Miracle-dz/UniFAR-FTUs huggingface dataset:
From AI to Computational Social Choice New Techniques, new Paradigms, new Applications

Résumé Computational social choice is an interdisciplinary ﬁeld at the crossing point of economics, AI, and more generally computer science. It consists in designing, analysing and computing mechanisms for making collective decisions, with various application subﬁelds such as voting, fair allocation of resources, participatory budgeting, selecting representative groups of individuals, or matching with preferences. Since the mid-2000s, The AI community has played a major role in the (computational) social choice research output. Not only it has lead to developing algorithms for collective decision making, but it has also helped reshaping and revitalising the ﬁeld, by identifying new paradigms, new problems, new objects of study and applications . I give below a (non-exhaustive) series of examples showing the role of AI in the study of collective decision making. I also give some thoughts about what should come next. This paper goes with my IJCAI-22 invited talk. It is written mostly for people outside the computational social choice community.
Wavelet methods for time series and spatial data

This thesis considers the application of wavelet methods to the analysis of time series and spatial data. In the rst part, we propose a locally stationary model of the covariance structure for data which lie on a regular grid. This is achieved by moving from a (global) Fourier decomposition of structure to a localised decomposition involving a set of discrete, non-decimated wavelets. The proposed model is subsequently applied to various texture analysis problems. These range from the classi cation of images taken from the standard Brodatz texture collection to subtle discrimination problems encountered by an industrial collaborator. The second part proposes an e cient construction of the inner product matrix of discrete autocorrelation wavelets | a quantity which is of crucial importance in the unbiased estimation of local wavelet spectra. The proposed scheme relates neighbouring elements of the matrix which lie on a given diagonal using a two-scale relationship of the autocorrelation wavelets. This results in a construction which is considerably more e cient than the brute force approach used to date. Finally, we conclude by detailing the results of initial research on the estimation of the local autocovariance structure of locally stationary time series. These results provide an interesting interpretation of this quantity in terms of familiar (stationary) time series measures.
Image Description Dataset for Language Learners

We focus on image description and a corresponding assessment system for language learners. To achieve automatic assessment of image description, we construct a novel dataset, the Language Learner Image Description (LLID) dataset, which consists of images, their descriptions, and assessment annotations. Then, we propose a novel task of automatic error correction for image description, and we develop a baseline model that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. Our experimental results show that the developed model can revise errors that cannot be revised without an image.
Development and Evaluation of a Publish/Subscribe IoT Data Sharing Model with LoRaWAN

Publish/subscribe architectures are becoming very common for many IoT environments such as power grid, manufacturing and factory automation. In these architectures, many different communication standards and middleware can be supported to ensure interoperability. One of the widely used publish/subscribe protocol is MQTT where a broker acts among publishers and subscribers to relay data on certain topics. While MQTT can be easily setup on cloud environments to perform research experiments, its large-scale and quick deployment for IoT environments with a widely used wireless MAC layer protocol such as LoRaWAN has not been thoroughly tested. Therefore, in this paper we develop and present a simulation framework in NS-3 to offer MQTT-based on publish/subscribe architecture that can also support LoRaWAN communication standard. To this end, we utilize NS-3’s LoRaWAN library and integrate it with a broker that connects to other types of publishers/subscribers. We enable unicast capability from the broker to LoRaWAN end-devices while supporting multiple topics at the broker. We tested several scenarios under this IoT architecture to demonstrate its feasibility while assessing the performance at scale.
Modelling Finite Populations

It is common to model the dynamics of an evolutionary algorithm by considering a typical or representative population. We consider modelling a genetic algorithm using two different descriptions of this typical population. The first using cumulants describing the distribution of fitnesses, the second using the distribution itself. Empirically the cumulant description is found to give a much more accurate description of the evolution despite the fact that it gives a cruder description of the typical distribution. The reason for the success of the cumulant approach is because cumulants are self-averaging quantities. The meaning of this statement in this context is explored. The full distribution description is exact in the limit of an infinite population. It is shown that even in simple problems this limit is only approached for very large populations. Furthermore, the infinite population limit can give misleading predictions for the observable long time behaviour of an evolutionary algorithm.
Error analysis of analytical coarse alignment formulations for stationary SINS

