Miracle-dz/UniFAR-FTUs huggingface dataset:
HOW WELL DOES A CROWD RECOGNIZE DIFFERENCES BETWEEN PHOTOGRAPHS OF THE SAME ARTWORKS?

This bachelor thesis presents two novel algorithms that can be used in combination to determine the similarity of two photographs. The one algorithm analyses and compares the tags of two reproductions. These tags were assigned in advance, mainly by laypersons on the ARTigo platform. More precisely, the algorithm calculates the cosine similarity of the tags that have been assigned to the images. The information provided by the first algorithm can then be used to verify how much the tags annotated by the crowd differ between two reproductions that are already known to represent the same original. The ARTigo platform is part of an interdisciplinary project within the field of digital art studies, in which computer scientists and art history scholars conduct joint research. This bachelor thesis is part of that research project. The second algorithm is a computer vision algorithm that first calculates the feature vectors of the images. These are then matched so as to estimate how similar the two images are. Then, in a second step, the second algorithm calculates the general colour difference of the images in the Lab colour space. The results of the second algorithm can then be used to draw conclusions about the actual difference between the images. These results can also be used to classify whether, in the case of a high degree of similarity between the tags, two digital reproductions of the same original are involved, or whether both reproductions merely show the same or a similar motif. Finally, the thesis reports how the similarity of the assigned tags and the results of computer vision can be used to draw conclusions regarding whether two images represent the same original. This thesis is concluded with reflections on further possible application of the approach described in this thesis as well as how the work reported about in this thesis could be deepened.
Robust Pruning at Initialization

Overparameterized Neural Networks (NN) display state-of-the-art performance. However, there is a growing need for smaller, energy-efficient, neural networks tobe able to use machine learning applications on devices with limited computational resources. A popular approach consists of using pruning techniques. While these techniques have traditionally focused on pruning pre-trained NN (LeCun et al.,1990; Hassibi et al., 1993), recent work by Lee et al. (2018) has shown promising results when pruning at initialization. However, for Deep NNs, such procedures remain unsatisfactory as the resulting pruned networks can be difficult to train and, for instance, they do not prevent one layer from being fully pruned. In this paper, we provide a comprehensive theoretical analysis of Magnitude and Gradient based pruning at initialization and training of sparse architectures. This allows us to propose novel principled approaches which we validate experimentally on a variety of NN architectures.
Fractionalized Quasiparticles in Dense Baryonic Matter

I discuss a novel scenario on how baryonic matter could turn into a Fermi liquid of fractionalized baryons driven by high density inside compact stars. It involves the “hidden” local vector bosons ρ and ω together with the “hidden” scale symmetry dilaton χ , both taken to be “dual” to the quarks and gluons of QCD in the sense of quark ↔ hadron duality and oﬀers a possibility, exploiting the “Cheshire Cat Principle”, to unify the “dichotomy” of baryons described in terms of skyrmions and fractional quantum Hall (FQH) droplets and to explore how the baryonic matter could behave as density goes way beyond the normal nuclear matter density to the density regime where “deconﬁned” quarks are presumed to ﬁgure. The question is raised as to whether the fractionalized characteristics of the “stuﬀ” – whatever they are – can be “probed” in the dense star core of compact stars.
Surrogate gradients for analog neuromorphic computing

Significance Neuromorphic systems aim to accomplish efficient computation in electronics by mirroring neurobiological principles. Taking advantage of neuromorphic technologies requires effective learning algorithms capable of instantiating high-performing neural networks, while also dealing with inevitable manufacturing variations of individual components, such as memristors or analog neurons. We present a learning framework resulting in bioinspired spiking neural networks with high performance, low inference latency, and sparse spike-coding schemes, which also self-corrects for device mismatch. We validate our approach on the BrainScaleS-2 analog spiking neuromorphic system, demonstrating state-of-the-art accuracy, low latency, and energy efficiency. Our work sketches a path for building powerful neuromorphic processors that take advantage of emerging analog technologies. To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been demonstrated. Here, we demonstrate surrogate gradient learning on the BrainScaleS-2 analog neuromorphic system using an in-the-loop approach. We show that learning self-corrects for device mismatch, resulting in competitive spiking network performance on both vision and speech benchmarks. Our networks display sparse spiking activity with, on average, less than one spike per hidden neuron and input, perform inference at rates of up to 85,000 frames per second, and consume less than 200 mW. In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms.
Proactive Approach for Odour Emissions Characterization in Wastewater Treatment Plant by H2odour System

