Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to target advertisements at

The goal of this thesis work is to develop a methodology to optimize advertisement targeting inside the Spotify platform. By understanding the relevance of advertisements to users, the advertisement efficacy can be improved leading to an overall reduced advertisement load, to a better user experience, and to an increased advertisement revenue. Three types of advertisement are delivered inside the Spotify platform: house advertisements, which promote the Spotify premium subscription, label advertisements, which promote artists and albums and commercial advertisements, which promote external brands or products. For each type, machine learning models were implemented to optimize the matching of advertisements to users. The house advertisement targeting focused on maximizing the conversions of users from free to premium subscriptions. Unlike traditional advertisement targeting models, which focus on estimating the user probability of converting, the proposed approach focuses on estimating the change in probability of converting when an advertisement is delivered, so that only impressions producing a true uplift are delivered. The targeting of label advertisements optimized the number of users starting to listen to the advertised music. The implemented approach delivers relevant advertisements based on an evaluation of the affinity between the user’s music and the advertised music and on the user’s listening behavior. The commercial advertisement targeting optimized the number of clicks on advertisements. Given that commercial advertisements advertise external products, the main challenge faced was the lack of relevant data to inform the targeting. The implemented approach tries to deal with this problem by combining feature based methods with collaborative filtering methods. The main contribution of this thesis work is the implementation of machine learning models to improve advertisement targeting inside the Spotify platform. In particular, the proposed methodology uses uplift modeling, with a modified approach to handle bias in the training data, and also makes use of meta-data to better understand the context of the campaigns.
AN OUTBREAK OF PASTEURELLOSIS IN JAPANESE QUAIL CHICKS ( COTURNIX COTURNIX JAPONICA )

Avian pasteurellosis is a contagious disease of domestic and wild birds caused by Pasteurella multocida. A private broiler quail farm (n=1100) with a mortality of 330 eight day old quail chicks without clinical signs were presented to the Department of Veterinary Pathology, Veterinary College and Research Institute, Orathanadu, Tamilnadu, India. On complete post-mortem examination, visceral organs showed generalised congestion and consolidation of lung. Histopathologically, liver showed severe congestion, multifocal necrotic hepatitis with heterophilic infiltrations. Lungs revealed bronchopneumonia. Cytological examination of lung impression showed bipolar organisms. Microbiological examination with heart swab, lung and liver tissue revealed Pasteurella sp. The bacteria were confirmed by PCR assay with KMT1 gene as Pasteurella multocida. Based on history, gross, cytology, microbiological and histopathological lesions the case was diagnosed as avian pasteurellosis.
Explanation of CNN Mechanism Using Traditional Facial Affective Recognition

With the development of deep learning, the structure of convolution neural network is becoming more and more complex and the performance of object recognition is getting better. However, the classification mechanism of convolution neural networks is still an unsolved core problem. The main problem is that convolution neural networks have too many parameters, which makes it difficult to analyze them. In this paper, we design and train a convolution neural network based on the expression recognition, and explore the classification mechanism of the network. By using the Deconvolution visualization method, the extremum point of the convolution neural network is projected back to the pixel space of the original image, and we qualitatively verify that the trained expression recognition convolution neural network forms a detector for the specific facial action unit. At the same time, we design the distance function to measure the distance between the presence of facial feature unit and the maximal value of the response on the feature map of convolution neural network. The greater the distance, the more sensitive the feature map is to the facial feature unit. By comparing the maximum distance of all facial feature elements in the feature graph, the mapping relationship between facial feature element and convolution neural network feature map is determined. Therefore, we have verified that the convolution neural network has formed a detector for the facial Action unit in the training process to realize the expression recognition.
Adversary lower bounds for the collision and the set equality problems

In theory, quantum query complexity is surprisingly well-understood. For each function, the (negative-weight) adversary method [1] gives a tight characterisation of its quantum query complexity [2]. The adversary bound is a semi-definite optimisation problem (SDP). It performs nicely under composition [2], and it was used to prove a strong direct product theorem [3]. There is no similar theory known for randomised query complexity. In practice, however, it is quite hard to construct a feasible solution to the adversary bound SDP that would be close to the optimal. In many cases, the positive-weight version of the bound is used. It is the original version of the bound developed by Ambainis [4]. This version uses combinatorial reasoning, and it is easy to apply. Unfortunately, the positive-weight adversary is subject to some severe constraints like the certificate complexity barrier [5, 6] and the property testing barrier [1]. The latter one, for instance, states that, if each positive and each negative input differ in at least ε fraction of the input variables, no positive-weight adversary can prove a lower bound better than Ω(1/ε). The proper negative-weight adversary method usually requires spectral analysis of the adversary matrix. It does not have many applications so far. It was used to prove tight lower bounds for formulae evaluation [7]. Also, a recent line of development [8, 9] resulted in a relatively general construction of adversary bounds superseding the certificate complexity
Retraction for RSC Advances : Broadband cavity enhanced absorption spectroscopy ( BBCEAS ) as a detection technique for a microfluidic flow cell made of Zeonor ®

