Miracle-dz/UniFAR-FTUs huggingface dataset:
Budgetary Participation: The Effects of Information Asymmetry, Goal Commitment, and Role Ambiguity on Job Satisfaction and Performance

This study proposes a comprehensive model which empirically investigates the antecedent, mediating and outcome variables of budgetary participation. We propose that the information asymmetry between superiors and subordinates creates the need for budgetary participation. Accordingly, this study hypothesizes that the outcomes (i.e., job satisfaction and performance) of budgetary participation will be mediated by goal commitment and role ambiguity. Based on a questionnaire survey of 194 middle level managers in Turkey, this study found that role ambiguity mediates the budgetary participation-job satisfaction and budgetary participation-performance relationships. The results are consistent with the view that the primary benefit of budgetary participation is to decrease role ambiguity, leading to more job satisfaction and better performance. Furthermore, we found that goal commitment mediates the budgetary participation-performance relationship. This result suggests that participation in the budgeting process increases goal commitment leading to better performance.
Value Relevance of FAS 157 Fair Value Hierarchy Information and the Impact of Corporate Governance Mechanisms

Statement of Financial Accounting Standards No. 157 (FAS 157), Fair Value Measurements, prioritizes the source of information used in fair value measurements into three levels: (1) Level 1 (observable inputs from quoted prices in active markets), (2) Level 2 (indirectly observable inputs from quoted prices of comparable items in active markets, identical items in inactive markets, or other market-related information), and (3) Level 3 (unobservable, firm-generated inputs). Using quarterly reports of banking firms in 2008, we find that the value relevance of Level 1 and Level 2 fair values is greater than the value relevance of Level 3 fair values. In addition, we find evidence that the value relevance of fair values (especially Level 3 fair values) is greater for firms with strong corporate governance. Overall, our results support the relevance of fair value measurements under FAS 157, but weaker corporate governance mechanisms may reduce the relevance of these measures.
What are the Costs of Conflicts between Controlling and Minority Shareholders in a Concentrated Ownership Environment?

The main agency conflict in concentrated ownership environments occurs between controlling and minority shareholders. We investigate the impact on share prices when news released on the specialized media indicates that the interests of these groups diverge. Specifically, we analyze 24 announcements of conflicts between shareholder groups in Brazil using two different event study methodologies. The results strongly support our hypothesis that such news constitutes an evidence of relevant agency costs taking place, leading to a higher perception of risk and reduced share prices. We find a strong negative stock price reaction to the announcement of such conflicts, with cumulative abnormal returns of around 12% for the 15 days surrounding the event date. Besides, this negative impact do not seems to be transitory, since there is no post-event positive drift. Overall, the results reinforce the importance of the adoption of good corporate governance practices in order to avoid corporate value destruction due to problems between shareholder groups.
New Evidence on the Pricing and Performance of Initial Public Offerings in Thailand: 1997–2008

This paper examines the pricing and performance of initial public offerings (IPOs) in the Stock Exchange of Thailand (SET) from February 1997 to October 2008. Underpricing is calculated using headline underpricing, underpricing issuer loss, underpricing loss by market value, and underpricing loss by issuer price. The results show that average underpricing of 17.60%, 5.01%, 6.94%, 6.68% and 16.10%, respectively. This is a significantly lower level of underpricing than previously documented in Thailand before the 1997 Asian financial crisis. Post-listing performance is assessed using monthly cumulative abnormal returns, buy-and-hold returns and wealth relatives. The findings show that Thai IPOs generally outperform market benchmarks up to 24 months and underperform thereafter up to 36 months. However, there is much variation in long-run performance among industries. For example, multiple regression analysis shows that IPOs in the financial services industry perform relatively better in the longer run.
The European Equal Opportunity Rule in Transfer of Control: A Signaling Model

Introduced in the European Union, the equal opportunity rule is seen as protecting investors in the event of a transfer of control. This rule is analyzed in a setting of information asymmetry and future private benefits between the new controlling shareholders and the outside investors. Both parties need to design a new implicit contract to share the firm’s ownership. Using a signaling model, we show that the new controlling shareholder issues signals to outside shareholders to deliver private information on the firm’s future economic return and his private rate of appropriation. Ownership stake of the controlling shareholder and the premium embedded in the acquisition price are key parameters. In a controlling ownership system, the equal opportunity rule modifies the relative behaviors of controlling and outside shareholders. The quality of information deteriorates despite the fact that the discipline may be stronger.
Shareholders in the Boardroom: Wealth Effects of the SEC’s Rule to Facilitate Director Nominations