The characterization and quantification of the odour emissions in wastewater treatment plant (WWTP) represents a fundamental step in order to identify potential odour problems and establish the best and most appropriate odour management tools. Nowadays odour emissions in WWTP are characterized by the use of sampling techniques which take samples directly from the gaseous phase: i.e. when the emission and consequently the potential problem are already present.The definition of proactive tools for the assessment of potential odour emissions from the emitting sources can prevent the occurrence of potential significant odours events and as consequence complaints.The research presents and describes a novel system, called H2Odour, useful to measure the potential Odour Emission Capacity (OEC) directly from liquids. The system has been designed and developed to standardize the stripping phase of gaseous samples, ensuring the maximization of the odour compounds transfer from the liquid to the gaseous phase. The application on a real full scale WWTP is highlighted. H2Odour represents a novel, proactive device to prevent potential critical situations, allowing a preliminary characterization of the maximum potential odour emissions directly from the liquid phase and through this control activity, leading to a greater environmental and social acceptability of the WWTP.
p70 S6K Controls Selective mRNA Translation during Oocyte Maturation and Early Embryogenesis in Xenopus laevis

In mammalian cells, p70 S6K plays a key role in translational control of cell proliferation in response to growth factors. Because of the reliance on translational control in early vertebrate development, we cloned a Xenopus homolog of p70 S6K and investigated the activity proﬁle of p70 S6K during Xenopus oocyte maturation and early embryogenesis. p70 S6K activity is high in resting oocytes and decreases to background levels upon stimulation of maturation with progesterone. During embryonic development, three peaks of activity were observed: immediately after fertilization, shortly before the midblastula transition, and during gastrulation. Rapamycin, an inhibitor of p70 S6K activation, caused oocytes to undergo germinal vesicle breakdown earlier than control oocytes, and sensitivity to progesterone was increased. Injection of a rapamycin-insensitive, con- stitutively active mutant of p70 S6K reversed the effects of rapamycin. However, increases in S6 phosphorylation were not signiﬁcantly affected by rapamycin during maturation. mos mRNA, which does not contain a 5 * terminal oligopyrimidine tract (5 * -TOP), was translated earlier, and a larger amount of Mos protein was produced in rapamycin-treated oocytes. In fertilized eggs rapamycin treatment increased the translation of the Cdc25A phosphatase, which lacks a 5 * -TOP. Translation assays in vivo using both DNA and RNA reporter constructs with the 5 * -TOP from elongation factor 2 showed decreased translational activity with rapamycin, whereas constructs without a 5 * -TOP or with an internal ribosome entry site were translated more efﬁciently upon rapamycin treatment. These results suggest that changes in p70 S6K activity during oocyte maturation and early embryogenesis selectively alter the translational capacity available for mRNAs lacking a 5 * -TOP region.
Group Structure in Correlations and its Applications in Cryptography

Correlated random variables are a key tool in cryptographic applications like secure multiparty computation. We investigate the power of a class of correlations that we term group correlations: A group correlation is a uniform distribution over pairs (x, y) ∈ G such that x + y ∈ S, where G is a (possibly non-abelian) group and S is a subset of G. We also introduce bi-a ne correlations, and show how they relate to group correlations. We present several structural results, new protocols and applications of these correlations. The new applications include a completeness result for black box group computation, perfectly secure protocols for evaluating a broad class of black box mixed-groups circuits with bi-a ne homomorphisms, and new information-theoretic results. Finally, we uncover a striking structure underlying OLE: In particular, we show that OLE over F2n , is isomorphic to a group correlation over Z4 .
The Liberalization Process of Satire in Postauthoritarian Democracies: Potentials and Limits in Mexico’s Network Television

