Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial acquisition of Canadian companies by domestic and foreign companies : a valuation analysis

This research examines domestic and foreign partial acquisitions in Canada and investigates the resulting returns to target shareholders. The sample of the research is Canadian companies partially acquired from 1997 to 2005. The results show significant positive abnormal returns around the announcement date, which implies rising takeover expectation in the market. They provide a signal for investors to buy stocks before the announcement. The results also reveal negative abnormal returns in the one-year post acquisition period for targets, which implies diminishing takeover expectation. It is not recommended that investors invest a large amount without existing evidence of plans for a complete takeover by the acquirer. The return difference between the targets of domestic and foreign partial acquisitions is not significant around the announcement and in the post acquisition period. This implies that the market reaction for foreign partial acquisitions is not statistically different from domestic partial acquisitions.
Link Prediction through Deep Learning

Inferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1,2, e-commerce3, social media4 and criminal intelligence5. Numerous methods have been proposed to solve the link prediction problem6–8. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics9, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models10 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities11. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains12–16, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance.
The endosomal trafficking regulator LITAF controls the cardiac Nav1.5 channel via the ubiquitin ligase NEDD4-2

The QT interval is a recording of cardiac electrical activity. Previous genome-wide association studies identified genetic variants that modify the QT interval upstream of LITAF (lipopolysaccharide-induced tumor necrosis factor-α factor), a protein encoding a regulator of endosomal trafficking. However, it was not clear how LITAF might impact cardiac excitation. We investigated the effect of LITAF on the voltage-gated sodium channel Nav1.5, which is critical for cardiac depolarization. We show that overexpressed LITAF resulted in a significant increase in the density of Nav1.5-generated voltage-gated sodium current INa and Nav1.5 surface protein levels in rabbit cardiomyocytes and in HEK cells stably expressing Nav1.5. Proximity ligation assays showed co-localization of endogenous LITAF and Nav1.5 in cardiomyocytes, whereas co-immunoprecipitations confirmed they are in the same complex when overexpressed in HEK cells. In vitro data suggest that LITAF interacts with the ubiquitin ligase NEDD4-2, a regulator of Nav1.5. LITAF overexpression down-regulated NEDD4-2 in cardiomyocytes and HEK cells. In HEK cells, LITAF increased ubiquitination and proteasomal degradation of co-expressed NEDD4-2 and significantly blunted the negative effect of NEDD4-2 on INa. We conclude that LITAF controls cardiac excitability by promoting degradation of NEDD4-2, which is essential for removal of surface Nav1.5. LITAF-knockout zebrafish showed increased variation in and a nonsignificant 15% prolongation of action potential duration. Computer simulations using a rabbit-cardiomyocyte model demonstrated that changes in Ca2+ and Na+ homeostasis are responsible for the surprisingly modest action potential duration shortening. These computational data thus corroborate findings from several genome-wide association studies that associated LITAF with QT interval variation.
Development of electrically conductive porous silk fibroin/carbon nanofiber scaffolds

Tissue engineering applications typically require three-dimensional scaffolds which provide the requisite surface area for cellular functions, while allowing transport of nutrients, waste and oxygen to and from the surrounding tissues. Scaffolds need to ensure sufficient mechanical properties to provide mechanically stable frameworks under physiologically relevant stress levels. Meanwhile, electrically conductive platforms are also desirable for the regeneration of specific tissues, where electrical impulses are transmitted throughout the tissue for proper physiological functioning. Towards this goal, carbon nanofibers (CNFs) were incorporated into silk fibroin (SF) scaffolds whose pore size and porosity were controlled during a salt leaching process. In our methodology, CNFs were dispersed in SF due to the hydrogen bond-forming ability of hexafluoro-2-propanol, a fluoroalcohol used as a solvent for SF. Results showed enhanced electrical conductivity and mechanical properties upon the incorporation of CNFs into the SF scaffolds, while the metabolic activities of cells cultured on SF/CNF nanocomposite scaffolds were significantly improved by optimizing the CNF content, porosity and pore size range of the scaffolds. Specifically, SF/CNF nanocomposite scaffolds with electrical conductivities as high as 0.023 S cm−1, tangent modulus values of 260 ± 30 kPa, a porosity as high as 78% and a pore size of 376 ± 53 µm were fabricated for the first time in the literature. Furthermore, an increase of about 34% in the wettability of SF was achieved by the incorporation of 10% CNF, which provided enhanced fibroblast spreading on scaffold surfaces.
Antibacterial Property and Biocompatibility of Silver, Copper, and Zinc in Titanium Dioxide Layers Incorporated by One-Step Micro-Arc Oxidation: A Review

