Miracle-dz/UniFAR-FTUs huggingface dataset:
Master Thesis Dark Patterns

People who shop online deal with dark patterns regularly, among which are also deceptive countdown timers. Deceptive countdown timers indicate a limited-time offer or deal that is not truly limited-time, as the deal does not expire when the timer reaches zero. Previous research has studied the taxonomies and prevalence of such dark patterns, but only a handful of studies have investigated the effects of dark patterns. This study investigates the effects of deceptive countdown timers on consumers’ buying behavior and experience. We compared the reaction of participants that encountered no special offers, only a discount, and a discount accompanied by a deceptive countdown timer in an online experiment (N = 245). Our results show that deceptive countdown timers, like discounts, can affect product choice, but also induce a variety of responses ranging from participants mentioning they do not mind countdown timers to participants that deem them manipulative, deceptive, and feel negatively impacted by them. Furthermore, our results also suggest that the deceptive countdown timer can diminish the incentive of consumers to choose a product with a discount making the consumer skeptical and averse to offers with (deceptive) countdown timers. Future research should investigate the effects of other dark patterns, as our experimental setup is suitable for re-use. Also, future work could focus on the diminishing effects dark patterns have and explore different ways to protect consumers from dark patterns. Given the observed negative impact, we call for a ban on deceptive countdown timers and ask policymakers to consider studies such as this one when creating new policies.
Impacts of Development Induced Displacement on the Tribal Communities of India: An Integrative Review

: Development-induced displacement (DID) has been a contentious issue linked to development today. Thousands of people, particularly tribal and other marginalized communities in India, are being uprooted from their land every year in the name of development. The available evidence-based suggests that DID brings a myriad of implications to the lives of the displaced. However, it is unclear what domains DID impacts and marginalizes tribal communities. Therefore, this review aims to analyze the impacts of DID on the lives of tribal communities in India. Synthesizing evidence on the impact of DID deserves high policy and research attention. An integrative review of the published literature between 1992 and 2019 was undertaken. The review was performed using the database from Google Scholar and Scopus. Seventy-three articles met the inclusion criteria and were analyzed. Five themes emerged from the review on the impact of DID on tribal communities: impact on land and livelihood, impacts on socio-cultural life, impact on health and education, human rights violation, and the state of rehabilitative measures. DID has put a significant toll on the lives of tribal communities of India. Policy measures should be implemented appropriately to ensure the rights of tribal and other vulnerable communities. Each of the displacement on the different domains of tribal life. The themes include (a) impact on and impacts on (c) impact on and rights and
The special McKay correspondence and exceptional collection

We show that the derived category of coherent sheaves on the quotient stack of the affine plane by a finite small subgroup of the general linear group is obtained from the derived category of coherent sheaves on the minimal resolution by adding a semiorthogonal summand with a full exceptional collection. The proof is based on an explicit construction in the abelian case, together with the analysis of the behavior of the derived categories of coherent sheaves under root constructions.
Intrinsic shear transformations in metallic glasses

: Plastic flow in amorphous solids is known to be carried by localized shear transformations (STs) which have been proposed to preferentially initiate from some “defect” units in the structure, akin to dislocations and point defects in crystalline solids. Despite the central role of STs in the mechanical deformation of metallic glasses (MGs), our knowledge of their characteristics has so far been limited to hypothetical models, based on computer simulations using unreleastically high cooling rates. Using combined molecular dynamics (MD) and Monte Carlo (MC) simulations, here we have succeeded in solidifying a metallic liquid at an effective cooling rate as slow as 500 K/s to approach that typical in experiments for producing bulk MGs. Exploiting this realistic MG model, we find that STs do not arise from signature structural defects that can be recognized a priori . Upon yielding, only about 2% of the total atoms participate in STs, each event involving as few as ~10 atoms. These findings rectify the unrealistically high content of “liquid-like regions” retained in MD-produced glass structures, which has rendered the MG model artificially ductile and under-predicted the sample-wide shear modulus by at least ~20% (with respect to that of experimental BMGs). Our finding sheds light on the scope of intrinsic structural inhomogeneity as well as the indeterministic aspect of the ST emergence under mechanical loading.
Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection

