9,033 research outputs found
CacheWarp: Software-based Fault Injection using Selective State Reset
AMD SEV is a trusted-execution environment (TEE), providing confidentiality and integrity for virtual machines (VMs). With AMD SEV, it is possible to securely run VMs on an untrusted hypervisor. While previous attacks demonstrated architectural shortcomings of earlier SEV versions, AMD claims that SEV-SNP prevents all attacks on the integrity.
In this paper, we introduce CacheWarp, a new software-based fault attack on AMD SEV-ES and SEV-SNP, exploiting the possibility to architecturally revert modified cache lines of guest VMs to their previous (stale) state. Unlike previous attacks on the integrity, CacheWarp is not mitigated on the newest SEV-SNP implementation, and it does not rely on specifics of the guest VM. CacheWarp only has to interrupt the VM at an attacker-chosen point to invalidate modified cache lines without them being written back to memory. Consequently, the VM continues with architecturally stale data. In 3 case studies, we demonstrate an attack on RSA in the Intel IPP crypto library, recovering the entire private key, logging into an OpenSSH server without authentication, and escalating privileges to root via the sudo binary. While we implement a software-based mitigation proof-of-concept, we argue that mitigations are difficult, as the root cause is in the hardware
Techniques for high-multiplicity scattering amplitudes and applications to precision collider physics
In this thesis, we present state-of-the-art techniques for the computation of scattering amplitudes in Quantum Field Theories. Following an introduction to the topic, we describe a robust framework that enables the calculation of multi-scale two-loop amplitudes directly relevant to modern particle physics phenomenology at the Large Hadron Collider and beyond. We discuss in detail the use of finite fields to bypass the algebraic complexity of such computations, as well as the method of integration-by-parts relations and differential equations. We apply our framework to calculate the two-loop amplitudes contributing to three process: Higgs boson production in association with a bottom-quark pair, W boson production with a photon and a jet, as well as lepton-pair scattering with an off-shell and an on-shell photon. Finally, we draw our conclusions and discuss directions for future progress of amplitude computations
Algorithms and complexity for approximately counting hypergraph colourings and related problems
The past decade has witnessed advancements in designing efficient algorithms for approximating the number of solutions to constraint satisfaction problems (CSPs), especially in the local lemma regime. However, the phase transition for the computational tractability is not known. This thesis is dedicated to the prototypical problem of this kind of CSPs, the hypergraph colouring. Parameterised by the number of colours q, the arity of each hyperedge k, and the vertex maximum degree Î, this problem falls into the regime of LovĂĄsz local lemma when ΠⲠqáľ. In prior, however, fast approximate counting algorithms exist when ΠⲠqáľ/Âł, and there is no known inapproximability result. In pursuit of this, our contribution is two-folded, stated as follows.
⢠When q, k ⼠4 are evens and Π⼠5¡qáľ/², approximating the number of hypergraph colourings is NP-hard.
⢠When the input hypergraph is linear and ΠⲠqáľ/², a fast approximate counting algorithm does exist
Fairness-aware Machine Learning in Educational Data Mining
Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM.
In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions:
We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes.
We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects.
We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering.
We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation.
In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E
Algebraic solutions of linear differential equations: an arithmetic approach
Given a linear differential equation with coefficients in , an
important question is to know whether its full space of solutions consists of
algebraic functions, or at least if one of its specific solutions is algebraic.
After presenting motivating examples coming from various branches of
mathematics, we advertise in an elementary way a beautiful local-global
arithmetic approach to these questions, initiated by Grothendieck in the late
sixties. This approach has deep ramifications and leads to the still unsolved
Grothendieck-Katz -curvature conjecture.Comment: 47 page
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
Trustworthy Federated Learning (TFL) typically leverages protection
mechanisms to guarantee privacy. However, protection mechanisms inevitably
introduce utility loss or efficiency reduction while protecting data privacy.