Current attempts to reform financial markets presume that shareholder empowerment benefits shareholders. We investigate the wealth effects associated with the SEC’s rule to facilitate director nominations by shareholders. Our results are not in line with shareholder empowerment creating value: the average daily abnormal returns surrounding events that increase (decrease) the probability of passage of the proposal are significantly negative (positive). Furthermore, given an increase in the probability of passage of the proposal, firms whose shareholders are more likely to use the rule to nominate directors experience more negative abnormal returns.
Buy-Sell Imbalances On and Around Round Numbers* Forthcoming in Management Science

This paper provides evidence that stock traders focus on round numbers as cognitive reference points for value. Using a random sample of more than 100 million stock transactions, we find excess buying (selling) by liquidity demanders at all price points one penny below (above) round numbers. Further, the size of the buy-sell imbalance is monotonic in the roundness of the adjacent round number (i.e., largest adjacent to integers, second-largest adjacent to half-dollars, etc.). Conditioning on the price path, we find much stronger excess buying (selling) by liquidity demanders when the ask falls (bid rises) to reach the integer than when it crosses the integer. We discuss and test three explanations for these results. Finally, 24-hour returns also vary by price point and buy-sell imbalances are a major determinant of that variation across price points. Buying (selling) by liquidity demanders below (above) round numbers yield losses approaching $1 billion per year. JEL classification: C15, G12, G20.
GERENCIALES 115.indd

The purpose of this study is to find evidence that shows that either active management of private pension funds in Colombia actually adds value to the investors or, on the contrary, investors would achieve better results if they invested in passively managed products such as, for example, an ETF (Exchange Trade Fund). After conducting a review of data available from 30 different private pension funds in Colombia and 30 ETFs that had similar investment goals to these portfolios, our findings reveal that, using common performance indicators, a Colombian investor would have a better ROI by investing in passively managed products (ETFs) than in portfolios currently offered by voluntary pension funds in Colombia.
WATER INFILTRATION IN TWO CULTIVATED SOILS IN SOUTHERN BRAZIL

Infiltration is the passage of water through the soil surface, influenced by the soil type and cultivation and by the soil roughness, surface cover and water content. Infiltration absorbs most of the rainwater and is therefore crucial for planning mechanical conservation practices to manage runoff. This study determined water infiltration in two soil types under different types of management and cultivation, with simulated rainfall of varying intensity and duration applied at different times, and to adjust the empirical model of Horton to the infiltration data. The study was conducted in southern Brazil, on Dystric Nitisol (Nitossolo Bruno aluminoferrico humico) and Humic Cambisol (Cambissolo Humico aluminico leptico) soils to assess the following situations: simulated rains on the Nitisol from 2001 to 2012 in 31 treatments, differing in crop type, sowing direction, type of soil opener on the seeder, amount and type of crop residue and amount of liquid swine manure applied; on the Cambisol, rains were simlated from 2006 to 2012 and 18 treatments were evaluated, differing in crop, seeding direction and crop residue type. The constant of the water infiltration rate into the soil varies significantly with the soil type (30.2 mm h-1 in the Nitisol and 6.6 mm h-1 in the Cambisol), regardless of the management system, application time and rain intensity and duration. At the end of rainfalls, soil-water infiltration varies significantly with the management system, with the timing of application and rain intensity and duration, with values ranging from 13 to 59 mm h-1, in the two studied soils. The characteristics of the sowing operation in terms of relief, crop type and amount and type of crop residue influenced soil water infiltration: in the Nitisol, the values of contour and downhill seeding vary between 27 and 43 mm h-1, respectively, with crop residues of corn, wheat and soybean while in the Cambisol, the variation is between 2 and 36 mm h-1, respectively, in soybean and corn crops. The Horton model fits the values of water infiltration rate into the soil, resulting in the equation i = 30.2 + (68.2 - 30.2) e-0.0371t (R2 = 0.94**) for the Nitisol and i = 6.6 + (64.5 - 6.6) e-0.0537t (R2 = 0.99**) for the Cambisol.
Clarity Plusâ¢ digital PCR: A novel platform for absolute quantification of SARS-CoV-2