The potential of television satire, an important means of denunciation of abuses of power and incompetence by politicians, can be limited in post-authoritarian democracies, where previous arrangements between political elites and media may hinder what satirists can say and to whom. Drawing from the literature on satire in post-authoritarian regimes and on the liberalization of Mexican media, we explain the surge of satire in Mexican broadcast television and its limits. A further qualitative analysis of 274 jokes uttered in satirical shows broadcast between 1995 and 2019 demonstrates that, while the liberalization process fostered satirical expressions about the abuse of power, incompetence, and frivolity of politicians, the remaining instrumentalization practices led to self-censorship, officialdom, and futile humor. Hence, we conclude that the political-economic conditions that played a part in the political transition influenced the scope of Mexican television satire.
The Emotional Wellbeing of Doctoral Students Conducting Qualitative Research with Vulnerable Populations

Qualitative studies have the potential to evoke emotional distress among participants, particularly where sensitive issues are explored. Consequently, novice qualitative researchers become well versed in strategies to minimise risk to participants. But what happens to the novice researcher when they are confronted with the emotional account of a participant? While research practices consistently serve to protect the wellbeing of participants, concern for researcher wellbeing is often overlooked. This study explored doctoral students’ experiences of undertaking qualitative research projects with vulnerable populations, and the ways in which this influenced their emotional wellbeing. Semi-structured interviews were conducted with six doctoral candidates attending a public Australian university. The analysis shed light on the ways in which doctoral students navigate their emotional wellbeing, pointing to a range of challenges and resources. This article discusses potential intervention points throughout doctoral candidature, while pondering deeper questions about the responsibilities of universities to support early career researchers.
Alto Snapshot/Restore: Enabling Stateful, Long-Lived Serverless Computation

As demand for the serverless computing paradigm increases, so does demand for a currently unavailable serverless architecture: stateful, long-lived serverless computation. In this paper, we re-introduce Alto, which provides such an architecture. Using Alto, developers can enjoy the autoscaling and fine-grained billing promised by the serverless platform while also enjoying the performance benefits of co-locating code and local data. This serverless paradigm makes developing existing applications easier and also enables new cloud application architectures. We explain why providing state-ful, long-lived serverless computation requires fast snapshot and restore of computation. We explain how Alto virtual-izes the managed runtime to provide fast snapshot and restore. We describe how Alto moves filesystem and network state from the kernel into the Alto runtime to improve performance. Finally, we evaluate an Alto prototype against Checkpoint-and-Restore In Userspace (CRIU), a fast process checkpoint-restore tool, and show that Alto snapshot and restore performance is comparable.
Numerical Investigation of Heat Transfer in a Rectangular Channel with Square Baffles and a Triangular Obstacle

Received: 13 July 2020 Accepted: 26 December 2020 In this work, the heat transfer was investigated in a rectangular channel with an internal triangular obstacle and square baffles. Three square baffles were located on the lower wall of the channel, while a triangular obstacle was placed in the middle of the channel and placed before the first baffle. Air was used as a working fluid. The governing equations were solved with finite volume method. The effect of the geometric parameters such as the ratio of the width to height of the obstacle, the vertical and horizontal distance between obstacle and baffle and the baffle angle, has been investigated on the heat transfer in this system. Then, in next parts, the effect of baffle and obstacle shape was considered. The results show that by increasing the width and height of the obstacle, the baffle temperature decreases and the Nusselt number of the baffle increases. In fact, when the obstacle width becomes larger, a longer vortex is formed that helps to improve the heat transfer rate. Also, when the obstacle is perpendicular to the flow path, better heat exchange between the baffle and the fluid was performed. With Elliptical baffles and a triangular obstacle, higher heat transfer was achieved.
A Logic for Locally Complete Abstract Interpretations

We introduce the notion of local completeness in abstract interpretation and define a logic for proving both the correctness and incorrectness of some program specification. Abstract interpretation is extensively used to design sound-by-construction program analyses that over-approximate program behaviours. Completeness of an abstract interpretation A for all possible programs and inputs would be an ideal situation for verifying correctness specifications, because the analysis can be done compositionally and no false alert will arise. Our first result shows that the class of programs whose abstract analysis on A is complete for all inputs has a severely limited expressiveness. A novel notion of local completeness weakens the above requirements by considering only some specific, rather than all, program inputs and thus finds wider applicability. In fact, our main contribution is the design of a proof system, parameterized by an abstraction A, that, for the first time, combines over- and under-approximations of program behaviours. Thanks to local completeness, in a provable triple ⊢A [P ] c [Q], the assertion Q is an under-approximation of the strongest post-condition post[c](P ) such that the abstractions in A of Q and post[c](P ) coincide. This means that Q is never too coarse, namely, under mild assumptions, the abstract interpretation of c does not yield false alerts for the input P iff Q has no alert. Thus, ⊢ A [P ] c [Q] not only ensures that all the alerts raised in Q are true ones, but also that if Q does not raise alerts then c is correct.
A Large Scale Benchmark for Uplift Modeling

