minimum word accuracy is done within the assessment of all modules of the INSPIRE system. The outcome of this assessment will act as a benchmark for the developers of other modules, mainly of the speech preprocessing (microphone array with beam forming and denoising) and for the engineers and phoneticians tuning the automatic speech recognizer. The article describes the test and the outcome of the estimation of the minimum word accuracy necessary to use INSPIRE in an acceptable way.
A quadratic programming approach to simultaneous buffer insertion/sizing and wire sizing

In this paper, we present a completely new approach to the problem of delay minimization by simultaneous buffer insertion and wire sizing for a wire. We show that the problem can be formulated as a convex quadratic program, which is known to be solvable in polynomial time. Nevertheless, we explore some special properties of our problem and derive an optimal and very efficient algorithm, modified active set method (MASM), to solve the resulting program. Given m buffers and a set of m discrete choices of wire width, the running time of our algorithm is O(mn/sup 2/) and is independent of the wire length in practice. For example, an instance of 100 buffers and 100 choices of wire width can be solved in 0.92 s. In addition, we extend MASM to consider simultaneous buffer insertion, buffer sizing, and wire sizing. The resulting algorithm MASM-BS is again optimal and very efficient. For example, with six choices of buffer size and 10 choices of wire width, the optimal solution for a 15000 /spl mu/m long wire can be found in 0.05 s. Besides, our formulation is so versatile that it is easy to consider other objectives like wire area or power dissipation, or to add constraints to the solution. Also, wire capacitance lookup tables, or very general wire capacitance models which can capture area capacitance, fringing capacitance, coupling capacitance, etc. can be used.
Estrogens and Spermiogenesis: New Insights from Type 1 Cannabinoid Receptor Knockout Mice

Spermatogenesis is a complex mechanism which allows the production of male gametes; it consists of mitotic, meiotic, and differentiation phases. Spermiogenesis is the terminal differentiation process during which haploid round spermatids undergo several biochemical and morphological changes, including extensive remodelling of chromatin and nuclear shape. Spermiogenesis is under control of endocrine, paracrine, and autocrine factors, like gonadotropins and testosterone. More recently, emerging pieces of evidence are suggesting that, among these factors, estrogens may have a role. To date, this is a matter of debate and concern because of the agonistic and antagonistic estrogenic effects that environmental chemicals may have on animal and human with damaging outcome on fertility. In this review, we summarize data which fuel this debate, with a particular attention to our recent results, obtained using type 1 cannabinoid receptor knockout male mice as animal model.
A Quick Classification for Area Environmental Audio Data Based on Local Search Tree

In area environment, various forms of life can exhale different kinds of audio signals, and these audio signals are closely related to a variety of organisms living environment and human activities. For analyzing these audio data automatically, local search data structure is proposed in this paper, that it is local search tree (LS-Tree), to classify audio data in the natural environment rapidly. Firstly, a technology for classification is brought forward in this paper using effective segment length of audio data (ESLOAD), frequency component of maximum harmonic weight (FCOMHW) and first order difference Mel-frequency cepstral coefficients matrix (D-MFCCM) in a segment. Secondly, a LS-Tree is pro- posed in this paper. ESLOAD and FCOMHW in segment are chose as main and second key for the LS-Tree respectively. Classification parameters are classified and managed through the LS-tree. Finally, through experiment of nine categories totally 107 normal audio data, it is indicated that the technique is effective to classify many kinds of audio data in the natural environment.
Analyzing NMR spectra with the Morlet wavelet

We study the time-scale representation provided by the Morlet wavelet transform for characterizing NMR signals. From an analytical analysis and simulations, we conclude that the wavelet shows a satisfactory performance even when a baseline, an additive Gaussian noise or a solvent are present in the signals. It can also cope with non-Lorentzian lineshapes which commonly occur because of the inhomogeneous distribution of molecules in a substance. These results mean that the Morlet wavelet transform is a potential tool to quantify in vivo NMR signals.
Do Liquidity Constraints and Interest Rates Matter for Consumer Behavior? Evidence from Credit Card Data

This paper utilizes a unique new dataset of credit card accounts to analyze how people respond to changes in credit supply. The data consist of a panel of thousands of individual credit card accounts from several different card issuers, with associated credit bureau data. We estimate both marginal propensities to consume (MPCs) out of liquidity and interest-rate elasticities. We also evaluate the ability of different models of consumption to rationalize our results, distinguishing the Permanent-Income Hypothesis (PIH), liquidity constraints, precautionary saving, and behavioral models. We find that increases in credit limits generate an immediate and significant rise in debt, counter to the PIH. The average 'MPC out of liquidity' (dDebt/dLimit) ranges between 10%-14%. The MPC is much larger for people starting near their limits, consistent with binding liquidity constraints. However, the MPC is significant even for people starting well below their limit. We show this response is consistent with buffer-stock models of precautionary saving. Nonetheless there are other results that conventional models cannot easily explain, e.g. why so many people are borrowing on their credit cards, and simultaneously holding low yielding assets. Unlike most other studies, we also find strong effects from changes in account-specific interest rates. The long-run elasticity of debt to the interest rate is approximately -1.3. Less than half of this elasticity represents balance-shifting across cards, with most reflecting net changes in total borrowing. The elasticity is larger for decreases in interest rates than for increases, which can explain the widespread use of temporary promotional rates. The elasticity is smaller for people starting near their credit limits, again consistent with liquidity constraints.
Thermodynamic properties, hysteresis behavior and stress-strain analysis of MgH2 thin films, studied over a wide temperature range

Using hydrogenography, we investigate the thermodynamic parameters and hysteresis behavior in Mg thin films capped by Ta/Pd, in a temperature range from 333 K to 545 K. The enthalpy and entropy of hydride decomposition, ? Hdes = ? 78.3 kJ/molH2, ? Sdes = ? 136.1 J/K molH2, estimated from the Van't Hoff analysis, are in good agreement with bulk results, while the absorption thermodynamics, ? Habs = ? 61.6 kJ/molH2, ? Sabs = ? 110.9 J/K molH2, appear to be substantially affected by the clamping of the film to the substrate. The clamping is negligible at high temperatures, T > 523 K, while at lower temperatures, T < 393 K, it is considerable. The hysteresis at room temperature in Mg/Ta/Pd films increases by a factor of 16 as compared to MgH2 bulk. The hysteresis increases even further in Mg/Pd films, most likely due to the formation of a Mg-Pd alloy at the Mg/Pd interface. The stress–strain analysis of the Mg/Ta/Pd films at 300–333 K proves that the increase of the hysteresis occurs due to additional mechanical work during the (de-)hydrogenation cycle. With a proper temperature correction, our stress–strain analysis quantitatively and qualitatively explains the hysteresis behavior in thin films, as compared to bulk, over the whole temperature range.