Wind turbine yaw control plays an important role in increasing the wind turbine production and also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective wind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by the problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error on wind turbines on-line in order to improve the reliability of yaw angle measurement in real time. Particularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm operators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of qualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is firstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting fault based on Supervisory Control and Data Acquisition (SCADA) data. The zero-point shifting fault is detected in the proposed method by analyzing the power performance under different yaw angles. The SCADA data are partitioned into different bins according to both wind speed and yaw angle in order to deeply evaluate the power performance. An indicator is proposed in this method for power performance evaluation under each yaw angle. The yaw angle with the largest indicator is considered as the yaw angle measurement error in our work. A zero-point shifting fault would trigger an alarm if the error is larger than a predefined threshold. Case studies from several actual wind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault and also in improving the wind turbine performance. Results of the proposed method could be useful for wind farm operators to realize prompt adjustment if there exists a large error of yaw angle measurement.
Can Desegregation Close the Racial Gap in High School Coursework?

This paper examines the interplay between desegregation, institutional bias, and individual behavior in education. Using a game-theoretic model that considers race-heterogeneous social incentives, the study investigates the effects of between-school desegregation on within-school disparities in coursework. The analysis incorporates a segregation measure based on entropy and proposes an optimization-based approach to evaluate the impact of student reassignment policies. The results highlight that Black and Hispanic students in predominantly White schools, despite receiving less encouragement to apply to college, exhibit higher enrollment in college-prep coursework due to stronger social incentives from their classmates' coursework decisions.
Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokeh

In this paper, we tackle the problem of monocular bokeh synthesis, where we attempt to render a shallow depth of field image from a single all-in-focus image. Unlike in DSLR cameras, this effect can not be captured directly in mobile cameras due to the physical constraints of the mobile aperture. We thus propose a network-based approach that is capable of rendering realistic monocular bokeh from single image inputs. To do this, we introduce three new edge-aware Bokeh Losses based on a predicted monocular depth map, that sharpens the foreground edges while blurring the background. This model is then finetuned using an adversarial loss to generate a realistic Bokeh effect. Experimental results show that our approach is capable of generating a pleasing, natural Bokeh effect with sharp edges while handling complicated scenes.
Limit theorems for time averages of continuous-state branching processes with immigration

In this work we investigate limit theorems for the time-averaged process $\left(\frac{1}{t}\int_0^t X_s^x ds\right)_{t\geq 0}$ where $X^x$ is a subcritical continuous-state branching processes with immigration (CBI processes) starting in $x \geq 0$. Under a second moment condition on the branching and immigration measures we first prove the law of large numbers in $L^2$ and afterward establish the central limit theorem. Assuming additionally that the big jumps of the branching and immigration measures have finite exponential moments of some order, we prove in our main result the large deviation principle and provide a semi-explicit expression for the good rate function in terms of the branching and immigration mechanisms. Our methods are deeply based on a detailed study of the corresponding generalized Riccati equation and related exponential moments of the time-averaged process.
An Artificial Intelligence Approach for Online Optimization of Flexible Manufacturing Systems

This paper addresses the problem of efficiently operating a flexible manufacturing machine in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is formulated as a sequential decision making problem under uncertainty where, at every time step the uncertainty comes from the lack of knowledge about fu-ture electricity consumption and future weather dependent energy prices. We propose to address this problem using deep reinforcement learning. To this purpose, we designed a deep learning architecture to forecast the load profile of future manufacturing schedule from past production time series. Combined with the forecast of future energy prices, the reinforcement-learning algorithm is trained to perform an online optimization of the production ma-chine in order to reduce the long-term energy costs. The concept is empirical-ly validated on a flexible production machine, where the machine speed can be optimized during the production.
Partially Relevant Video Retrieval

