5,732 research outputs found
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new
experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse
Classical and quantum algorithms for scaling problems
This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size
On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications
Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified
Essays on Corporate Disclosure of Value Creation
Information on a firm’s business model helps investors understand an entity’s resource requirements, priorities for action, and prospects (FASB, 2001, pp. 14-15; IASB, 2010, p. 12). Disclosures of strategy and business model (SBM) are therefore considered a central element of effective annual report commentary (Guillaume, 2018; IIRC, 2011). By applying natural language processing techniques, I explore what SBM disclosures look like when management are pressed to say something, analyse determinants of cross-sectional variation in SBM reporting properties, and assess whether and how managers respond to regulatory interventions seeking to promote SBM annual report commentary. This dissertation contains three main chapters. Chapter 2 presents a systematic review of the academic literature on non-financial reporting and the emerging literature on SBM reporting. Here, I also introduce my institutional setting. Chapter 3 and Chapter 4 form the empirical sections of this thesis. In Chapter 3, I construct the first large sample corpus of SBM annual report commentary and provide the first systematic analysis of the properties of such disclosures. My topic modelling analysis rejects the hypothesis that such disclosure is merely padding; instead finding themes align with popular strategy frameworks and management tailor the mix of SBM topics to reflect their unique approach to value creation. However, SBM commentary is less specific, less precise about time horizon (short- and long-term), and less balanced (more positive) in tone relative to general management commentary. My findings suggest symbolic compliance and legitimisation characterize the typical annual report discussion of SBM. Further analysis identifies proprietary cost considerations and obfuscation incentives as key determinants of symbolic reporting. In Chapter 4, I seek evidence on how managers respond to regulatory mandates by adapting the properties of disclosure and investigate whether the form of the mandate matters. Using a differences-in-differences research design, my results suggest a modest incremental response by treatment firms to the introduction of a comply or explain provision to provide disclosure on strategy and business model. In contrast, I find a substantial response to enacting the same requirements in law. My analysis provides clear and consistent evidence that treatment firms incrementally increase the volume of SBM disclosure, improve coverage across a broad range of topics as well as providing commentary with greater focus on the long term. My results point to substantial changes in SBM reporting properties following regulatory mandates, but the form of the mandate does matter. Overall, this dissertation contributes to the accounting literature by examining how firms discuss a central topic to economic decision making in annual reports and how firms respond to different forms of disclosure mandate. Furthermore, the results of my analysis are likely to be of value for regulators and policymakers currently reviewing or considering mandating disclosure requirements. By examining how companies adapt their reporting to different types of regulations, this study provides an empirical basis for recalibrating SBM disclosure mandates, thereby enhancing the information set of capital market participants and promoting stakeholder engagement in a landscape increasingly shaped by non-financial information
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Investigation of the metabolism of rare nucleotides in plants
Nucleotides are metabolites involved in primary metabolism, and specialized
metabolism and have a regulatory role in various biochemical reactions in all forms of life. While in other organisms, the nucleotide metabolome was characterized
extensively, comparatively little is known about the cellular concentrations of
nucleotides in plants. The aim of this dissertation was to investigate the nucleotide metabolome and enzymes influencing the composition and quantities of nucleotides in plants. For this purpose, a method for the analysis of nucleotides and nucleosides in plants and algae was developed (Chapter 2.1), which comprises efficient quenching of enzymatic
activity, liquid-liquid extraction and solid phase extraction employing a weak-anionexchange resin. This method allowed the analysis of the nucleotide metabolome of plants in great depth including the quantification of low abundant deoxyribonucleotides and deoxyribonucleosides. The details of the method were summarized in an article, serving as a laboratory protocol (Chapter 2.2).
Furthermore, we contributed a review article (Chapter 2.3) that summarizes the
literature about nucleotide analysis and recent technological advances with a focus on plants and factors influencing and hindering the analysis of nucleotides in plants, i.e., a complex metabolic matrix, highly stable phosphatases and physicochemical
properties of nucleotides. To analyze the sub-cellular concentrations of metabolites, a protocol for the rapid isolation of highly pure mitochondria utilizing affinity chromatography was developed (Chapter 2.4).
