Miracle-dz/UniFAR-FTUs huggingface dataset:
Answer to Referee #1

Major Comment nr.1: The NPZD model is very simple which is not a problem by itself. It performs enough well to be suitable for that study. This is quite clearly shown in the validation section of the paper. However, according to me, it lacks a critical reservoir especially concerning the objectives of that study: DOC or more precisely semi-labile and semi-refractory DOC. Concentrations of semi-labile DOC range from typically 20 to 40 umol/L in the upper ocean (Hansell et al., 2009; Hansell and Carlson, 2014). Its lifetime is also quite long and ranges from weeks to years which makes it possible for that pool to be transported far away from its production region. It has been shown to potentially play an important role in the subtropical gyres (e.g., Roussenov et al., 2006; Torres-Valdés et al., 2009). In the present study, this pool is omitted and thus, a potentially large contribution to the lateral export of organic carbon is not represented. This needs at least to be discussed in the discussion section.
Generics and Exceptions : A Reply to eohen ( 2004 )

It is well known that exceptions are allowed in generics. Concerning this widely-agreed-upon phenomenon of exceptions in generics, Cohen (2004) argues that exceptions are allowed only if "homogeneity" is not violated. That is, the exceptions should not form a salient "chunk" of the domain of the generic. For this, he proposes two ways of mapping of cognitive mental representations, namely, "tree" and "geometric" representations. It will be argued in this paper, however, that choices between these two mental representations claimed to be involved in the interpretation process of generics are quite arbitrary, and that counterexamples also exist for the "homogeneity" requirement. Given this, the main purpose of this paper will be to discuss the problems of Cohen's theory. Its other purpose will be to try to delve into the fundamental issue of the meaning of generics, and to suggest that generics, similar to metaphors, involve cognitive conceptualizations based on the language users' encyclopedic knowledge, world knowledge from experiences, common sense, beliefs, stereotypes, prejudices, etc.
Papers Inventories in Dynamic General Equilibrium

This article investigates a dynamic general equilibrium model with a stockout constraint, which means that no seller can sell more than the inventories that she has. The model successfully explains two inventory facts; (i) inventory investment is procyclical, and (ii) production is more volatile than sales. The key intuition is that, since inventories and demand are complements in generating sales, the optimal level of inventories is increasing in expected demand. Thus, when demand is expected to be strong, rms increase their production not only to meet their demand but also to accumulate inventories. Also, our model shows that the inventory to sales ratio is persistent and countercyclical, while the (endogenous) markup is countercyclical. These are because a high interest rate in booms discourages rms to hold inventories.
Performance Analysis of Spectrum Sensing Techniques in Nakagami and Rice Fading Channels

This paper aims at investigating the performance of four eigenvalue-based techniques for centralized da t -fusion cooperative spectrum sensing in cognitive radio net works over flat Nakagami-m and Rice fading channels. The detection techniques are the generalized likelihood ratio tes t (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum eigenvalue detection (MED), and the energy detection (ED). In the case of Nakagamim, arbitrary fading and phase parameters were assumed, and so was with the Rice p arameter in the case of the Rician model. Index Terms — cognitive radio, cooperative eigenvalue spectrum sensing, Nakagami, Rice.
Multi-category MIDI music generation based on LSTM Generative adversarial network

Music generation by neural networks has become a central issue since deep neural networks demonstrated their ability in learning from big data collections. This paper proposes a music score generation model which employs multi-layer RNNs and GAN scheme. First of all, the midi sequences are passed to the model, which is parsed as tone lengths, frequencies, intensities, and timing, and then the music theory law is introduced, while the initial sequences are set as music chords. Consequently, the distribution of music is learned in the process of training. The experimental results show that it is a feasible network structure which can generate multi-category music with good hearing experience.
A methodology to appraise the quality of case studies in corporate tax avoidance

Developing countries’ governments are struggling to increase their tax revenues, as multinational enterprises are still engaged in tax avoidance. Mediatized case studies conducted by NGOs aim to give voice to these marginalized players in the international tax debate, yet they have been criticized for lacking quality. This paper has developed, in a participatory way, a methodology to assess the quality of tax avoidance case studies. This method provides avenues for NGOs to demonstrate expertise that needs to be included in the debate on international tax avoidance. As the second step, 14 case studies involving Dutch corporate entities have been analysed using these quality standard guidelines. The lack of adequate data affects the quality of the case studies negatively. Greater transparency from companies and governments is necessary to address this issue. In addition, the outcomes suggest that NGOs can increase the quality of their case studies by improving their technical expertise and their own accountability. The first indications imply that, since the method has been developed and applied, researchers have started to take the quality standards into consideration.
Consumer Inflation Uncertainty and the Macroeconomy : Evidence from a New Micro-Level Measure

