Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Chance Constrained Programs over Wasserstein Balls

In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set of the potential data-generating distributions and subsequently hedging against all member distributions within this ambiguity set, data-driven optimization effectively combats the ambiguity with which real-life data sets are plagued. Chen et al. (2022) study data-driven, chance-constrained programs in which a decision has to be feasible with high probability under every distribution within a Wasserstein ball centered at the empirical distribution. The authors show that the problem admits an exact deterministic reformulation as a mixed-integer conic program and demonstrate (in numerical experiments) that the reformulation compares favorably to several state-of-the-art data-driven optimization schemes.
Natural language inference

Inference has been a central topic in artificial intelligence from the start, but while automatic methods for formal deduction have advanced tremendously, comparatively little progress has been made on the problem of natural language inference (NLI), that is, determining whether a natural language hypothesis h can justifiably be inferred from a natural language premise p. The challenges of NLI are quite different from those encountered in formal deduction: the emphasis is on informal reasoning, lexical semantic knowledge, and variability of linguistic expression. This dissertation explores a range of approaches to NLI, beginning with methods which are robust but approximate, and proceeding to progressively more precise approaches. We first develop a baseline system based on overlap between bags of words. Despite its extreme simplicity, this model achieves surprisingly good results on a standard NLI evaluation, the PASCAL RTE Challenge. However, its effectiveness is limited by its failure to represent semantic structure. To remedy this lack, we next introduce the Stanford RTE system, which uses typed dependency trees as a proxy for semantic structure, and seeks a low-cost alignment between trees for p and h, using a cost model which incorporates both lexical and structural matching costs. This system is typical of a category of approaches to NLI based on approximate graph matching. We argue, however, that such methods work best when the entailment decision is based, not merely on the degree of alignment, but also on global features of the aligned 〈p, h〉 pair motivated by semantic theory. Seeking still greater precision, we devote the largest part of the dissertation to developing an approach to NLI based on a model of natural logic. We greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. Our system decomposes an inference problem into a sequence of atomic edits which transforms p into h; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. Finally, we address the problem of alignment for NLI, by developing a model of phrase-based alignment inspired by analogous work in machine translation, including an alignment scoring function, inference algorithms for finding good alignments, and training algorithms for choosing feature weights.
An experimental study of fluid dynamics of stall delay on the blade of a horizontal axis wind turbine

—Fluid dynamics of the stall delay phenomenon is studied experimentally in this work on a horizontal-axis wind turbine (HAWT) blade. The 3D-printed blade is produced from a downscaled model of a 5kW HAWT. Its rotational motion in the test section of wind tunnel is precisely controlled by a programmable motor. Both mean static pressures and dynamic pressures are measured on the suction surface at the spanwise location of 0.25R of the blade at two different global tip speed ratios (TSR) of 3 and 5. The TSR of 3 corresponds to a large angel of attack (AOA) at 30 degrees where the flow is fully separated. The other TSR of 5 corresponds to a smaller AOA of 15 degrees. Planar time-resolved particle image velocimetry measurements at 0.25R for the above two TSRs were performed to study the dynamics of the separated flows when the stall is delayed on the rotating blade. The Reynolds numbers at these two TSRs, based on the half chord length of the blade and its relative velocity, are 8683 and 6695, respectively. Finally, the possible correlation between the dynamics of pressure and the time evolution of shed vortices is analyzed during stall delay.
A modified abstraction of Sierpiński fractals towards enhanced sensitivity of a cross-coupled bow-tie nanostructure