The method for the purification of nucleotides furthermore contributed to the
comprehensive analysis of the nucleotide metabolome in germinating seeds and in
establishing seedlings of A. thaliana, with a focus on genes involved in the synthesis of thymidilates (Chapter 2.5) and the characterization of a novel enzyme of purine nucleotide degradation, the XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Chapter 2.6). Protein homology analysis comparing A. thaliana, S. cerevisiae, and H. sapiens led to the identification and characterization of an enzyme involved in the metabolite damage repair system of plants, the INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Chapter 2.7). It was shown that this enzyme dephosphorylates deaminated purine nucleotide triphosphates and thus prevents their incorporation into nucleic acids. Lossof-function mutants senesce early and have a constitutively increased content of salicylic acid. Also, the source of deaminated purine nucleotides in plants was investigated and it was shown that abiotic factors contribute to nucleotide damage.Nukleotide sind Metaboliten, die am Primärstoffwechsel und an spezialisierten
Stoffwechselvorgängen beteiligt sind und eine regulierende Rolle bei verschiedenen
biochemischen Reaktionen in allen Lebensformen spielen. Während bei anderen
Organismen das Nukleotidmetabolom umfassend charakterisiert wurde, ist in Pflanzen
vergleichsweise wenig über die zellulären Konzentrationen von Nukleotiden bekannt.
Ziel dieser Dissertation war es, das Nukleotidmetabolom und die Enzyme zu
untersuchen, die die Zusammensetzung und Menge der Nukleotide in Pflanzen
beeinflussen. Zu diesem Zweck wurde eine Methode zur Analyse von Nukleotiden und
Nukleosiden in Pflanzen und Algen entwickelt (Kapitel 2.1), die ein effizientes Stoppen
enzymatischer Aktivität, eine Flüssig-Flüssig-Extraktion und eine
Festphasenextraktion unter Verwendung eines schwachen Ionenaustauschers
umfasst. Mit dieser Methode konnte das Nukleotidmetabolom von Pflanzen eingehend
analysiert werden, einschlieĂźlich der Quantifizierung von Desoxyribonukleotiden und
Desoxyribonukleosiden mit geringer Abundanz. Die Einzelheiten der Methode wurden
in einem Artikel zusammengefasst, der als Laborprotokoll dient (Kapitel 2.2).
DarĂĽber hinaus wurde ein Ăśbersichtsartikel (Kapitel 2.3) verfasst, der die Literatur
ĂĽber die Analyse von Nukleotiden und die jĂĽngsten technologischen Fortschritte
zusammenfasst. Der Schwerpunkt lag hierbei auf Pflanzen und Faktoren, die die
Analyse von Nukleotiden in Pflanzen beeinflussen oder behindern, d. h. eine komplexe
Matrix, hochstabile Phosphatasen und physikalisch-chemische Eigenschaften von
Nukleotiden.
Um die subzellulären Konzentrationen von Metaboliten zu analysieren, wurde ein
Protokoll fĂĽr die schnelle Isolierung hochreiner Mitochondrien unter Verwendung einer
Affinitätschromatographie entwickelt (Kapitel 2.4).
Die Methode zur Analyse von Nukleotiden trug auĂźerdem zu einer umfassenden
Analyse des Nukleotidmetaboloms in keimenden Samen und in sich etablierenden
Keimlingen von A. thaliana bei, wobei der Schwerpunkt auf Genen lag, die an der
Synthese von Thymidilaten beteiligt sind (Kapitel 2.5), sowie zu der Charakterisierung
eines neuen Enzyms des Purinnukleotidabbaus, der XANTHOSINE
MONOPHOSPHATE PHOSPHATASE (Kapitel 2.6). Eine Proteinhomologieanalyse, die A. thaliana, S. cerevisiae und H. sapiens
miteinander verglich fĂĽhrte zur Identifizierung und Charakterisierung eines Enzyms,
das an der Reparatur von geschädigten Metaboliten in Pflanzen beteiligt ist, der
INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Kapitel 2.7). Es konnte gezeigt
werden, dass dieses Enzym desaminierte Purinnukleotidtriphosphate
dephosphoryliert und so deren Einbau in Nukleinsäuren verhindert.
Funktionsverlustmutanten altern früh und weisen einen konstitutiv erhöhten Gehalt an Salicylsäure auf. Außerdem wurde die Quelle der desaminierten Purinnukleotide in Pflanzen untersucht, und es wurde gezeigt, dass abiotische Faktoren zur
Nukleotidschädigung beitragen
Personalized prostate cancer management : AI-assisted prostate pathology and improved active surveillance
Prostate cancer is a major global health concern and is the most common cancer-related cause
of death in Sweden. Prostate cancer screening using PSA has been shown to reduce prostate
cancer mortality but also leads to significant overdiagnosis and overtreatment of low-risk cancers.