Titanium (Ti) and its alloys are commonly used in medical devices. However, biomaterial-associated infections such as peri-implantitis and prosthetic joint infections are devastating and threatening complications for patients, dentists, and orthopedists and are easily developed on titanium surfaces. Therefore, this review focuses on the formation of biofilms on implant surfaces, which is the main cause of infections, and one-step micro-arc oxidation (MAO) as a coating technology that can be expected to prevent infections due to the implant. Many researchers have provided sufficient data to prove the efficacy of MAO for preventing the initial stages of biofilm formation on implant surfaces. Silver (Ag), copper (Cu), and zinc (Zn) are well used and are incorporated into the Ti surface by MAO. In this review, the antibacterial properties, cytotoxicity, and durability of these elements on the Ti surface incorporated by one-step MAO will be summarized. This review is aimed at enhancing the importance of the quantitative control of Ag, Cu, and Zn for their use in implant surfaces and the significance of the biodegradation behavior of these elements for the development of antibacterial properties.
Weighted QMIX: Expanding Monotonic Value Function Factorisation

QMIX is a popular $Q$-learning algorithm for cooperative MARL in the centralised training and decentralised execution paradigm. In order to enable easy decentralisation, QMIX restricts the joint action $Q$-values it can represent to be a monotonic mixing of each agent's utilities. However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on other agents' actions. To analyse this representational limitation, we first formalise the objective QMIX optimises, which allows us to view QMIX as an operator that first computes the $Q$-learning targets and then projects them into the space representable by QMIX. This projection returns a representable $Q$-value that minimises the unweighted squared error across all joint actions. We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action. We rectify this by introducing a weighting into the projection, in order to place more importance on the better joint actions. We propose two weighting schemes and prove that they recover the correct maximal action for any joint action $Q$-values, and therefore for $Q^*$ as well. Based on our analysis and results in the tabular setting we introduce two scalable versions of our algorithm, Centrally-Weighted (CW) QMIX and Optimistically-Weighted (OW) QMIX and demonstrate improved performance on both predator-prey and challenging multi-agent StarCraft benchmark tasks.
Causal Modeling with Stochastic Confounders

This work extends causal inference with stochastic confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a random input space. We estimate causal effects involving latent confounders that may be interdependent and time-varying from sequential, repeated measurements in an observational study. Our approach extends current work that assumes independent, non-temporal latent confounders, with potentially biased estimators. We introduce a simple yet elegant algorithm without parametric specification on model components. Our method avoids the need for expensive and careful parameterization in deploying complex models, such as deep neural networks, for causal inference in existing approaches. We demonstrate the effectiveness of our approach on various benchmark temporal datasets.
Fibrous Carbohydrate Fractions and in Vitro True and Apparent Digestibility of 10 Tropical Forage Grasses

Forage grasses constitute the most economical form of cattle feeding. This is especially so in the Humid Tropic where potential exists for forage production throughout the year. The utilization of forages depends principally upon changes that occur in chemical composition and digestibility during the vegetative cycle. The Weende scheme of feed analyses (19) has been widely utilized as a means to determine the chemical composition of forage grasses. Recently, Van Soest (30) proposed new methods of analyses based on the use of detergents which render more exact and precise results. Furthermore, in vitro techniques (6,7,27,34), have been developed to estimate digestibility which are simpler, cheaper and considered to be as accurate as in vivo techniques. The nutritive evaluation of forage grasses utilizing the techniques of Goering and Van Soest (16) is rather limited, especially in the Tropics. For this reason, this methodology was employed in this study to determine chemical composition and in vitro digestibility. The wide margin from 30 to 180 days of plant age, covering 30-day cutting intervals, offers a better appreciation of the variations in nutritive value at different growth stages of forage grasses.
Estimation of Self-Posture of a Pedestrian Using MY VISION Based on Depth and Motion Network