This paper introduces a micro-level measure of consumer inflation uncertainty. Literature on cognition and communication documents that people use round numbers as a communicative tool to convey uncertainty. I construct an uncertainty measure that exploits consumers’ tendency to round their inflation forecasts to multiples of five on the Michigan Survey of Consumers. I document cross-sectional and time series properties of the measure and provide support for its validity. Mean inflation uncertainty is countercyclical and positively correlated with inflation disagreement, inflation volatility, and the Economic Policy Uncertainty Index. Inflation uncertainty varies more in the cross section than over time, so a major benefit of this new measure is its cross-sectional dimension which enables micro-level analysis of the relationship between uncertainty and consumption. More uncertain consumers are more reluctant to spend on durables, cars, and homes, and their spending attitudes are less sensitive to interest rates. The measure also has applications to inflation dynamics and monetary policy. For example, the expectations of more-certain consumers can be used to improve Phillips curve estimation. JEL codes: D800, D830, D840, E200, E210, E310
Prevalence and load of airway bacteria in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is defined by irreversible airflow limitation, usually caused by exposure to noxious particles or gases. COPD patients suffer from chronic daily symptoms, and may occasionally suffer acute exacerbations – episodes in which there is a worsening of symptoms above day-today levels. Exacerbation aetiology is variable and controversial, although infection and air pollution are believed to play a part. Certain bacterial pathogens, known as typical airway bacteria, are found with high prevalence in individuals at exacerbation. The burden of other airway bacteria is currently ill-defined. This study initially compared microbiological culture with quantitative PCR for detection of three commonly identified airway bacteria in COPD, Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis (typical airway bacteria). Quantitative PCR was utilised to establish prevalence and load of these species at stable, exacerbation and exacerbation recovery states. Typical airway bacterial prevalence and load was assessed against a range of clinical factors in COPD. Additional quantitative PCRs examined the prevalence and load of atypical airway bacteria (Chlamydophila pneumoniae, Legionella pneumophila, Mycoplasma pneumoniae) and the entire bacterial microflora of the lungs. My original contribution to knowledge is that higher load of typical airway bacteria is associated with higher levels of systemic inflammation and airflow limitation in both stable and exacerbated COPD. For the first time it has also been demonstrated that airway microbiome load is not associated with airflow limitation or systemic inflammation changes, providing evidence that typical airway bacteria in particular are contributing to disease severity. Atypical airway bacteria prevalence in COPD was negligible. The investigations in this thesis highlight the need for rapid antibiotic therapy in exacerbations where typical airway bacteria presence is suspected or confirmed. Furthermore, prophylactic antibiotic therapy should be considered for stable COPD patients with confirmed typical airway bacterial presence, as a means of reducing inflammation and airflow limitation.
A molecular dynamics investigation of the kinetic bottlenecks of the hPin 1 WW domain . II : simulations with the Go model

The present paper is the second of a series in which we perform molecular dynamics simulations on the WW domain of Pin1 protein. The aim of the work is the reconstruction of the folding/unfolding pathway with a special interest for the kinetic bottlenecks. In the first paper of the series we showed that unfolding simulations using the Sorenson/Head-Gordon (SHG) model correctly identify the kinetic bottlenecks in agreement with the experimental data. In the present paper we repeat the same simulation protocol using the Go model. The simulations show that the unfolding mechanisms reconstructed by the two models are consistent with each other, but the Go process is much less cooperative and thus less accurate in the identification of the kinetic bottlenecks. The poor performance of the Go model in the case of Pin1 WW domain, can be related to the absence of angular potentials, which makes the protein conformations more flexible: such an effect is presumably amplified by the small size and by the particular shape of the protein. Key-Words: WW domains, Pin1 protein , kinetic bottlenecks, Go model, Sorenson/Head-Gordon model, Molecular Dynamics
UTILIZING A HYDROXYAPATITE-BINDING DOMAIN TO ANCHOR BONE REGENERATIVE PEPTIDES TO HYDROXYAPATITE-CONTAINING BIOMATERIALS by