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient [1]. To foster research in this topic we release a publicly available collection of 25 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor. We provide details on the data collection and sanity checks performed that allow the use of this data for counter-factual prediction. We formalize the task of uplift prediction that could be performed with this data, along with the relevant evaluation metrics. Finally we show that the dataset size makes it now possible to reach statistical significance when evaluating baseline methods on the most challenging target.
Exploring Textual Cohesion Characteristics for German Readability Classiﬁcation

This thesis investigates diﬀerent feature sets that can be used to assess the cohesion of German texts including aspects of discourse coherence. The features are used to classify texts in terms of diﬃculty in a binary way. We focus on feature sets that were presented within researches for English and French, but not yet considered for German. This includes features based on diﬀerent types of referring expressions, referential features, features based on connectives and features based on syntactic transitions. The implemented features will be evaluated on a corpus of German magazine articles that either address children or adults. The evaluation includes the features that were replicated from a previous study on this corpus. It is shown that the readability classiﬁcation based on a combination of the implemented features and the replicated features results in an accuracy of 89.8%. The best performing feature subset implemented within this work is based on types of referring expressions, where especially pronoun frequencies make important contributions.
Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions

In the display advertising market, one of the most popular advertisers’ goals is acquiring conversions such as app installs and purchases, and an important technology that enables the advertising platform to support this campaign goal is to predict conversion rate (CVR). There are two major difficulties in predicting CVR: one is that conversions often don’t happen immediately after a click, and the other is that some advertising products have to accept multiple conversions. In this paper, we introduce a new model jointly trained Negative Binomial and Order Statistics to tackle the multiple conversions and a series of conversion delays, simultaneously. Our proposed model shows the significant improvement in the real traffic data. CCS CONCEPTS • Information systems→Computational advertising; •Mathematics of computing → Probabilistic algorithms; • Theory of computation → Theory and algorithms for application domains.
Improved Transportation Networks Facilitate Adaptation to Pollution and Temperature Extremes

The social costs of pollution and climate change hinge critically on humans’ ability to adapt. Based on transaction records from the world’s largest payment network, this research compiles daily travel flows and documents that China's rapid expansion of high-speed railways (HSR) facilitates the use of intercity travel as an effective adaptation strategy. Access to HSR reduces travelers' exposure to extreme air pollution and temperature by 7% and 10%, leading to substantial health benefits. These reductions are attributed to both contemporaneous responses to unexpected adverse conditions and also longer-horizon changes in travel patterns.
Design of a Flat Membrane Module for Fouling and Permselectivity Studies

Flat membrane modules of laboratory scale are commonly used to study membrane performance. As membrane performance is influenced by pressure and velocity, it is important to have hydrodynamics conditions as uniform as possible over the membrane surface. However, most modules designed for laboratory studies have high pressure drop and abrupt changes of flow direction what yields to lack of uniform flow and uncertainty of the pressure value over the membrane surface. In this paper, we explain how COMSOL was used to design a module intended to minimize these drawbacks. The Incompressible NavierStokes mode was used to optimize the geometry of the fluid chamber while the solid stress-strain mode was used to improve the mechanical performance of the module. Fluid dynamic computation results were evaluated with experimental results obtained with the module prototype.
3D Shape Generation with Grid-based Implicit Functions

Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it is difficult to add spatial supervision into the generation process, as the AE only gives us a global representation. To remedy these issues, we propose to train the GAN on grids (i.e. each cell covers a part of a shape). In this representation each cell is equipped with a latent vector provided by an AE. This localized representation enables more expressiveness (since the cell-based latent vectors can be combined in novel ways) as well as spatial control of the generation process (e.g. via bounding boxes). Our method outperforms the current state of the art on all established evaluation measures, proposed for quantitatively evaluating the generative capabilities of GANs. We show limitations of these measures and propose the adaptation of a robust criterion from statistical analysis as an alternative.