We experimentally demonstrate a modified abstraction of a fractal geometry (up to order M = 2), namely the Sierpiński fractal, with intrinsic self-similarity for a multitude of infrared sensing applications. The modification particularly strengthens the dipolar resonance and enables optical magnetism at longer wavelengths on a relatively miniaturized footprint. In contrast to the conventional resonant sensing, we harness the broadband electric field enhancement of the modified fractal patterns originating from the lightning rod effect in the non-resonant regime. We demonstrate strong enhancement of molecular absorption at mid-IR by the fractal patterns in the non-resonant regime even under extreme thermal broadening. Finally, we extend the work towards the functional study of the molecular fingerprint of ultra-thin film (∼5 nm) on a non-complementary metamaterial platform in the non-resonant regime. With the help of the solid state chemical dewetting of the monolayer, we also successfully demonstrate a new type of cross-coupling mediated sensitivity of the multispectral and mutually coupled fractal patterns. The research clearly indicates the usefulness of broadband electric field enhancement by the second order fractal pattern for on chip, complete profiling of mid-IR fingerprints of biological elements, i.e. cell, and protein monolayer on a limited footprint and under versatile morphological states.
A Generic Deep Architecture for Single Image Reﬂection Removal and Image Smoothing ( Supplementary Material )

• Section 2: Detailed explanation and analysis of our synthetic data generation method for training CEILNet in the reflection removal task. • Section 3: Comparison of an I-CNN network with varying convolutional layers against the full CEILNet pipeline. • Section 4: Visualization of more edge maps predicted by E-CNN for both the reflection removal and image smoothing tasks. • Section 5: More visual comparisons with existing deep image smoothing networks. • Section 6: More visual comparisons with our baselines and existing methods on the reflection removal problem. • Section 7: More visual results of our CEILNet on both synthetic and real reflection images. For the latter, our network is the only approach capable of reliably separating a series of difficult single real-world images into reflection and background layers without strong additional priors. • Section 8: More visual results of our CEILNet for different smoothing filters.
DOCUMENTOS DE ECONOMÍA Y FINANZAS INTERNACIONALES Working Papers on International Economics and Finance

The aim of this paper is to investigate the dynamics of the exporting activity of manufacturing firms that are involved in complex internationalization strategies. We consider complex internationalization to be when firms are simultaneously active in exporting, importing intermediates and international production, which are typically associated with participation in GVCs. Our descriptive data show that these triple mode internationalized firms belong to an elite group of firms that exhibit a higher level of labour productivity, are larger and show a higher likelihood of engaging in product innovation. On the basis of the estimation of a random-effects probit model with panel data, we find that once such firm characteristics are controlled for, internationalization complexity plays an important role in continuing to export. Additionally, the results from a dynamic panel data model show that being involved in more sophisticated internationalization modes positively influences the level of exports. Thus, it seems that firms active in a complex mix of internationalization strategies have an added advantage which enables them to confront the uncertainty of foreign markets in better conditions and translates to a lower likelihood of ceasing exporting and to higher export values. We go one step further and investigate whether the impact is different during the trade collapse in 2009 and the following recovery. JEL codes: F14, F60.
Methods for Specular Color Component Accelerate Calculation

The new approaching for Phong shading method speed increasing is presented in given paper. According to the first approach for those parts of image were highlights is presented is used the Blinn lightning model and for the rest part the simple Lambert model is applied. Using this method the quality of scene stay the same when we use the complicated Blinn model and the scene creation speed is razed significantly. For Phong shading method is proposed the approach accordingly to which the normal for the current pixel in triangle can be calculated using the normals to the two privies pixels. Given approaches can significantly decrease the amount of calculation and thus increase the shading speed.
The Gauss-Newton algorithm applied to track-while-scan radar

The Gauss-Newton (GN) algorithm is the minimum-variance non-recursive estimation procedure invented by Gauss in 1809 [3, 4, 7]. Mathematicians refer to it by that name; statisticians refer to it as nonlinear regression; astronomers call it differential correction. In 1959 Swerling reworked Gauss' non-recursive algorithm into a recursive format [9], giving rise to the Bayes-Swerling filter. In 1960/61, Kalman and Bucy published their algorithm [5, 6], which, in the absence of process noise, can be derived from Swerling's recursive format [7]. The huge advances in computing power and affordability of RAM since the early 60's have made it desirable that we re-examine Gauss' original algorithm which avoids the problems and/or limitations incurred by either the Swerling or the Kalman recursive formats, and at the same time opens up tremendous flexibility in terms of access to internal filter values. This paper examines the GN algorithm and how it has been applied to Track-While-Scan (TWS) radar. A companion paper in these proceedings discusses the application of GN to Passive Coherent Location (PCL) radar [8]. (5 pages)
Dynamic optical saturation microscopy