This paper presents a comprehensive error analysis of coarse alignment formulations for stationary strapdown inertial navigation systems (SINS). Analytical expressions for the residual normality, orthogonality, alignment, and Euler angle errors are systematically derived, allowing us to establish comparisons in terms of rapidity, accuracy, and autonomy requirements. For the purpose of the error analysis, geographic latitude and local gravity acceleration uncertainties are considered, in addition to the inertial sensor uncertainties. As the main contribution of this paper, an improved coarse alignment method is proposed, which is based on orthonormality constraints existing between the Euler angles and the attitude matrix. In contrast to the traditional methods, the proposed formulation does not imply normality and orthogonality errors, besides producing time-independent alignment and Euler angle errors. The latter is seen to be particularly interesting for situations wherein, due to time constraints, posterior fine alignment and orthonormalization procedures cannot be implemented. The superiority of the proposed formulation is validated through simulated and experimental tests, regardless of the SINS initial orientation. The accuracy degradation produced by each alignment formulation in the navigation stage is used as the performance index.
The natural rate of interest: estimates for the euro area

The natural rate of interest is the theoretical real rate at which inflation neither rises nor falls. Determined independently of monetary policy, it is a benchmark to assess whether policy is accommodative or restrictive. It is unobservable but estimated to be negative in the euro area over recent years. With modest inflation expectations and the effective lower bound on nominal interest rates, conventional monetary policy struggled to raise inflation, explaining the ECB’s recourse to non-conventional instruments.
Exact and Approximation Algorithms for Computing Reversal Distances in Genome Rearrangement

Genome rearrangement is a research area capturing wide attention in molecular biology. The reversal distance problem is one of the most widely studied models of genome rearrangements in inferring the evolutionary relationship between two genomes at chromosome level. The problem of estimating reversal distance between two genomes is modeled as sorting by reversals. While the problem of sorting signed permutations can have polynomial time solutions, the problem of sorting unsigned permutations has been proven to be NP-hard [4]. This work introduces an exact greedy algorithm for sorting by reversals focusing on unsigned permutations. An improved method of producing cycle decompositions for a 3/2-approximation algorithm and the consideration of 3-cycles for reversal sequences are also presented in this paper.
Soft Matter Physics: An Introduction

CHAPTER 1 Condensed Matter: General Characters, The Chemical Bond, Particle Interactions. / CHAPTER 2 Atomic and Molecular Arrangements. / CHAPTER 3 The Order Parameter: Amplitude and Phase. / CHAPTER 4 Phase Transitions. / CHAPTER 5 Elasticity of Mesomorphic Phases. / CHAPTER 6 Dynamics of Isotropic and Anisotropic Fluids. / CHAPTER 7 Fractals and Growth Phenomena. / CHAPTER 8 Dislocations in Solids. Plastic Relaxation. / CHAPTER 9 Dislocations in Smectic and Columnar Phases. / CHAPTER 10 Curvature Defects in Smectics and Columnar Phases. / CHAPTER 11 Disclinations and Topological Point Defects. Fluid Relaxation. / CHAPTER 12 Topological Theory of Defects. / CHAPTER 13 Surface Phenomena. / CHAPTER 14 Stability of Colloidal Systems. / CHAPTER 15 Polymers: Structural Properties.
Identification of Multiword Expressions in Tweets for Hate Speech Detection