4 Broadband cavity enhanced absorption spectroscopy (BBCEAS) has so far been used for 5 increasing the effective optical path length and hence detection sensitivity of flow cells made 6 of high-optical quality glass. Glass, however, is fragile, expensive and difficult to process. In 7 this work, the feasibility of BBCEAS for high sensitivity detection in a microfluidic flow cell 8 made of a cyclic olefin copolymer, Zeonor has been demonstrated for the first time. The 9 Zeonor device is disposable and is suitable for applications in areas such as clinical 10 diagnostics, where glass devices would be unsuitable. The effective number of passes of light 11 through the Zeonor device was increased by two orders of magnitude on placing it between 12 two 99±0.3% reflectivity broadband (400-800 nm) mirrors. At 572 nm, the minimum 13 detectable absorption coefficient (i.e. absorbance per unit length) achieved using the Zeonor 14 device was 2.9×10 cm, which corresponds to a limit of detection of 1.1 μM potassium 15 permanganate solution. This is two orders of magnitude better than the typical LOD achieved 16 by performing single pass absorbance measurements in microfluidic devices. 17 18
Dynamic Factor Demands for Aggregate Southeastern United States Agriculture

Abstract A four equation input demand system for aggregate Southeastern United States agriculture consistent with dynamic optimizing behavior is specified and estimated. Labor and materials are considered as variable inputs while land and capital are treated as quasi-fixed inputs. It is found that the adjustment rates for capital and land differ considerably and are interdependent. Further, the data appear consistent with the existence of an aggregate production technology and the hypothesized optimizing behavior.
Fit perspectives and theory building in information systems

Our study examines the fit concept in the context of IS planning, a major research stream in the IS literature. Previous research reveals inconsistencies in the IS planning literature that may be due to a lack of congruence between the conceptual and statistical forms of fit employed within and between studies. We systematically examined the IS planning literature between 2000 and 2006 for congruence between the conceptual and statistical forms of fit. We found that 17% of studies lacked congruence and the most problematic area was with the bivariate representations of fit. We provide an empirical comparison of bivariate formulations and a decision tree to determine appropriate forms of fit
RATIONAL HOMOLOGY COBORDISMS FORMS

THE INTERPLAY between a 3-manifold and the 4-manifolds that it bounds is an important aspect of many problems in low-dimensional topology. For instance, given a 3-manifold M with the rational homology of a sphere, one would like to know if it is the boundary of a 4manifold W with the rational homology of a ball. This question is central to investigations of knot cobordism [6], and embeddings of 3-manifolds [14]. If M has a cyclic covering space, classified by a homomorphism cp: H,(M)-+Z,, then the work of Atiyah-Singer defines an invariant o(M, cp) in terms of the intersection form of any Cmanifold which M bounds and over which the covering extends. Our main theorem states (with some technical hypotheses) that whenever M bounds a rational homology ball W, then for appropriately chosen covers, the invariant a(M, cp) must be f 1. This theorem has its roots in the work of Casson-Gordon [6], who proved the same result under the hypotheses that W be a 2-fold branched cover of a slice disk in B4, and that the covering space have prime-power degree. The restriction to prime-powers was essential to their method, which was based on a homological analysis (related to “Smith-theory”) of the covering space p of W. Recently, Fintushel and Stern [IO] have used gauge theory to prove that if W is a smooth 4-manifold whose boundary is a spherical space-form (i.e. S3 modulo a free linear group action), then the Casson-Gordon results hold without the restriction on the order of the covering. The point of our theorem is that for smooth 4-manifolds neither the hypothesis about prime-powers nor the restriction to spherical space-forms is needed. In contrast, in high dimensions, conditions on the primepower invariants often provide complete criteria for odd-dimensional manifolds to be homology cobordant [S]. The situation for topological 4-manifolds is less clear and is discussed at the end of the paper. Our theorem applies to a 3-manifold M whose cover fi is a rational homology sphere. By analogy with the linear case, one might call such manifolds “rational space forms”. Note that a rational space form is also a rational homology sphere. A rational cobordism of rational homology spheres {M:} is a 4-manifold W with the rational homology of a punctured 4-sphere. The invariants we use may be defined in many different ways [4,3, 14, 61. The most convenient definition for our purposes is the following. Associated to a representation v of H,(M) into Z,, there is a complex representation ‘pc in U(1). An easy cobordism theory argument shows that some number, say r, copies of (M, cp) is the
Learning Spherical Convolution for Fast Features from 360 ° Imagery