Hydroxyapatite (HA) biomaterials are widely used for bone regenerative purposes due to the osteoconductive properties of HA. However, strategies for improving HA performance by functionalizing surfaces with bioactive factors are limited. In this study, we explored the use of an HA-binding domain (heptaglutamate, “E7”) to facilitate ionic coupling of the collagen mimetic peptide, DGEA, to HA-containing materials, which included solid HA disks and electrospun polycaprolactone matrices incorporating nanoparticulate HA. We found that the E7 domain directed more peptide to the surface of HA and enhanced peptide retention on both materials in vitro. Moreover, peptides were retained in vivo for at least two months, highlighting the potential of this mechanism as a sustained delivery system for bioactive peptides. Most importantly, E7DGEA-coupled HA, as compared with DGEA-HA, enhanced the adhesion and osteoblastic differentiation of mesenchymal stem cells, and also increased new bone formation and direct bone-implant contact on HA disks implanted into rat tibiae. Collectively, these results support the use of E7-DGEA peptides to enhance osteogenesis on HA substrates, and further suggest that the E7 domain can serve as a universal tool for anchoring a wide variety of bone regenerative molecules to any type of HA-containing material.
Bank Supervision and Information Content in Bank Stock Price : Global Evidence

Banks are inherently more opaque than non-bank firms, but government regulation mitigates this information asymmetry problem (Flannery et al., 2004). This paper examines the informational role of bank regulators in 34 countries. Using bank stock return synchronicity (R 2 statistics of the expanded market model) as a proxy for the amount of bank-specific information that is impounded into stock prices, we find that the traditional approach to bank supervision, which directly discipline banks by taking specific actions against bank management, bank owners, and bank auditors, mainly help generate market-wide information. Our results suggest that bank regulators should carefully exert their supervisory powers because those have significant market-wide influence. Additionally, we find that a supervisory strategy that forces banks to disclose accurate information and induces the private sector monitoring tends to help generate more bank-specific information. This relationship is more pronounced in countries with sound legal institutions. This study’s results help shed light on the debate on the effectiveness of mandated information disclosure by government regulation in improving the banking sector’s transparency.
GRADIENT FLOWS ON A SPACE OF POWER-FUNCTIONS FOR MINIMIZING A CONVEX FUNCTIONAL

We study a class of nonlinear dynamical systems to develop eÆcient algorithms. As an eÆcient algorithm, interior point method based on Newton's method is well known for solving convex programming problems which include linear, quadratic, semide nite, lp-programming problems [2] [3]. On the other hand, the geodesic of information geometry [1] is represented by a continuous Newton's method for minimizing a convex functional called divergence. Thus, we discuss a relation between information geometry and convex programming in a related family of continuous Newton's method. In particular, as the optimization of parameter values, we consider the -projection problem from a given data onto an information geometric submanifold spanned with power-functions such as a weighted lp-norm. First, we present there exists a structural similarity between the -projection and semide nite programming problems [4]. The geometric structure is based on the autoparallelisms [1] or linear property in the function space over nite discrete variables or the space of positive de nite matrices, respectively. The property is used to derive an approximation algorithm as predictor-corrector method for solving the problem through the geodesic. The maximum step-length is determined by geometric quantities with respect to the Riemannian metric and the dual-connection. We also show that the proposed method is e ective in simulation. Next, we reformulate the -projection problem into a form of convex programming as a lpprogramming and the related ones. From the reformulated problems, we derive self-concordant barrier functions [2] [3] according to the real values of . Thus, it means the existence of a polynomial time interior-point algorithm for our problem. Furthermore, we present the coincidence with the gradient directions on the geodesic for the divergence and on the aÆne-scaling trajectory for a modi ed barrier function. These results connect part of nonlinear and algorithmic analyses with the discreteness of variables.
TensorFlow at Scale MPI , RDMA and All That

Deep learning has proven to be a successful tool for solving a large variety of problems in various scientific fields and beyond. In recent years, the models as well as the available datasets have grown bigger and more complicated and thus an increasing amount of computing resources is required in order to train these models in a reasonable amount of time. Besides being able to use HPC resources, deep learning model developers want flexible frameworks which allow for rapid prototyping. One of the most important of these frameworks is Google TensorFlow, which provides both features, i.e. good performance as well as flexibility. In this paper we discuss different solutions for scaling the TensorFlow Framework to thousands of nodes on contemporary Cray XC supercomputing systems.
CS 490 project : Securing the Integrity of Voting Machine Data Modules

While there are many works that address the user interface layer of voting machines, very few seem to address the lower-level layers such as the data module integrity. This paper is a step-by-step attempt to remedy that situation by exploring for a suitable definition of “secure” for a data module, and devising a way to implement such a module.
NEW ROBUST TESTS FOR THE DETECTION OF ARCH EFFECT

ARCH model has become very popular because it enables the econometricians to estimate the variance of a series at a particular point of time. Although a number of tests are available in the literature, there is evidence that they fail to detect ARCH effect especially when outliers are present in the data. In this article, we proposed two new tests for the detection of ARCH effect of innovation based on simple Chi-square ( 2 χ ) and Student’s -t tests and call them the Arch TestChi-square based (Arch 2 χ ) and Arch TestStudent’s –t based (Arch Student’s –t), respectively. The performance of the proposed tests is examined by real life data as well as Monte Carlo simulations.
A Wireless Traffic Taxonomy for Evaluating MAC Protocol Performance