Multiword expression (MWE) identification in tweets is a complex task due to the complex linguistic nature of MWEs combined with the non-standard language use in social networks. MWE features were shown to be helpful for hate speech detection (HSD). In this article, we present joint experiments on these two related tasks on English Twitter data: first we focus on the MWE identification task, and then we observe the influence of MWE-based features on the HSD task. For MWE identification, we compare the performance of two systems: lexicon-based and deep neural networks-based (DNN). We experimentally evaluate seven configurations of a state-of-the-art DNN system based on recurrent networks using pre-trained contextual embeddings from BERT. The DNN-based system outperforms the lexicon-based one thanks to its superior generalisation power, yielding much better recall. For the HSD task, we propose a new DNN architecture for incorporating MWE features. We confirm that MWE features are helpful for the HSD task. Moreover, the proposed DNN architecture beats previous MWE-based HSD systems by 0.4 to 1.1 F-measure points on average on four Twitter HSD corpora.
Turmeric Rhizome’s Colour, Size and Edge based Approach for Identification of Variety

Digital Image Processing (DIP) based identification method helps in reducing the manual inspection and provides the greater accuracy with the fast processing. Turmeric rhizomes identification based on some important morphological features extracted from the images is carried out with image processing methods. Different types of turmeric exhibit diverse physical and morphological properties. This paper presents an approach where the turmeric rhizomes are identified and classified based on its features such as colour, shape and size. Experimental analysis was conducted with few species such as Nizamabad, Salem, Erode and Rajapuri. The external features-based identification in the proposed methodology is simple and an efficient in identifying the species.
Construction of equilibrium distributions from simulation of nonequilibrium processes with feedback control

The fluctuation theorems of stochastic thermodynamics have previously been used to derive a reweighting scheme that constructs an equilibrium distribution from realizations of a driven nonequilibrium process. This has been applied as a simulation strategy to calculate equilibrium averages in models where the equilibrium distribution is difficult to simulate directly. Fluctuation theorems have also been generalized to encompass a feedback controller acting on the system. In this thesis, these results are combined to extend the reweighting scheme to the presence of feedback, which widens the range of potential use cases for the method. In the statistical weights, this introduces an information theoretic term involving a virtual “reverse” feedback mechanism not part of the simulation. This becomes a degree of freedom that may be chosen to optimize the convergence of the reweighting scheme. This is done analytically to yield an expression for the information term, which may however become difficult to estimate accurately from simulation data. The reweighting scheme is then applied to a simple Brownian particle in a 1D potential, both in the absence of feedback, and for two special cases of feedback for which the information term is simple to calculate. The reweighting is reliable for the model without feedback, but not for the two cases of feedback considered. The data indicate that these feedback choices are suboptimal in different respects. This suggests that for the extended method to work reliably, both the simulated and reverse feedback mechanisms must be chosen close to optimally.
Estimation of aquifer parameters under transient and steady-state conditions: 2

Paper 2 of this three-part series starts with a discussion of the question, Under what conditions is the aquifer inverse problem well-posed? After defining the terms uniqueness, identifiability, and stability, theoretical considerations and synthetic examples are used to demonstrate that ill-posedness can be mitigated by including prior information about the parameters in the estimation criterion to be minimized. At the same time, the inclusion of such information is shown to be insufficient to guarantee uniqueness and stability in all cases. Several test problems in the recent literature, which have resulted in pessimistic conclusions about the solvability of the aquifer inverse problem, are shown to be ill-posed; a question is thus raised about the validity of these conclusions in the general case. Various conjugate gradient algorithms, coupled with the adjoint state finite element method for computing the gradient of the estimation criterion, and with Newton's method for determining the optimum step size downgradient, are compared. A marked improvement in the rate at which these algorithms converge is shown to be achieved by switching from one method to another when the former slows down or fails to converge.
UDC 616

