Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate impact on geophysical thermokarst processes during the past 6000 years

About one quarter of all land surfaces in the northern hemisphere are underlain by permafrost. Permafrost soils contain a large amount of organic carbon and a destabilization due to the projected future amplitude of Arctic-climate warming might lead to a global-scale feedback mechanism due to enhanced emission of greenhouse gases. This PhD study focuses on the novel development and evaluation of the first global-scale scale thermokarst model (TAPAS), which is applied to simulated climate conditions of the past 6000 years. Thermokarst is a coupled thermal-hydrological process, which leads to an enhanced thawing of ice-rich permafrost on local-to-regional scales. The TAPAS model dynamically simulates the development of thermokarst lakes (TKL) and the aggregation of excess-ice. It is developed for a future implementation into JSBACH, the land component of the Max Plack Institute Earth System Model (MPI ESM). The TAPAS model includes the major steps of thermokarst formation. Little ponds develop after disturbance of the surface heat balance, which can be caused by warm summer temperature, high winter snow depth, wildfires, or flood events. The ponds expand to large lakes because of enhanced melting of excess-ice and thawing of adjacent soils until drainage of the lakes due to connection to subterranean drainage passages or river networks. Then empty basins are left and permafrost recovers again and excess-ice aggregates in the interior of the soil. For present climate, the evaluation of the TAPAS model against satellite based observations shows good agreement of the modelled lake fraction in eastern Siberian lowlands, Lena River delta, Mackenzie River delta, and North Slope of Alaska. The regional distribution of TKL shows potential for improvement because the lake fraction is often overestimated as in Siberia and in northern parts of central Canada. Furthermore several climate scenarios are designed to investigate the responses of thermokarst to climate warming over several millennia. The simulations show that more lakes are formed in periods of climate warming, which agrees well to observations on regional-scale. However, drainage of lakes increases as well because the permafrost area decreases and less excess-ice accumulates, which are the main preconditions for thermokarst. Therefore, a smaller number of TKL is reached on a long-time scale. In summary, the TAPAS model is able to simulate all principal stages of thermokarst cycle that are known from current research on thermokarst and shows long-term variability of lake covered areas in response to climate change. In future applications the TAPAS model can be introduced into an ESM to improve representation of the surface water balance in high latitudes and to receive hydrological boundary data for biochemical modelling of the permafrost carbon cycle. Overall this PhD thesis constitutes the first step to represent thermokarst processes in an ESM.
Enumerating hyperelliptic curves over finite fields in quasilinear time

We present an algorithm that, for every fixed genus $g$, will enumerate all hyperelliptic curves of genus $g$ over a finite field $k$ of odd characteristic in quasilinear time; that is, the time required for the algorithm is $\widetilde{O}(q^{2g-1})$, where $q=\#k$. Such an algorithm already exists in the case $g=2$, thanks to the work of Mestre and Cardona and Quer on reconstructing curves from their Igusa invariants. Experimentally, it appears that our new algorithm is much faster in practice than one based on their work.
Contracting with a Learning Agent

Many real-life contractual relations differ completely from the clean, static model at the heart of principal-agent theory. Typically, they involve repeated strategic interactions of the principal and agent, taking place under uncertainty and over time. While appealing in theory, players seldom use complex dynamic strategies in practice, often preferring to circumvent complexity and approach uncertainty through learning. We initiate the study of repeated contracts with a learning agent, focusing on agents who achieve no-regret outcomes. Optimizing against a no-regret agent is a known open problem in general games; we achieve an optimal solution to this problem for a canonical contract setting, in which the agent's choice among multiple actions leads to success/failure. The solution has a surprisingly simple structure: for some $\alpha>0$, initially offer the agent a linear contract with scalar $\alpha$, then switch to offering a linear contract with scalar $0$. This switch causes the agent to ``free-fall'' through their action space and during this time provides the principal with non-zero reward at zero cost. Despite apparent exploitation of the agent, this dynamic contract can leave \emph{both} players better off compared to the best static contract. Our results generalize beyond success/failure, to arbitrary non-linear contracts which the principal rescales dynamically. Finally, we quantify the dependence of our results on knowledge of the time horizon, and are the first to address this consideration in the study of strategizing against learning agents.
On the Separability Problem of VASS Reachability Languages