Therefore, protection mechanisms and their parameters should be carefully
chosen to strike an optimal tradeoff between \textit{privacy leakage},
\textit{utility loss}, and \textit{efficiency reduction}. To this end,
federated learning practitioners need tools to measure the three factors and
optimize the tradeoff between them to choose the protection mechanism that is
most appropriate to the application at hand. Motivated by this requirement, we
propose a framework that (1) formulates TFL as a problem of finding a
protection mechanism to optimize the tradeoff between privacy leakage, utility
loss, and efficiency reduction and (2) formally defines bounded measurements of
the three factors. We then propose a meta-learning algorithm to approximate
this optimization problem and find optimal protection parameters for
representative protection mechanisms, including Randomization, Homomorphic
Encryption, Secret Sharing, and Compression. We further design estimation
algorithms to quantify these found optimal protection parameters in a practical
horizontal federated learning setting and provide a theoretical analysis of the
estimation error.Comment: arXiv admin note: text overlap with arXiv:2209.0023
A Generalised abc Conjecture and Quantitative Diophantine Approximation
The abc Conjecture and its number field variant have huge implications across a wide
range of mathematics. While the conjecture is still unproven, there are a number of
partial results, both for the integer and the number field setting. Notably, Stewart
and Yu have exponential abc bounds for integers, using tools from linear forms in
logarithms, while GyĹry has exponential abc bounds in the number field
case, using methods from S-unit equations [20]. In this thesis, we aim to combine
these methods to give improved results in the number field case. These results are
then applied to the effective Skolem-Mahler-Lech problem, and to the smooth abc
conjecture.
The smooth abc conjecture concerns counting the number of solutions to a+b = c
with restrictions on the values of a, b and c. this leads us to more general methods
of counting solutions to Diophantine problems. Many of these results are asymptotic
in nature due to use of tools such as Lemmas 1.4 and 1.5 of Harman's "Metric Number Theory". We make these
lemmas effective rather than asymptotic other than on a set of size δ > 0, where δ is
arbitrary. From there, we apply these tools to give an effective Schmidtâs Theorem,
a quantitative Koukoulopoulos-Maynard Theorem (also referred to as the Duffin-
Schaeffer Theorem), and to give effective results on inhomogeneous Diophantine
Approximation on M0-sets, normal numbers and give an effective Strong Law of
Large Numbers. We conclude this thesis by giving general versions of Lemmas 1.4
and 1.5 of Harman's "Metric Number Theory"
Random Additive Polynomials
We study the distribution of the Galois group of a random -additive
polynomial over a rational function field: For a power of a prime , let
be a random polynomial
chosen uniformly from the set of -additive polynomials of degree and
height , that is, the coefficients are independent uniform polynomials of
degree . The Galois group is a random subgroup of
. Our main result shows that is almost surely large as are fixed and . For example, we give necessary and sufficient
conditions so that asymptotically almost surely. Our
proof uses the classification of maximal subgroups of . We also
consider the limits: fixed, and fixed, ,
which are more elementary.Comment: 26 page
New Porous Nanomaterials For Battery and Supercapacitor
Lithium-Sulfur batteries have a high energy storage capacity while their sulfur cathode suffers large volume change, polysulfides dissolution and shuttle effect, and capacity fading during long-term cycling. To help lock sulfur and mitigate these problems, we introduced halloysite, a natural clay material with a nanotube format, to disperse and confine sulfur nanoparticles as well as to suppress the dissolution and migration of polysulfides. Halloysite was made conductive by covering it with a glucose-derived carbon skin. Sulfur nanoparticles were then trapped in both the lumen and outside surface of individual nanotubes with a loading dosage up to 80 %. In this new halloysite/sulfur composites cathode, the hollow nanostructure of halloysite provides space to allow dimension changes of encapsulated sulfur nanoparticles during repeated lithiation while limiting their size up to the diameter of nanotube lumen (i.e., 25 nm or less). The stacked halloysite clusters further create many nanoscale voids to divide the sulfur-electrolyte interface into isolated domains and increase the migration tortuosity in electrolytes to suppress the dissolution and shuttle effect of polysulfides. These features together contribute to improved cycling stability, retaining nearly ~84% of the starting capacity over 250 cycles, though the diffusion of lithium ions going in and out of nanotubes show some differences.