Current methods for text-to-video retrieval (T2VR) are trained and tested on video-captioning oriented datasets such as MSVD, MSR-VTT and VATEX. A key property of these datasets is that videos are assumed to be temporally pre-trimmed with short duration, whilst the provided captions well describe the gist of the video content. Consequently, for a given paired video and caption, the video is supposed to be fully relevant to the caption. In reality, however, as queries are not known a priori, pre-trimmed video clips may not contain sufficient content to fully meet the query. This suggests a gap between the literature and the real world. To fill the gap, we propose in this paper a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR). An untrimmed video is considered to be partially relevant w.r. t. a given textual query if it contains a moment relevant to the query. PRVR aims to retrieve such partially relevant videos from a large collection of untrimmed videos. PRVR differs from single video moment retrieval and video corpus moment retrieval, as the latter two are to retrieve moments rather than untrimmed videos. We formulate PRVR as a multiple instance learning (MIL) problem, where a video is simultaneously viewed as a bag of video clips and a bag of video frames. Clips and frames represent video content at different time scales. We propose a Multi-Scale Similarity Learning (MS-SL) network that jointly learns clip-scale and frame-scale similarities for PRVR. Extensive experiments on three datasets (TVR, ActivityNet Captions, and Charades-STA) demonstrate the viability of the proposed method. We also show that our method can be used for improving video corpus moment retrieval.
Learning Continuous Implicit Representation for Near-Periodic Patterns

Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and are composed of tiled motifs with appearance differences caused by lighting, defects, or design elements. A good NPP representation is useful for many applications including image completion, segmentation, and geometric remapping. But representing NPP is challenging because it needs to maintain global consistency (tiled motifs layout) while preserving local variations (appearance differences). Methods trained on general scenes using a large dataset or single-image optimization struggle to satisfy these constraints, while methods that explicitly model periodicity are not robust to periodicity detection errors. To address these challenges, we learn a neural implicit representation using a coordinate-based MLP with single image optimization. We design an input feature warping module and a periodicity-guided patch loss to handle both global consistency and local variations. To further improve the robustness, we introduce a periodicity proposal module to search and use multiple candidate periodicities in our pipeline. We demonstrate the effectiveness of our method on more than 500 images of building facades, friezes, wallpapers, ground, and Mondrian patterns on single and multi-planar scenes.
Constraining Sterile Neutrino Dark Matter in the Milky Way Halo with Swift-XRT

We present a search for sterile neutrino dark matter decay signals in the Milky Way’s dark matter halo by considering the entirety of the Swift X-ray Telescope (Swift-XRT) data archive. After filtering the raw archive, we analyze a ∼77 Ms data set containing the full field of view, as well as a ∼41 Ms data set with point sources excised using the Swift-XRT Point Source catalog. We report nondetections of emission lines across the 3–6 keV continuum in both data sets, including at and around 3.5 keV. The point-source-excised data set is found to have higher sensitivity to faint dark matter decay signals due to its freedom from point-source contamination and is thus used to set constraints. Nondetections across the total data set’s continuum are used to constrain the sterile neutrino dark matter parameter space, marginally strengthening existing X-ray constraints. Nondetections at ∼3.5 keV in data subsets grouped by angular distance from the galactic center are used to constrain the 3.5 keV line’s galactic intensity profile, providing the strongest constraints to date across ∼1/4 of the galaxy.
Simulating how animals learn: a new modelling framework applied to the process of optimal foraging

Animal learning has interested ecologists and psychologists for over a century. Mathematical models that explain how animals store and recall information have gained attention recently. Central to this work is statistical decision theory (SDT), which relates information uptake in animals to Bayesian inference. SDT effectively explains many learning tasks in animals, but extending this theory to predict how animals will learn in changing environments still poses a challenge for ecologists. We addressed this shortcoming with a novel implementation of Bayesian Markov Chain Monte Carlo (MCMC) sampling to simulate how animals sample environmental information and learn as a result. We applied our framework to an individual-based model simulating complex foraging tasks encountered by wild animals. Simulated ``animals"learned behavioral strategies that optimized foraging returns simply by following the principles of an MCMC sampler. In these simulations, behavioral plasticity was most conducive to efficient foraging in unpredictable and uncertain environments. Our model suggests that animals prioritize highly concentrated resources even when these resources are less available overall, in line with existing knowledge on optimal foraging and ideal free distribution theory. Our innovative computational modelling framework can be applied more widely to simulate the learning of many other tasks in animals and humans.
Reduce Communication Costs and Preserve Privacy: Prompt Tuning Method in Federated Learning

Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way by aggregating model updates. However, for many natural language processing (NLP) tasks that utilize pre-trained language models (PLMs) with large numbers of parameters, there are considerable communication costs associated with FL. Recently, prompt tuning, which tunes some soft prompts without modifying PLMs, has achieved excellent performance as a new learning paradigm. Therefore we want to combine the two methods and explore the effect of prompt tuning under FL. In this paper, we propose ”FedPrompt” as the ﬁrst work study prompt tuning in a model split learning way using FL, and prove that split learning greatly reduces the communication cost, only 0.01% of the PLMs’ parameters, with little decrease on accuracy both on IID and Non-IID data distribution. This improves the efﬁciency of FL method while also protecting the data privacy in prompt tuning. In addition, like PLMs, prompts are uploaded and downloaded between public platforms and personal users, so we try to ﬁgure out whether there is still a backdoor threat using only soft prompt in FL scenarios. We further conduct backdoor attacks by data poisoning on FedPrompt. Our experiments show that normal backdoor attack can not achieve a high attack success rate, proving the robustness of FedPrompt. We hope this work can promote the application of prompt in FL and raise the awareness of the possible security threats.
The Astrodust+PAH Model: A Unified Description of the Extinction, Emission, and Polarization from Dust in the Diffuse Interstellar Medium

We present a new model of interstellar dust in which large grains are a single composite material, “astrodust,” and nanoparticle-sized grains come in distinct varieties including polycyclic aromatic hydrocarbons (PAHs). We argue that a single-composition model for grains larger than ∼0.02 μm most naturally explains the lack of frequency dependence in the far-infrared (FIR) polarization fraction and the characteristic ratio of optical to FIR polarization. We derive a size distribution and alignment function for 1.4:1 oblate astrodust grains that, with PAHs, reproduce the mean wavelength dependence and polarization of Galactic extinction and emission from the diffuse interstellar medium while respecting constraints on solid-phase abundances. All model data and Python-based interfaces are made publicly available.
Hardware-aware mobile building block evaluation for computer vision

In this paper, we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner. Our comparison uses pareto fronts based on randomly sampled networks from a design space to capture the underlying accuracy/complexity trade-offs. We show that our approach enables matching of information obtained by previous comparison paradigms, but provides more insights into the relationship between hardware cost and accuracy. We use our methodology to analyze different building blocks and evaluate their performance on a range of embedded hardware platforms. This highlights the importance of benchmarking building blocks as a preselection step in the design process of a neural network. We show that choosing the right building block can speed up inference by up to a factor of two on specific hardware ML accelerators.
MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing

Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online courses. Some previous studies on KT have concentrated only on the interpretability of the model, whereas others have focused on enhancing the performance. Models that consider both interpretability and the performance improvement have been insufficient. Moreover, models that focus on performance improvements have not shown an overwhelming performance compared with existing models. In this study, we propose MonaCoBERT, which achieves the best performance on most benchmark datasets and has significant interpretability. MonaCoBERT uses a BERT-based architecture with monotonic convolutional multihead attention, which reflects forgetting behavior of the students and increases the representation power of the model. We can also increase the performance and interpretability using a classical test-theory-based (CTT-based) embedding strategy that considers the difficulty of the question. To determine why MonaCoBERT achieved the best performance and interpret the results quantitatively, we conducted ablation studies and additional analyses using Grad-CAM, UMAP, and various visualization techniques. The analysis results demonstrate that both attention components complement one another and that CTT-based embedding represents information on both global and local difficulties. We also demonstrate that our model represents the relationship between concepts.
A scattering transform for graphs based on heat semigroups, with an application for the detection of anomalies in positive time series with underlying periodicities

This paper develops an adaptive version of Mallat’s scattering transform for signals on graphs. The main results are norm bounds for the layers of the transform, obtained from a version of a Beurling-Deny inequality that permits to remove the nonlinear steps in the scattering transform. Under statistical assumptions on the input signal, the norm bounds can be reﬁned. The concepts presented here are illustrated with an application to traﬃc counts which exhibit characteristic daily and weekly periodicities. Anomalous traﬃc patterns which deviate from these expected periodicities produce a response in the scattering transform.
Direct summands of klt singularities

We show that direct summands (or more generally, pure images) of klt type singularities are of klt type. As a consequence, we give a diﬀerent proof of a recent result of Braun, Greb, Langlois and Moraga that reductive quotients of klt type singularities are of klt type.