We show that the regular separability problem of VASS reachability languages is decidable and $\mathbf{F}_{\omega}$-complete. At the heart of our decision procedure are doubly-marked graph transition sequences, a new proof object that tracks a suitable product of the VASS we wish to separate. We give a decomposition algorithm for DMGTS that not only achieves perfectness as known from MGTS, but also a new property called faithfulness. Faithfulness allows us to construct, from a regular separator for the $\mathbb{Z}$-versions of the VASS, a regular separator for the $\mathbb{N}$-versions. Behind faithfulness is the insight that, for separability, it is sufficient to track the counters of one VASS modulo a large number that is determined by the decomposition.
A Cooper-pair beam splitter as a feasible source of entangled electrons

We investigate the generation of an entangled electron pair emerging from a system composed of two quantum dots attached to a superconductor Cooper pair beam splitter. We take into account three processes: Crossed Andreev Reflection, cotuneling, and Coulomb interaction. Together, these processes play crucial roles in the formation of entangled electronic states, with electrons being in spatially separated quantum dots. By using perturbation theory, we derive an analytical effective model that allows a simple picture of the intricate process behind the formation of the entangled state. Several entanglement quantifiers, including quantum mutual information, negativity, and concurrence, are employed to validate our findings. Finally, we define and calculate the covariance associated with the detection of two electrons, each originating from one of the quantum dots with a specific spin value. The time evolution of this observable follows the dynamics of all entanglement quantifiers, thus suggesting that it can be a useful tool for mapping the creation of entangled electrons in future applications within quantum information protocols.
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept

Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. The broad application of ML and the accelerated pace of its evolution lead to an increasing need for dedicated teaching concepts aimed at making the application of this technology more reliable and responsible. However, teaching ML is a daunting task. Aside from the methodological complexity of ML algorithms, both with respect to theory and implementation, the interdisciplinary and empirical nature of the field need to be taken into consideration. This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines with heterogeneous levels of mathematics, programming and domain expertise. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. These cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.
Solving intractable chemical problems by tensor decomposition

Many complex chemical problems encoded in terms of physics-based models become computationally intractable for traditional numerical approaches due to their unfavourable scaling with increasing molecular size. Tensor decomposition techniques can overcome such challenges by decomposing unattainably large numerical representations of chemical problems into smaller, tractable ones. In the first two decades of this century, algorithms based on such tensor factorizations have become state-of-the-art methods in various branches of computational chemistry, ranging from molecular quantum dynamics to electronic structure theory and machine learning. Here, we consider the role that tensor decomposition schemes have played in expanding the scope of computational chemistry. We relate some of the most prominent methods to their common underlying tensor network formalism, providing a unified perspective on leading tensor-based approaches in chemistry and materials science.
A regularity property of fractional Brownian sheets

A function $f$ defined on $[0, 1]^d$ is called strongly chargeable if there is a continuous vector-field $v$ such that $f(x_1, \dots,x_d)$ equals the flux of $v$ through the rectangle $[0, x_1] \times \cdots \times [0, x_d]$ for all $(x_1, \dots, x_d) \in [0, 1]^d$. In other words, $f$ is the primitive of the divergence of a continuous vector-field. We prove that the sample paths of the Brownian sheet with $d \geq 2$ parameters are almost surely not strongly chargeable. On the other hand, those of the fractional Brownian sheet of Hurst parameter $(H_1, \dots, H_d)$ are shown to be almost surely strongly chargeable whenever \[ \frac{H_1 + \cdots + H_d}{d}>\frac{d - 1}{d}. \]
Understanding the electrochemical performance and diffusion kinetics of HC$||$Na$_3$V$_2$(PO$_4$)$_3$/C full cell battery for energy storage applications