Improved risk stratification and effective active surveillance are crucial to balancing the
benefits of screening with the risk of overdiagnosis and overtreatment.
In Study I, we studied the uptake and the follow-up of active surveillance using a retrospective
cohort of patients who were diagnosed with low-risk prostate cancer between 2008 and 2017
in Stockholm County. Our results showed that only 50% of eligible active surveillance patients
received active surveillance as their primary treatment choice at diagnosis. Most men that
enrolled in active surveillance remained on surveillance during the first years after diagnosis
(82% during a median 3.5 years), but did not receive a follow up according to guidelines with
regard to repeat biopsies and PSA tests.
Current clinical practice has seen an increase in the use of magnetic resonance imaging (MRI)
and the incorporation of risk prediction models to select men with the highest suspicion of clinically
significant prostate cancer for prostate biopsy. However, the effectiveness and how MRI
and risk prediction models should be incorporated into active surveillance follow-up have yet to
be established. Study II evaluated the performance of MRI-targeted biopsies and a blood-based
risk prediction model (the Stockholm3 test) for monitoring disease progression in patients on
active surveillance and compared this to the conventional follow-up using PSA and systematic
biopsies. When MRI-targeted and systematic biopsies were combined, the detection rate
of clinically significant prostate cancer increased when compared to conventional systematic
biopsies. Biopsies performed in MRI-positive men resulted in a 49% reduction in performed
biopsies, at the expense of failing to diagnose 1.4% clinically significant prostate cancer in MRInegative
men. The incorporation of the Stockholm3 test showed a 27% reduction in required
MRI investigations and a 57% reduction in performed biopsies compared to performing only
systematic biopsies.
In Study III, we digitized biopsy cores from STHLM3 participants to develop an artificial
intelligence (AI) for prostate cancer diagnostics. The AI system demonstrated clinically useful
performance that was comparable to that of the study pathologist for cancer detection (AUC
of 0.986) and for predictions of cancer length (correlation of 0.87) and grading performance
that was on par with that of expert prostate pathologists.
In Study IV, we developed a conformal predictor to estimate the uncertainty of the predictions
for the model in Study III. The uncertainty estimates were used to control the error rate so that
only predictions with high confidence are accepted and unreliable predictions can be detected.
The conformal predictor was able to identify unreliable predictions as a result of variations in
digital pathology scanners, preparation of tissue in different pathology laboratories, and the
existence of unusual prostate tissue that the AI model was not exposed to during training.
Little is known about the relationships between prostate cancer genetic risk factors and the
morphology of prostate tissue. In Study V:, we investigated whether weakly supervised deep
learning can learn to detect such possible associations. The findings in this paper imply relationships
between prostatic tissue morphology and genetic risk factors for prostate cancer,
particularly in young men. These results provide proof of principle for exploring the use of
morphological information in multi-modal prostate cancer risk prediction algorithms.
In conclusion, the purpose of this thesis was to describe possible extensions to improve prostate
cancer active surveillance management, as well as to develop prediction models for improved
prostate cancer diagnostics
Assembling Single RbCs Molecules with Optical Tweezers
Optical tweezer arrays are useful tools for manipulating single atoms and molecules.
An exciting avenue for research with optical tweezers is using the interactions between polar molecules for quantum computation or quantum simulation.
Molecules can be assembled in an optical tweezer array starting from pairs of atoms.
The atoms must be initialised in the relative motional ground state of a common trap.
This work outlines the design of a Raman sideband cooling protocol which is implemented to prepare an 87-Rubidium atom in the motional ground state of an 817 nm tweezer, and a 133-Caesium atom in the motional ground state of a 938 nm tweezer.
The protocol circumvents strong heating and dephasing associated with the trap by operating at lower trap depths and cooling from outside the Lamb-Dicke regime.
By analysing several sources of heating, we design and implement a merging sequence that transfers the Rb atom and the Cs atom to a common trap with minimal motional excitation.
Subsequently, we perform a detailed characterisation of AC Stark shifts caused by the tweezer light, and identify several situations in which the confinement of the atom pair influences their interactions.
Then, we demonstrate the preparation of a molecular bound state after an adiabatic ramp across a magnetic Feshbach resonance.
Measurements of molecular loss rates provide evidence that the atoms are in fact associated during the merging sequence, before the magnetic field ramp.
By preparing a weakly-bound molecule in an optical tweezer, we carry out important steps towards assembling an array of ultracold RbCs molecules in their rovibrational ground states
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