In project 2, we worked on the anode development for LIBs. Silicon-rich (e.g., \u3e30 wt.%) anodes are desired to leverage the current capacity of lithium-ion batteries (LIBs) towards commercial cell performance requirements in critical markets, such as the transportation sector. A new type of nanofiber-in-microfiber silicon/carbon composite electrode was prepared and tested as a potential silicon-rich anode candidate. A co-axial electrospinning setup was used to produce a unique hybrid composite fiber configuration, in which silicon nanoparticles were suspended in a polymer solution to serve as the middle stream while the sheath stream was comprised of another polymer solution. Polyvinyl alcohol (PVA) was chosen as the silicon dispersion fluid because of its limited viscosity increase even at a very high solid allowance, which after carbonization held those nanoparticles together as short, branched nanofibers. Polyacrylonitrile (PAN) sheath fluid helped wrap the formed short, silicon-rich nanofiber bundles to form a nonwoven, ductile microfiber mat. After being carbonized into composite anodes, the silicon-rich nanofibers were used to host the majority of lithium ions while their thin carbon skin, originating from carbonized PVA, promotes conductivity and charge transfer. The nanofibrous morphology and the mesoscale space in between help accommodate the notorious volume expansion issues in silicon anodes during lithiation/delithiation processes. The outside PAN-derived microfibers provide structural support for the encapsulated silicon-rich nanofibers and simultaneously serve as the three-dimensional current collector. The new composite anodes were confirmed on their unique fibrous configuration and improved electrochemical performance. With 40 wt% Si, such silicon-rich, nanofiber-in-microfiber anodes achieve ~900 mAhg-1 reversible capacity and ~90% capacity retention over 250 cycles by effectively balancing challenges on silicon-rich fibrous anode and electrode pulverization.
Beside battery research, we also worked on supercapacitors with high power density in project 3. Despite the great benefits plastics have brought to our modern lives, a large volume of plastic wastes increasingly threatens our environment and human health. Through a hydrothermal carbonization and crystallization process involving nitric acid and ethanol, drinking bottles made of polyethylene terephthalate were successfully converted into carbon quantum dots (CQDs) and thin carbon sheets simultaneously, with the former well dispersed and intercalated in the latter as a ball-sheet carbon structure (BSCs). The formed unique, connected, and conductive carbon network allows rapid transport of ions and electrons besides their large surface area and numerous ion hosting sites. The electrodes made of such a plastic ball-sheet carbon structure (PBSCs) therefore exhibit pseudocapacitance behavior with the specific capacity reaching 237 F/g at the charge rate of 1 A/g. Superior cycling stability on the energy storage was also found. Our method offers a new avenue to upcycle some plastic wastes as valuable energy storage systems, to help boost the recycling of plastic waste, and move forwards to the sustainable deployment of various clean energy resources
Fair Correlation Clustering in Forests
The study of algorithmic fairness received growing attention recently. This stems from the awareness that bias in the input data for machine learning systems may result in discriminatory outputs. For clustering tasks, one of the most central notions of fairness is the formalization by Chierichetti, Kumar, Lattanzi, and Vassilvitskii [NeurIPS 2017]. A clustering is said to be fair, if each cluster has the same distribution of manifestations of a sensitive attribute as the whole input set. This is motivated by various applications where the objects to be clustered have sensitive attributes that should not be over- or underrepresented. Most research on this version of fair clustering has focused on centriod-based objectives.
In contrast, we discuss the applicability of this fairness notion to Correlation Clustering. The existing literature on the resulting Fair Correlation Clustering problem either presents approximation algorithms with poor approximation guarantees or severely limits the possible distributions of the sensitive attribute (often only two manifestations with a 1:1 ratio are considered). Our goal is to understand if there is hope for better results in between these two extremes. To this end, we consider restricted graph classes which allow us to characterize the distributions of sensitive attributes for which this form of fairness is tractable from a complexity point of view.
While existing work on Fair Correlation Clustering gives approximation algorithms, we focus on exact solutions and investigate whether there are efficiently solvable instances. The unfair version of Correlation Clustering is trivial on forests, but adding fairness creates a surprisingly rich picture of complexities. We give an overview of the distributions and types of forests where Fair Correlation Clustering turns from tractable to intractable.
As the most surprising insight, we consider the fact that the cause of the hardness of Fair Correlation Clustering is not the strictness of the fairness condition. We lift most of our results to also hold for the relaxed version of the fairness condition. Instead, the source of hardness seems to be the distribution of the sensitive attribute. On the positive side, we identify some reasonable distributions that are indeed tractable. While this tractability is only shown for forests, it may open an avenue to design reasonable approximations for larger graph classes
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