In recent years, the usage of digital polymerase chain reaction (dPCR) for various clinical applications has increased exponentially. Considering the growing demand for improved dPCR technology, the Clarity Plus dPCR system which features enhanced multiplexing capability and a wider dynamic range for nucleic acid analysis was recently launched. In this study, a dPCR assay optimized for use on Clarity Plus was evaluated for the absolute quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent responsible for the global coronavirus disease 2019 (COVID-19) outbreak. The assay demonstrated good inter- and intra- assay precision, accuracy, as well as excellent linearity across a range of over 6 orders of magnitude for target gene quantification. In addition, comparison of the assay on both dPCR and qPCR platforms revealed that dPCR exhibited a slightly higher sensitivity compared to its qPCR counterpart when quantifying SARS-CoV-2 at a lower concentration. Overall, the results showed that the dPCR assay is a reliable and effective approach for the absolute quantification of SARS-CoV-2 and can potentially be adopted as a molecular tool in applications such as detecting low viral loads in patients as well as in wastewater surveillance of COVID-19.
Aerosol SARS-CoV-2 in hospitals and long-term care homes during the COVID-19 pandemic

Background: Few studies have quantified aerosol concentrations of SARS-CoV-2 in hospitals and long-term care homes, and fewer still have examined samples for viability. This information is needed to clarify transmission risks beyond close contact. Methods: We deployed particulate air samplers in rooms with COVID-19 positive patients in hospital ward and ICU rooms, rooms in long-term care homes experiencing outbreaks, and a correctional facility experiencing an outbreak. Samplers were placed between 2 and 3 meters from the patient. Aerosol (small liquid particles suspended in air) samples were collected onto gelatin filters by Ultrasonic Personal Air Samplers (UPAS) fitted with <2.5{micro}m (micrometer) and <10{micro}m size-selective inlets operated for 16 hours (total 1.92m3), and with a Coriolis Biosampler over 10 minutes (total 1.5m3). Samples were assayed for viable SARS-CoV-2 virus and for the viral genome by multiplex PCR using the E and N protein target sequences. We validated the sampling methods by inoculating gelatin filters with viable vesicular stomatitis virus (VSV), and with three concentrations of viable SARS-CoV-2, operating personal samplers for 16hrs, and quantifying viable virus recovery by TCID50 assay. Results: In total, 138 samples were collected from 99 rooms. RNA samples were positive in 9.1% (6/66) of samples obtained with the UPAS 2.5{micro}m samplers, 13.5% (7/52) with the UPAS 10{micro}m samplers, and 10.0% (2/20) samples obtained with the Coriolis samplers. Culturable virus was not recovered in any samples. Viral RNA was detected in 10.9% of the rooms sampled. There was no significant difference in viral RNA recovery between the different room locations or samplers. Method development experiments indicated minimal loss of SARS-CoV-2 viability via the personal air sampler operation. Key Findings: Although a subset of aerosol samples exhibited detectable SARS-CoV-2 RNA at low titres, the presence of viable SARS-CoV-2 virus in aerosols appears to be infrequent at >2m distance.
Determination of Total Flavone in Licorice by Spectrophotometry

Objective:A method for determination of total flavone in Licorice was set up.Method :Using the charac- teristic that flavanonols are easily transformed chalcones under alkali conditions.Reference substance was liquiritin, 10% KOH was chromogenic agent and the detection wavelength was at 400nm.Result:The linear range was be- tween 2.05-41. 13μg·mL~(-1)(r=0.9992)and the average recovery was 97.6% with corresponding RSD of 2.0%(n=3). Conclusion:This method is simple,convenient,rapid and is suitable for determination of total fla- vone in Licorice.
In recent years considerable attention has been devoted to differences across countries in the institutional environments in which corporations operate, and the consequences of these institutional differences for corporate performance

In recent years considerable attention has been devoted to differences across countries in the institutional environments in which corporations operate, and the consequences of these institutional differences for corporate performance. In this paper we test for the presence of differences in corporate performance across 38 countries, and in particular whether these differences are related to the types of legal systems existing in each country. To measure corporate performance we estimate returns on investment relative to company costs of capital in each country. We find significant differences in the investment performance of corporations across our sample of countries. Companies in countries with English-origin, common law legal systems perform significantly better on average than do those in civil law systems.
ALP-portal heavy neutrino dark matter