A system is proposed that performs gait analysis of a pedestrian to improve a walk posture and at the same time to prevent a fall. In the system, a user walks with a chest-mounted camera. His/her walking posture is estimated using a pair of images obtained from the camera. Normally it is difficult to estimate the camera movement, when the parallax of the image pair is small. Therefore, the system uses a convolutional neural network. Optical flow and camera movement, and depth images are estimated alternately. Satisfactory results were obtained experimentally.
The impact of employees' commitment on organizational performances

This paper examines the impact of employees’ commitment on the productivity of organizations in the Republic of Serbia. Employees’ commitment represents the willingness of employees to make efforts for the benefit of the organization and the desire to remain in them. It can be seen as: affective, continuous and normative commitment. Affective commitment reflects a belief in the goals of an organization and a willingness to actively participate in its development; normative commitment reflects a sense of obligation on employees to remain in the organization while continuing commitment is due to employees' assessment that leaving the organization causes greater costs than benefits. Previous research shows that all types of commitment have a major impact on the performance of organizations. As the number of empirical studies on this topic in Serbia is limited, this paper examines the impact of each type of organizational commitment on the productivity of organizations in Serbia, as well as the impact of the overall commitment. The aim of the paper was to identify the types of organizational commitment that have the greatest impact on the performance of organizations in Serbia and to propose measures to HR managers whose implementation can improve the operations of domestic organizations. The starting point of the paper was that employees' commitment has a statistically significant effect on organizational performance. To verify the validity of this assumption, a primary survey was conducted. 169 employees of 17 organizations in Serbia were surveyed. By applying correlation and regression analysis methods, it has been proven that overall organizational commitment, as well as its certain types, have a positive impact on the productivity of organizations in Serbia, with the impact of normative commitment on performance greater than other types of commitment. At the same time, the contribution of continuous commitment to the performance of organizations in Serbia is negligible (statistically insignificant). In line with the obtained results, recommendations for human resources managers in Serbia are proposed. The theoretical implications of the paper are reflected in filling the gap in the national literature in the field of organizational commitment, while the empirical implications are reflected in the mechanisms and measures that are proposed at the end of the paper.
Holistic Small-Signal Modeling and AI-Assisted Region-Based Stability Analysis of Autonomous AC and DC Microgrids

In this paper, a holistic small-signal model of hybrid AC and DC microgrids is developed, including AC subsection, DC subsection, and interface inverters between AC and DC buses. Based on the derived complete small-signal model, a region-based stability analysis approach is proposed and developed. Meanwhile, to obtain the steady-state operating points used in the regionbased stability analysis, practical and effective power flow calculation is conducted for droop-controlled hybrid AC and DC microgrids. Rather than following a conventional point-by-point stability evaluation procedure, the stability region implemented in this work is derived based on the selected cross-domain parameters from either control systems or main power circuits. Furthermore, an artificial intelligence (AI) assisted Kernel Ridge Regression (KRR) algorithm is implemented to derive the stability boundary with enhanced computational efficiency. Simulation tests are presented to demonstrate the effectiveness of the proposed method.
PUBLIC SERVICE PERSPECTIVES Clinics-in-Common: Creating Uncommon Professionals

Most professional educational programs develop specialized experts who view problems from the perspectives of their own disciplines. Yet, our most pressing social problems are rarely limited to a single disciplinary approach. Instead of providing future professionals with the tools necessary for solving problems that cross disciplines, many professional training programs inadvertently stifle multidisciplinary communication and collaboration by isolating students from students in other disciplines. Practitioners are trained to manage societal problems rather than to solve them within varied communities. This article describes an innovative multidisciplinary clinic model based at the University of Hawai‘i’s William S. Richardson School of Law that focuses specifically on the needs of children who have entered or are at risk of entering the foster care system. It also discusses a medical–legal partnership within a community health center based on principles learned and experiences gained from this unique model.
SCAN-Toolbox: Structural COBRA Add-oN (SCAN) for Analysing Large Metabolic Networks