Diabetes is a significant social problem, since it leads both to disability and high mortality rate. The experiment involved 32 sexually mature nonlinear males of white rats. Experimental groups of animals were administered a single streptozotocin dose of 70 mg/kg intraperitoneally and NADP – 30 mg/kg. The animals in the experimental groups were slaughtered and studied on the 11, 21 and 31 days after streptozotocin administration. The studies have shown that NADP affects the metabolism, reduces the production of acids and, as a result, reduces the excretion of acids by the kidneys in experimental animals.
Unravelling the Myths of Enterprise Risk Management (ERM) and Shareholders’ Wealth

: urpose: The primary objective of this study is to explore the extent to which ERM implementation is capable of enhancing shareholders’ wealth. Design/Methodology: A sample of 283 companies operating in the Main Market of Bursa Malaysia was selected. This study utilized the definition of ERM as the independent variable and shareholders’ wealth as dependent variable. Findings: The overall observation revealed that ERM was significant in explaining the variation in shareholders’ wealth. Objective setting, event identification, risk response, control activities, information and communication, and monitoring substantiated the hypothesized relationship. Originality/Value: The research framework of this study, which utilized assumptions from stewardship and agency theory, could serve as guidance for future research on organizational control and governance. The study fills the gap in literature which mostly concentrated on the USA and western countries. Secondly, this study applied a ratio-based survey in line with COSO’s ERM framework. Managerial Implication: The CRO and the board should focus more advising functions at the earlier stage of the ERM implementation and monitoring functions once ERM matured.
Study on the Modulation of MDA and Lipid Profile Status of Some Women during Menstrual Cycle

Study was carried out to evaluate levels of Malondialdehyde and lipid profile parameters of some women in different phases of menstrual cycle. All subjects were premenopausal within childbearing age between 18 years to 50 years who had normal 28 days cycle. Blood samples were collected at follicular, ovulatory and luteal phase and their lipid profile and Malondialdehyde levels were determined spectrophotometrically. Applying analysis of variance with P value at (P<0.05) cholesterol and low-density lipoprotein showed significant difference at follicular and luteal phases. Marked variation was observed in all three phases for malondialdehyde with a peak at ovulatory phase. Pattern of result could be used to explain the interplay of lipids and hormonal behavior in menstruation.
How Worker Productivity and Wages Grow with Tenure and Experience: The Firm Perspective

How worker productivity evolves with tenure and experience is central to economics, shaping, for example, life-cycle earnings and the losses from involuntary job separation. Yet, worker-level productivity is hard to identify from observational data. This paper introduces direct measurement of worker productivity in a firm survey designed to separate the role of on-the-job tenure from total experience in determining productivity growth. A key innovation is to elicit what managers know about the productivity of their workers. Several findings emerge concerning the initial period on the job. (1) On-the-job productivity growth exceeds wage growth, consistent with wages not being allocative period-by-period. (2) Previous experience is a substitute, but a far less than perfect one, for on-the-job tenure. (3) There is substantial heterogeneity across jobs in the extent to which previous experience substitutes for tenure. The survey makes use of administrative data to construct a representative sample of firms, check for selective non-response, validate survey measures with administrative measures, and calibrate parameters not measured in the survey.
Prevention Mechanism on DDOS Attacks by using Multilevel Filtering of Distributed Firewalls

— In the past decade, it has been found that DDoS has proved to be the most dangerous attack. IP spoofing is one of the kinds of DDoS attack which is emerging as a big threat in today’s world of technology. The Proposed Framework is a unique technique composed of distributed firewalls and hop count based filtering, it can be used to prevent such kind of DDoS attack. At The primary level, distributed firewalls filters the IP addresses which can be either Internet or Intranet. Along with this it also provide various other advantages such as reducing the dependency of network topology. At secondary level, hop count and TTL based filtering provides more secure level of filtration. In this paper, we have proposed a new framework which reduces the limitations
Comparison of diﬀerent methods of nitrogen-vacancy layer formation in diamond for wideﬁeld quantum microscopy