The efficient energy storage devices are crucial to meet the soaring global energy demand for sustainable future. Recently, the sodium-ion batteries (SIBs) have emerged as one of the excellent cost effective solution due to the uniform geographical distribution and abundance of sodium. Here, we use hard carbon (HC) as an anode and Na$_3$V$_2$(PO$_4$)$_3$/C (NVP/C) as a cathode to fabricate a HC$||$NVP/C full cell battery and understand its electrochemical performance and diffusion kinetics. These materials are characterized through the analysis of x-ray diffraction and Raman spectroscopy to confirm their single phase and structure. The full cell demonstrates a high operating voltage of $\sim$3.3 V, with minimal polarization of 0.05 V, attributed to the lower working voltage of the HC. Interestingly, for the full cell battery we find the specific capacity of around 70 mAh/g at 0.1 C and even around 35 mAh/g at high current rate of 5 C along with high rate capability up to 55 cycles. The diffusion kinetics of the full cell battery is investigated through detailed analysis of CV curves at various scan rates, and the diffusion coefficient is found to be 5--8$\times$10$^{-11}$ cm$^2$/s for the anodic as well as cathodic peaks.
Simultaneous design of fermentation and microbe

Constraint-based optimization of microbial strains and model-based bioprocess design have been used extensively to enhance yields in biotechnological processes. However, strain and process optimization are usually carried out in sequential steps, causing underperformance of the biotechnological process when scaling up to industrial fermentation conditions. Herein, we propose the optimization formulation SimulKnock that combines the optimization of a fermentation process with metabolic network design in a bilevel optimization program. The upper level maximizes space-time yield and includes mass balances of a continuous fermentation, while the lower level is based on flux balance analysis. SimulKnock predicts optimal gene deletions and finds the optimal trade-off between growth rate and product yield. Results of a case study with a genome-scale metabolic model of E. coli indicate higher space-time yields than a sequential approach using OptKnock for almost all target products considered. By leveraging SimulKnock, we reduce the gap between strain and process optimization.
PRE: A Peer Review Based Large Language Model Evaluator

The impressive performance of large language models (LLMs) has attracted considerable attention from the academic and industrial communities. Besides how to construct and train LLMs, how to effectively evaluate and compare the capacity of LLMs has also been well recognized as an important yet difficult problem. Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs on different tasks. However, these paradigms often suffer from high cost, low generalizability, and inherited biases in practice, which make them incapable of supporting the sustainable development of LLMs in long term. In order to address these issues, inspired by the peer review systems widely used in academic publication process, we propose a novel framework that can automatically evaluate LLMs through a peer-review process. Specifically, for the evaluation of a specific task, we first construct a small qualification exam to select"reviewers"from a couple of powerful LLMs. Then, to actually evaluate the"submissions"written by different candidate LLMs, i.e., the evaluatees, we use the reviewer LLMs to rate or compare the submissions. The final ranking of evaluatee LLMs is generated based on the results provided by all reviewers. We conducted extensive experiments on text summarization tasks with eleven LLMs including GPT-4. The results demonstrate the existence of biasness when evaluating using a single LLM. Also, our PRE model outperforms all the baselines, illustrating the effectiveness of the peer review mechanism.
On Time-Varying Delayed Stochastic Differential Systems with Non-Markovian Switching Parameters

This paper focuses on time-varying delayed stochastic differential systems with stochastically switching parameters formulated by a unified switching behavior combining a discrete adapted process and a Cox process. Unlike prior studies limited to stationary and ergodic switching scenarios, our research emphasizes non-Markovian, non-stationary, and non-ergodic cases. It arrives at more general results regarding stability analysis with a more rigorous methodology. The theoretical results are validated through numerical examples.
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions

Person re-identification via 3D skeletons is an important emerging research area that triggers great interest in the pattern recognition community. With distinctive advantages for many application scenarios, a great diversity of 3D skeleton based person re-identification (SRID) methods have been proposed in recent years, effectively addressing prominent problems in skeleton modeling and feature learning. Despite recent advances, to the best of our knowledge, little effort has been made to comprehensively summarize these studies and their challenges. In this paper, we attempt to fill this gap by providing a systematic survey on current SRID approaches, model designs, challenges, and future directions. Specifically, we first formulate the SRID problem, and propose a taxonomy of SRID research with a summary of benchmark datasets, commonly-used model architectures, and an analytical review of different methods' characteristics. Then, we elaborate on the design principles of SRID models from multiple aspects to offer key insights for model improvement. Finally, we identify critical challenges confronting current studies and discuss several promising directions for future research of SRID.
DAEDALUS: Defense Against Firmware ROP Exploits Using Stochastic Software Diversity