Background: Modelling and analysis of metabolic networks can be carried out with different levels of granularity, ranging from structural (topological) analysis to constraint-based methods to kinetic modelling. The increasing number of genome-scale metabolic reconstructions and the growing importance of metabolic modelling intensify the need for integrated approaches allowing the application of different analysis methods. Objective: There is no well-organized and freely accessible code package or specific toolbox/plugin available for the reconstruction of metaboliteand enzyme-centric networks. So, we have developed a toolbox which could be able to perform such reconstructions. Method: We have developed the SCAN-toolbox, an Add-on to the widely used COBRA framework for constraint-based modelling. SCAN is an open-source MATLAB/Octave toolbox which extends the COBRA framework and provides functions to prepare different metaboliteand enzyme-centric networks that could also be imported in other structural analysis softwares, such as Cytoscape. Results: We have applied the SCAN-toolbox to four metabolic networks to demonstrate its applicability. Conclusion: SCAN extends the COBRA framework towards structural analysis of large metabolic networks, including the reconstruction of undirected and directed metaboliteas well as enzyme-centric networks. A R T I C L E H I S T O R Y Received: August 08, 2016 Revised: October 10, 2016 Accepted: November 09, 2016 DOI: 10.2174/15748936116661611231237 29
ADOPTING SCOUTING AND HEURISTICS TO IMPROVE THE AI PERFORMANCE IN STARCRAFT

StarCraft is one of the most famous Real-Time Strategy (RTS) games, and there have been several competitions based on AI bots. In this game, the player often gathers information about their enemy based on a scouting task and from that recognizes and prepares a good strategy to counter the enemy plan. However, these tasks are considered difficult to implement with AI bots. In this paper, heuristic methods are proposed to recognize enemy plans and calculate reasonable attack timing decision. We also propose an effective method of scouting by adopting a potential field. These approaches were applied to a StarCraft AI bot, this bot has won the first place in mixed division of Student StarCraft AI Tournament 2012(SSCAI), the result is also discussed in this paper
Prognostic ability of inflammation-based markers in radioembolization for hepatocellular carcinoma

Aim: Inflammation-based markers, such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), have recently been used as prognostic indicators in hepatocellular carcinoma (HCC). We aimed to determine whether NLR and PLR may predict response to yttrium-90 transarterial radioembolization (TARE) as primary treatment for HCC. Methods: We performed a retrospective review of a prospectively collected database of HCC cases (1994–2019) and selected patients who received TARE as primary treatment (n = 42). Laboratory studies were used to calculate NLR and PLR. Response to TARE was determined using the modified response evaluation criteria in solid tumors (mRECIST). Patients were classified as non-responders (stable or progressive disease) or responders (partial or complete response) to treatment based on mRECIST. Results: Receiver operating characteristic curves identified a pre-treatment NLR cutoff of ≥ 2.83 and a pre-treatment PLR cutoff of ≥ 83 for predicting non-response to treatment. Pre-treatment NLR ≥ 2.83 was the only significant predictor of non-response to TARE in multivariate logistic regression analysis (odds ratio 7.83, P = 0.036). On time to progression analysis, both pre-treatment NLR ≥ 2.83 and pre-treatment PLR ≥ 83 were associated with a higher proportion of tumor progression at 6 months post-treatment (43.6% vs. 10.0%, P = 0.014, log-rank) and (38.6% vs. 0%, P = 0.010, log-rank), respectively. Conclusion: NLR confers prognostic value and may be superior to PLR in determining response to TARE as primary treatment for HCC. Future studies are necessary to validate these findings in a larger cohort.
Dual-Ring-Assisted Mach–Zehnder Interferometers

In this paper , we report a nanosecond 16 × 16 silicon electro-optic switch chip based on a Benes architecture. The switch adopts dual-ring-assisted Mach–Zehnder interferometers as the basic building blocks. In each switch element, both TiN microheaters and PIN diodes are integrated for ring resonance alignment and high-speed switching, respectively. A transfermatrix-based theoretical model is established to analyze the switch performances. The 16 × 16 switch is characterized by measuring the optical transmission spectra and quadrature phase-shift keying (QPSK) data transmission through 16 representative optical paths. The insertion loss of the entire switch chip is 10.6 ± 1.7 dB and the crosstalk is less than −20.5 dB. The 32-Gb/s QPSK signal is successfully switched to different destination ports by reconfiguring the optical paths, verifying the signal integrity after switching.
Simple and Scalable Sparse k-means Clustering via Feature Ranking

Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of features are relevant in distinguishing clusters. This has motivated the development of sparse clustering techniques that typically rely on k-means within outer algorithms of high computational complexity. Current techniques also require careful tuning of shrinkage parameters, further limiting their scalability. In this paper, we propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms. We show that our algorithm enjoys consistency and convergence guarantees. Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings. We showcase these contributions thoroughly via simulated experiments and real data benchmarks, including a case study on protein expression in trisomic mice.