This article makes a taxonomic study of wireless traffic in the link, network and transport layer. Also, the traffic is classified using a self-similar model based on frame components. In a simulation environment with different traffic loads, we made an analysis of application performance when comparing different network traffic loads with high and low levels of management frames at the link layer. The results show a performance improvement in real-time applications when specific management traffic components are eliminated since they have relevant contribution in the degree of self-similarity of the traffic. This result may help in the development of adaptive protocols for the link layer.
Laugh machine

: The Laugh Machine project aims at endowing virtual agents with the capability to laugh naturally, at the right moment and with the correct intensity, when interacting with human participants. In this report we present the technical development and evaluation of such an agent in one specific scenario: watching TV along with a participant. The agent must be able to react to both, the video and the participant’s behaviour. A full processing chain has been implemented, inte- grating components to sense the human behaviours, decide when and how to laugh and, finally, synthesize audiovisual laughter animations. The system was evaluated in its capability to enhance the affective experience of naive participants, with the help of pre and post-experiment questionnaires. Three interaction conditions have been compared: laughter-enabled or not, reacting to the participant’s behaviour or not. Preliminary results (the number of experiments is currently to small to obtain statistically significant differences) show that the interactive, laughter-enabled agent is positively perceived and is increasing the emotional dimension of the experiment. ´Ecole Abstract —The Laugh Machine project aims at endowing virtual agents with the capability to laugh naturally, at the right moment and with the correct intensity, when interacting with human participants. In this report we present the technical development and evaluation of such an agent in one speciﬁc scenario: watching TV along with a participant. The agent must be able to react to both, the video and the participant’s behaviour. A full processing chain has been implemented, inte-grating components to sense the human behaviours, decide when and how to laugh and, ﬁnally, synthesize audiovisual laughter animations. The system was evaluated in its capability to enhance the affective experience of naive participants, with the help of pre and post-experiment questionnaires. Three interaction conditions have been compared: laughter-enabled or not, reacting to the participant’s behaviour or not. Preliminary results (the number of experiments is currently to small to obtain statistically signiﬁcant differences) show that the interactive, laughter-enabled agent is positively perceived and is increasing the emotional dimension of the experiment. If an interesting event is detected it is classiﬁed and fused with previous events and those of other modalities. The ﬁnal decision can be shared through the network with external components. In order to train the recognition components a logging mechanism is incorporated in order to capture processed signals and add manual annotation. In an ofﬂine learning step the recognition components can now be tuned to improve accuracy.
The Practice of Defensive Medicine by doctors in primary health care in the Kingdom of Bahrain

The Practice of Defensive Medicine by doctors in primary health care in the Kingdom of Bahrain Dr.Batool Hasan, M.D, BSc, ABFM, Dr.Hanan Abdulrahim, M.D, BSc, ABFM, Dr. Muna AlMukhtar, M.D, BSc,, ABFM, Dr. Rahma AlAsfoor, M.D, BSc, ABFM, Dr. Mohamed Mandeel, MBBS, AUB, M.MED.SCI CONS. FAMILY PHYSICIAN, RESEARCH SUPERVISOR Family medicine residency program, Primary CareMinistry of Health, Kingdom of Bahrain
Dynamic spatial models , including spatial time series 1

This book has already presented many situations where the observation process under study contains the temporal dimension as well as the spatial dimension. This section is devoted to detail a popular and fairly general framework for handling these situations. It is based on firmly established models, called dynamic or state-space models, with a non-parametric flavor. They have proved a flexible tool to handle temporal correlation (West and Harrison, 1997) in a variety of different contexts. This section will also provide a number of contexts where these models can be applied in the context of spatial analysis. Dynamic models are described via a p-dimensional latent process β(·) defined over time according to a temporal difference equation
Lenient Frequency Adjusted Q-learning

Overcoming convergence to suboptimal solutions in coopera tiv multi-agent games has been a main challenge in reinforcement learning. The concept of “leniency” has been proposed to be more forgiving for initial mis-coordination. It has been shown theoretically that an arbitrarily high certainty of convergence to the global optimum can be achieved by increasing the degree o f l niency, but the relation of the evolutionary game theoretic model to the Lenient Q-learning algorith m relied on the simplifying assumption that all actions would be updated simultaneously. Building on insig hts from Frequency Adjusted Q-learning, this article introduces the variation Lenient Frequency Adjust ed Q-learning that matches the theoretical model precisely, and allows for arbitrarily high convergence to P areto optimal equilibria in cooperative games.