Fig. 1 shows how the proposed spherical convolutional network differs from an ordinary convolutional 10 neural network (CNN). In a CNN, each kernel convolves over the entire 2D map to generate a 2D 11 output. Alternatively, it can be considered as a neural network with a tied weight constraint, where 12 the weights are shared across all rows and columns. In contrast, spherical convolution only ties the 13 weights along each row. It learns a kernel for each row, and the kernel only convolves along the row 14 to generate 1D output. Also, the kernel size may differ at different rows and layers, and it expands 15 near the top and bottom of the image. 16
Magnetosheath Propagation Time of Solar Wind Directional Discontinuities

Observed delays in the ground response to solar wind directional discontinuities have been explained as the result of larger than expected magnetosheath propagation times. Recently, Samsonov et al. (2017, https://doi.org/10.1002/2017GL075020) showed that the typical time for a southward interplanetary magnetic field (IMF) turning to propagate across the magnetosheath is 14 min. Here by using a combination of magnetohydrodynamic simulations, spacecraft observations, and analytic calculations, we study the dependence of the propagation time on solar wind parameters and near‐magnetopause cutoff speed. Increases in the solar wind speed result in greater magnetosheath plasma flow velocities, decreases in the magnetosheath thickness and, as a result, decreases in the propagation time. Increases in the IMF strength result in increases in the magnetosheath thickness and increases in the propagation time. Both magnetohydrodynamic simulations and observations suggest that propagation times are slightly smaller for northward IMF turnings. Magnetosheath flow deceleration must be taken into account when predicting the arrival times of solar wind structures at the dayside magnetopause.
Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation

Core-Stateless Fair Queueing (CSFQ) is a scalable algorithm proposed more than two decades ago to achieve fair queueing without keeping per-ﬂow state in the network. Unfortunately, CSFQ did not take off, in part because it required protocol changes (i.e., adding new ﬁelds to the packet header), and hardware support to process packets at line rate. In this paper, we argue that two emerging trends are making CSFQ relevant again: ( i ) cloud computing which makes it feasible to change the protocol within the same datacenter or across datacenters owned by the same provider, and ( ii ) programmable switches which can implement sophisticated packet processing at line rate. To this end, we present the ﬁrst realization of CSFQ using programmable switches. In addition, we generalize CSFQ to a multi-level hierarchy, which naturally captures the trafﬁc in today’s datacenters, e.g., tenants at the ﬁrst level and ﬂows of each tenant at the second level of the hierarchy. We call this scheduler Hierarchical Core-Stateless Fair Queueing (HCSFQ), and show that it is able to accurately approximate hierarchical fair queueing. HCSFQ is highly scalable: it uses just a single FIFO queue, does not perform per-packet scheduling, and only needs to maintain state for the interior nodes of the hierarchy. We present analytical results to prove the lower bounds of HCSFQ. Our testbed experiments and large-scale simulations show that CSFQ and HCSFQ can provide fair bandwidth allocation and ensure isolation.
Supplemental Material for : Sound propagation in a uniform superfluid two-dimensional Bose gas

We consider a cloud in a rectangular box of size Lx × Ly. We create an excitation of density at frequency ω at one end of the rectangle and we choose this excitation to be invariant along the x-direction. We decompose this excitation on density modes whose gradient vanishes on the edges of the box and which are invariant along the x-direction. We define these spatial modes by S j(x, y) = √ 2/(LxLy) cos(k jy/2), where k j = 2 jπ/Ly and j ∈ N∗. In the linear regime, the response of the system is expected to occur at the same frequency ω, and each mode S j will be excited with an amplitude B j(ω) and a phase shift φ j(ω) with respect to the excitation. Therefore, the density profile at time t will be given by
The Data Assimilation Research Testbed: A Community Facility

Abstract The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DART's ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a model's state a.
High Performance Direct Torque Control of Electrical Aerodynamics Load Simulator using Fractional Calculus