Shivam Gola, 2, ∗ Sanjoy Mandal, † and Nita Sinha 2, ‡ 1The Institute of Mathematical Sciences, C.I. T Campus, Taramani, Chennai 600 113, India 2Homi Bhabha National Institute, BARC Training School Complex, Anushakti Nagar, Mumbai 400094, India 3AHEP Group, Institut de F́ısica Corpuscular, CSIC/Universitat de València, Parc Cient́ıfic de Paterna. C/ Catedrático José Beltrán, 2 E-46980 Paterna (Valencia), Spain
The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.
Assembly Planning by Recognizing a Graphical Instruction Manual

This paper proposes a robot assembly planning method by automatically reading the graphical instruction manuals designed for humans. Essentially, the method generates an Assembly Task Sequence Graph (ATSG) by recognizing a graphical instruction manual. An ATSG is a graph describing the assembly task procedure by detecting types of parts included in the instruction images, completing the missing information automatically, and correcting the detection errors automatically. To build an ATSG, the proposed method first extracts the information of the parts contained in each image of the graphical instruction manual. Then, by using the extracted part information, it estimates the proper work motions and tools for the assembly task. After that, the method builds an ATSG by considering the relationship between the previous and following images, which makes it possible to estimate the undetected parts caused by occlusion using the information of the entire image series. Finally, by collating the total number of each part with the generated ATSG, the excess or deficiency of parts are investigated, and task procedures are removed or added according to those parts. In the experiment section, we build an ATSG using the proposed method to a graphical instruction manual for a chair and demonstrate the action sequences found in the ATSG can be performed by a dual-arm robot execution. The results show the proposed method is effective and simplifies robot teaching in automatic assembly.
Node-Variant Graph Filters in Graph Neural Networks

Graph neural networks (GNNs) have been successfully employed in a myriad of applications involving graph signals. Theoretical findings establish that GNNs use nonlinear activation functions to create low-eigenvalue frequency content that can be processed in a stable manner by subsequent graph convolutional filters. However, the exact shape of the frequency content created by nonlinear functions is not known and cannot be learned. In this work, we use node-variant graph filters (NVGFs) –which are linear filters capable of creating frequencies–as a means of investigating the role that frequency creation plays in GNNs. We show that, by replacing nonlinear activation functions by NVGFs, frequency creation mechanisms can be designed or learned. By doing so, the role of frequency creation is separated from the nonlinear nature of traditional GNNs. Simulations on graph signal processing problems are carried out to pinpoint the role of frequency creation.
A Schottky-Diode-Based Wake-Up Receiver for IoT Applications

This paper presents an always-on low-power wake-up receiver (WuRx) that activates the remainder of the system when a wake-up signal is detected. The proposed receiver has two phases of waking up. The first phase uses an integrated CMOS Schottky diodes to detect the signal power at a low bias current. The approach dissipates low quiescent power and allows the reuse of the design in multiple frequency bands with only modifying the matching network. In the second phase, a data-locked startable oscillator is proposed to correlate the received data with a target signature. This design eliminates the area and power dissipation of an external crystal oscillator and only turns on when the second phase is activated. By correlating to a target signature, the second phase also reduces the probability of a false alarm (PFA) that would otherwise wake up the high-power bulk of the system. The two-phase approach leads to significant reduction in average power consumption when compared to a single-phase design. This implementation targets sub-ms wake-up latency and operates in the unlicensed band at a 750-MHz carrier frequency with a data rate of 200 kbps. The design achieves $\sim$8.45pJ/bit and $<$-50 dBm of input sensitivity and average power of 1.69$\mu$W. The system is implemented in 65-nm CMOS technology and occupies an area of 1mm$\times$0.75mm.
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL

We study session-based recommendation scenarios where we want to recommend items to users during sequential interactions to improve their long-term utility. Optimizing a long-term metric is challenging because the learning signal (whether the recommendations achieved their desired goals) is delayed and confounded by other user interactions with the system. Immediately measurable proxies such as clicks can lead to suboptimal recommendations due to misalignment with the long-term metric. Many works have applied episodic reinforcement learning (RL) techniques for session-based recommendation but these methods do not account for policy-induced drift in user intent across sessions. We develop a new batch RL algorithm called Short Horizon Policy Improvement (SHPI) that approximates policy-induced distribution shifts across sessions. By varying the horizon hyper-parameter in SHPI, we recover well-known policy improvement schemes in the RL literature. Empirical results on four recommendation tasks show that SHPI can outperform matrix factorization, offline bandits, and offline RL baselines. We also provide a stable and computationally efficient implementation using weighted regression oracles.