A new concept of fluorescence microscopy is presented allowing the breaking of the diffraction limit of optical microscopy by a factor of ca. five. It relies on measuring the temporal evolution of fluorescence after sudden switch-on of the light excitation. The observed temporal dynamics of the fluorescence signal can be converted into information about the spatial distribution of fluorophores within the exciting laser focus. The proposed scheme is technically simple and versatile, and allows resolution enhancement in all three dimensions.
Transport Optimal pour le Traitement d'Images

Optimal Transport is a well developed mathematical theory that defines robust metrics between probability distributions. The computation of optimal displacements between densities through the associated transport map makes this theory mainstream in several applicative fields. For image processing applications, the transport map can for instance be used to compute geodesics between images or to transfer characteristics of one image to another. In this context, it is of major interest to preserve the nature of the observed objects, in order to synthesize images that are physically and visually plausible. In this document, generalized Optimal Transport distances including relaxation and regularization are considered to improve the modeling of image processing problems. New models and algorithms within the continuous and discrete formulations of Optimal Transport are presented. With the continuous setting, the integration of physical regularization of the transport plan makes possible the interpolation of ocean images containing complex structures. In the discrete setting, the regularization of the transport plan is considered for color transfer between images. Convex and non-convex models are proposed to define automatic methods that adapt the proportion of colors required to synthesize visually plausible images. These methods are extended to the computation of barycenters to deal with the color normalization of multiple images. Finally, the entropy regularization of discrete Optimal Transport is used for image segmentation. A fast and convex model is designed to segment images, while respecting global color distribution constraints.
Educational Design Research

I argue that DBR is feasible to conduct doctoral research as a micro-cycle of design to develop design methodology and/or domain theory . I provide a rationale for choosing DBR as an underpinning methodology through which I designed the study and selected the data collection and analysis methods. I also describe how DBR was interrelated with the tenets of my study and the research questions. Providing an explanation of the relationship between DBR and participatory design, I explain how design methodology was developed in the context of my study. At the end, I briefly outline the findings and the contextual design principles that emerged from the findings
Shifting Higher Vocational Education Teachers' Response Toward Inward Affective Involvement in Ethical Dilemmas: Perspectives on the Design of Affective Learning Experiences to Inform Students’ Attitude Toward an Ethical Dilemma

Higher vocational education teachers often encounter students who are inclined to view ethical dilemmas with an inner distance. If teachers’ input comes solely from the cognitive rather than the affective component, the interaction about the ethical dilemma between teacher and students will never progress beyond that level of inner distance. In our qualitative study, a total of 31 higher vocational education teachers from 6 educational programs were placed in an experiential position by presenting them with an ethical dilemma. This study aims to inform the analysis and exploration of the problem of our overall design-based research project. By asking them about this dilemma in teams, they were stimulated to respond to the ethical dilemma and to view it affectively. The results show that most teachers kept their distance from the ethical dilemma. Only when their fundamental beliefs were being challenged they seemed to assume some degree of inward affective involvement. This study contributes to theoretical and practical knowledge about stimulating inward affective involvement with ethical dilemmas so that students can develop a conscious value-expressive attitude.
Turbulent flows over sparse canopies

Turbulent flows over sparse and dense canopies exerting a similar drag force on the flow are investigated using Direct Numerical Simulations. The dense canopies are modelled using a homogeneous drag force, while for the sparse canopy, the geometry of the canopy elements is represented. It is found that on using the friction velocity based on the local shear at each height, the streamwise velocity fluctuations and the Reynolds stress within the sparse canopy are similar to those from a comparable smooth-wall case. In addition, when scaled with the local friction velocity, the intensity of the off-wall peak in the streamwise vorticity for sparse canopies also recovers a value similar to a smooth-wall. This indicates that the sparse canopy does not significantly disturb the near-wall turbulence cycle, but causes its rescaling to an intensity consistent with a lower friction velocity within the canopy. In comparison, the dense canopy is found to have a higher damping effect on the turbulent fluctuations. For the case of the sparse canopy, a peak in the spectral energy density of the wall-normal velocity, and Reynolds stress is observed, which may indicate the formation of Kelvin-Helmholtz-like instabilities. It is also found that a sparse canopy is better modelled by a homogeneous drag applied on the mean flow alone, and not the turbulent fluctuations.
The Co-Terminal Swap Market Model with Bergomi Stochastic Volatility