Thin layers of near-surface nitrogen-vacancy (NV) defects in diamond substrates are the workhorse of NV-based wideﬁeld magnetic microscopy, which has applications in physics, geology and biology. Several methods exist to create such NV layers, which generally involve incorporating nitrogen atoms (N) and vacancies (V) into the diamond through growth and/or irradiation. While there have been detailed studies of individual methods, a direct side-by-side experimental comparison of the resulting magnetic sensitivities is still missing. Here we characterise, at room and cryogenic temperatures, ≈ 100 nm thick NV layers fabricated via three diﬀerent methods: 1) low-energy carbon irradiation of N-rich high-pressure high-temperature (HPHT) diamond, 2) carbon irradiation of δ doped chemical vapour deposition (CVD) diamond, 3) low-energy N + or CN − implantation into N-free CVD diamond. Despite signiﬁcant variability within each method, we ﬁnd that the best HPHT samples yield similar magnetic sensitivities (within a factor 2 on average) to our δ -doped samples, of < 2 µ T Hz − 1 / 2 for DC magnetic ﬁelds and < 100 nT Hz − 1 / 2 for AC ﬁelds (for a 400 nm × 400 nm pixel), while the N + and CN − implanted samples exhibit an inferior sensitivity by a factor 2-5, at both room and low temperature. We also examine the crystal lattice strain caused by the respective methods and discuss the implications this has for wideﬁeld NV imaging. The pros and cons of each method, and potential future improvements, are discussed. This study highlights that low-energy irradiation of HPHT diamond, despite its relative simplicity and low cost, is a competitive method to create thin NV layers for wideﬁeld magnetic imaging.
Causal Effects of Early Career Sorting on Labor and Marriage Market Choices: A Foundation for Gender Disparities and Norms

We study whether and how early labor market choices causally determine longer run career versus family outcomes differentially for men and women. We analyze the physician labor market by exploiting a randomized lottery that determines the sorting of Danish physicians into internships, i.e., their initial labor market positions, where students with worse lottery numbers end up assigned to less desirable local labor markets and entry-level jobs. Using administrative data spanning ten years after physicians’ graduations, we find causal effects of early career sorting on a range of life cycle outcomes that cascade from labor market choices, including human capital accumulation and occupational choice, to marriage market choices, including matching and fertility. Notably, the persistent effects are entirely driven by women, whereas men experience only temporary career disruptions from unfavorable early-stage sorting. Investigating sources of this gender divergence, differential baseline preferences over markets or specialties are an unlikely explanation. Instead, evidence points to differential search and mobility in response to the treatment, and to operating roles for employer-side factors, specifically mentorship at the workplace. Our findings have implications for policies aiming at gender equality in outcomes, as they reveal how persistent gaps can arise even in institutionally gender-neutral settings with early-stage equality of opportunity. * We thank Julie Cullen, Gordon Dahl, Claudia Goldin, Larry Katz, Camille Landais, Karthik Muralidharan, Claudia Olivetti, Tom Vogl, and seminar participants at UCSD, University of Copenhagen, Berlin Applied Micro Seminar, USC Center for Economic and Social Research, McGill University/Boston College Labor Seminar, Harvard Medical School/Health Care Policy, and the NBER Summer Institute Gender in the Economy Meeting for helpful comments and discussions. We thank the Danish Health Authority, the De Regionale Råd for Lægers Videreuddannelse, and Dansk Telemedicin A/S for data access and helpful institutional knowledge. Fadlon: University of California, San Diego, Department of Economics, and NBER, 9500 Gilman Drive #0508, La Jolla, CA 92093-0508, fadlon@ucsd.edu; Lyngse: University of Copenhagen, Department of Economics, and Center for Economic Behavior and Inequality (CEBI), fpl@econ.ku. dk; Nielsen: University of Copenhagen, Department of Economics, and CEBI, thn@econ.ku. dk. We gratefully acknowledge funding from the Danish Independent Research Fund (grant no. 9061-00035B). The activities of CEBI are financed by the Danish National Research Foundation.