Electrical load simulator system (ELSS) is a test rig used to apply medium range aerodynamics loads on flight actuation system in real time experiments. A novel high performance fractional order adaptive robust torque control law is proposed for Electrical Load Simulator which is subjected to extra torque disturbance, friction, and parametric uncertainties. Adaptive fuzzy system is used to estimate extra torque disturbance and parametric uncertainty is estimated using discontinuous projection based adaptive control. A friction observer is used to compensate nonlinear friction. Stability of closed loop is derived using Lyapunov method . The proposed method ensures transient performance of ELSS system subjected to none zero initial conditions. Frequency testing and extra torque elimination tests are performed using PID, integer order sliding mode control and the proposed controller. The efficiency of proposed controller is verified using extensive numerical simulations.
Resting heart rate and the risk of cardiovascular disease , total cancer , and all-cause mortality e A systematic review and dose e response meta-analysis of prospective studies

Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway Department of Epidemiology and Public Health, Imperial College, London, UK c Bjørknes University College, Oslo, Norway Department of Radiology, Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, USA Department of Internal Medicine, Section of Geriatrics, Yale School of Medicine, Adler Geriatric Center, New Haven, USA f Department of Preventive Cardiology, Oslo University Hospital Ullevål, Oslo, Norway
What matters in Design of Corporate Law

For the corporate business model to be successful, it is important to align the interests of those who control and finance the firm. Corporate law has here an important task to fulfill. It offers a legal framework that can facilitate for parties to conclude mutually preferable agreements at low transaction costs. The purpose of the paper is to show how to design corporate law to fulfill this task. A two-dimension model that simultaneously considers both regulation intensity and the level of default of the corporate law is presented. Earlier literature treats these dimensions separately. By adding a transaction cost perspective to our model, we assess different regulatory techniques and examine how legislation can help corporations by offering a standard contract that lowers transaction costs of contracting. This can be achieved through a legislation that covers most contingencies and take the heterogeneity of firms into consideration. Furthermore, default rules or standards of opt-out character should be combined with other regulatory techniques with lower transaction costs such as opt-in alternatives and menus.
Know When to Hold ' Em : Shu ing Deterministically in a

Nonconcatenative constraints, such as the shuue relation , are frequently employed in grammatical analyses of languages that have more exible ordering of constituents than English. We show how it is possible to avoid searching the large space of permutations that results from a nondeterministic application of shuue constraints. The results of our implementation demonstrate that deterministic application of shuue constraints yields a dramatic improvement in the overall performance of a head-corner parser for German using an HPSG-style grammar.
Indu tion vs. Logisti

Tree indu tion and logisti regression are two standard, o(cid:11){the{shelf methods for building models for lassi(cid:12) ation. We present a large{s ale experimental omparison of logisti regression and tree indu tion, assessing lassi(cid:12) ation a - ura y and the quality of rankings based on lass-membership probabilities. We use a learning{ urve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (1) Contrary to prior observations, logisti regression does not generally outperform tree indu tion. (2) More spe i(cid:12) ally, and not surprisingly, logisti regression is better for smaller training sets and tree indu tion for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning urves ross), so on lusions about indu tion-algorithm superiority on a given domain must be based on an analysis of the learning urves. (3) Contrary to onventional wisdom, tree indu tion is e(cid:11)e tive at pro-du ing probability{based rankings, although apparently omparatively less so for a given training{set size than at making lassi(cid:12) ations. Finally, (4) the domains on whi h tree indu tion and logisti regression are ultimately preferable an be hara terized surprisingly well by a simple measure of signal-to-noise ratio. (cid:3) Perli h, C. , F. Provost, and J. Simono(cid:11). \Tree Indu tion vs. Logisti Regression: A Learning-urve Analysis. " CeDER
Security Requirements Engineering : A Framework for Representation and Analysis Journal Item

This paper presents a framework for security requirements elicitation and analysis. The framework is based on constructing a context for the system, representing security requirements as constraints, and developing satisfaction arguments for the security requirements. The system context is described using a problem-oriented notation, then is validated against the security requirements through construction of a satisfaction argument. The satisfaction argument consists of two parts: a formal argument that the system can meet its security requirements and a structured informal argument supporting the assumptions expressed in the formal argument. The construction of the satisfaction argument may fail, revealing either that the security requirement cannot be satisfied in the context or that the context does not contain sufficient information to develop the argument. In this case, designers and architects are asked to provide additional design information to resolve the problems. We evaluate the framework by applying it to a security requirements analysis within an air traffic control technology evaluation project.
Computation of the Most Probable Motion from FuzzyCorrespondencesMoshe

An algorithm is presented for nding the most probable image motion between two images from fuzzy point correspondences. In fuzzy correspondence a point in one image is assigned to a region in the other image. Such a region can be line (aperture eeect) or a probability matrix. Noise and outliers are always present, and points may come from diierent motions. The presented algorithm, which uses linear programming , recovers the motion parameters and performs outlier rejection and motion-segmentation at the same time. The linear program computes the global optimum without an initial guess.