In this article, we apply the forward variance modeling approach by L.Bergomi to the co-terminal swap market model. We build an interest rate model for which all the market price changes of hedging instruments, interest rate swaps and European swaptions, are interpreted as the state variable variations, and no diffusion parameter calibration procedure is required. The model provides quite simple profit and loss (PnL) formula, with which we can easily understand where a material PnL trend comes from when it appears, and consider how we should modify the model parameters. The model has high flexibility to control the model dynamics because parameter calibration is unnecessary and the model parameters can be used solely for the purpose of the model dynamics control. With the model, the position management of the exotic interest rate products, e.g. Bermudan swaptions, can be carried out in a more sophisticated and systematic manner. A numerical experiment is performed to show the effectiveness of the approach for a Canary swaption, which is a special form of a Bermudan swaption.
HashKV: Enabling Efficient Updates in KV Storage via Hashing

Persistent key-value (KV) stores mostly build on the Log-Structured Merge (LSM) tree for high write performance, yet the LSM-tree suffers from the inherently high I/O amplification. KV separation mitigates I/O amplification by storing only keys in the LSM-tree and values in separate storage. However, the current KV separation design remains inefficient under update-intensive workloads due to its high garbage collection (GC) overhead in value storage. We propose HashKV, which aims for high update performance atop KV separation under updateintensive workloads. HashKV uses hash-based data grouping, which deterministically maps values to storage space so as to make both updates and GC efficient. We further relax the restriction of such deterministic mappings via simple but useful design extensions. We compare HashKV with state-of-the-art KV stores via extensive testbed experiments, and show that HashKV achieves 4.6× throughput and 53.4% less write traffic compared to the current KV separation design.
O ct 2 01 7 Event-Radar : Real-time Local Event Detection System for GeoTagged Tweet Streams

The local event detection is to use people’s posting messages with geotags on social networks to reveal the related ongoing events and their locations [1]. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from large geo-tagged tweet streams in real time remains challenging. A robust and efficient cloud-based realtime local event detection software system would benefit various aspects in the real-life society, from shopping recommendation for customer service providers to disaster alarming for emergency departments. We use the preliminary research GeoBurst [1] as a starting point, which proposed a novel method to detect local events. GeoBurst+ [2] leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract related tweets to form candidate events. It further summarises the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. We mainly implement a website demonstration system Event-Radar with an improved algorithm to show the real-time local events online for public interests. Better still, as the query window shifts, our method can update the event list with little time cost, thus achieving continuous monitoring of the stream.
Improved Techniques For Weakly-Supervised Object Localization

We propose an improved technique for weakly-supervised object localization. Conventional methods have a limitation that they focus only on most discriminative parts of the target objects. The recent study addressed this issue and resolved this limitation by augmenting the training data for less discriminative parts. To this end, we employ an effective data augmentation for improving the accuracy of the object localization. In addition, we introduce improved learning techniques by optimizing Convolutional Neural Networks (CNN) based on the state-of-the-art model. Based on extensive experiments, we evaluate the effectiveness of the proposed approach both qualitatively and quantitatively. Especially, we observe that our method improves the Top-1 localization accuracy by 21.4 - 37.3% depending on configurations, compared to the current state-of-the-art technique of the weakly-supervised object localization.
From convergence principles to stability and optimality conditions

We show in a rather general setting that Hoelder and Lipschitz stability properties ofsolutions to variational problems can be characterized by convergence of more or less abstract iteration schemes. Depending on the principle of convergence, new and intrinsic stability conditions can be derived. Our most abstract models are (multi-) functions on complete metric spaces. The relevance of this approach is illustrated by deriving bothclassical and new results on existence and optimality conditions, stability of feasible and solution sets and convergence behavior ofsolution procedures.
Learning Performing Data Flow Bidding Machine y : true user response Bid Strategy User Response Prediction Bid

Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.