This paper presents DAEDALUS, a software diversity-based framework designed to resist ROP attacks on Linux-based IoT devices. DAEDALUS generates unique, semantically equivalent but syntactically different rewrites of IoT firmware, disrupting large-scale replication of ROP attacks. DAEDALUS employs STOKE, a stochastic optimizer for x86 binaries, as its core diversity engine but introduces significant extensions to address unique IoT firmware challenges. DAEDALUS's effectiveness is evaluated using DDoSim, a published botnet DDoS attack simulation testbed. Results demonstrate that DAEDALUS successfully neutralizes ROP payloads by diversifying critical basic blocks in the firmware, preventing attackers from compromising multiple devices for DDoS attacks via memory error vulnerabilities. The findings indicate that DAEDALUS not only mitigates the impact of ROP attacks on individual IoT devices through probabilistic protection but also thwarts large-scale ROP attacks across multiple devices.
Kinetic theory of motility induced phase separation for active Brownian particles

When two active Brownian particles collide, they slide along each other until they can continue their free motion. For persistence lengths much larger than the particle diameter, the directors do not change, but the collision can be modeled as producing a net displacement on the particles compared to their free motion in the absence of the encounter. With these elements, a Boltzmann--Enskog-like kinetic theory is built. A linear stability analysis of the homogeneous state predicts a density instability resulting from the effective velocity reduction of tagged particles predicted by the theory.
Asymptotic properties of Vecchia approximation for Gaussian processes

Vecchia approximation has been widely used to accurately scale Gaussian-process (GP) inference to large datasets, by expressing the joint density as a product of conditional densities with small conditioning sets. We study fixed-domain asymptotic properties of Vecchia-based GP inference for a large class of covariance functions (including Mat\'ern covariances) with boundary conditioning. In this setting, we establish that consistency and asymptotic normality of maximum exact-likelihood estimators imply those of maximum Vecchia-likelihood estimators, and that exact GP prediction can be approximated accurately by Vecchia GP prediction, given that the size of conditioning sets grows polylogarithmically with the data size. Hence, Vecchia-based inference with quasilinear complexity is asymptotically equivalent to exact GP inference with cubic complexity. This also provides a general new result on the screening effect. Our findings are illustrated by numerical experiments, which also show that Vecchia approximation can be more accurate than alternative approaches such as covariance tapering and reduced-rank approximations.
A Bayesian multivariate extreme value mixture model

Impact assessment of natural hazards requires the consideration of both extreme and non-extreme events. Extensive research has been conducted on the joint modeling of bulk and tail in univariate settings; however, the corresponding body of research in the context of multivariate analysis is comparatively scant. This study extends the univariate joint modeling of bulk and tail to the multivariate framework. Specifically, it pertains to cases where multivariate observations exceed a high threshold in at least one component. We propose a multivariate mixture model that assumes a parametric model to capture the bulk of the distribution, which is in the max-domain of attraction (MDA) of a multivariate extreme value distribution (mGEVD). The tail is described by the multivariate generalized Pareto distribution, which is asymptotically justified to model multivariate threshold exceedances. We show that if all components exceed the threshold, our mixture model is in the MDA of an mGEVD. Bayesian inference based on multivariate random-walk Metropolis-Hastings and the automated factor slice sampler allows us to incorporate uncertainty from the threshold selection easily. Due to computational limitations, simulations and data applications are provided for dimension $d=2$, but a discussion is provided with views toward scalability based on pairwise likelihood.
Accelerated intermetallic phase amorphization in a Mg-based high-entropy alloy powder

High-energy mechanical milling is employed to synthesize a stable high-entropy alloy powder from an otherwise immiscible Mg-Ti rich metallic mixture. Predictions from first principles and materials theory corroborate the results from microscopic characterizations that reveal a transition of the amorphous phase from a precursor intermetallic structure by antisite disorder, lattice expansion and nanograin boundaries. This phase transformation is accelerated by the presence of multi principal elements that induce severe lattice distortion due to the high-configurational entropy. The atomic size mismatch of the dissimilar elements present in the alloy produces a stable amorphous phase that resists reverting to an ordered lattice even on annealing.
Deep Embedding Clustering Driven by Sample Stability

Deep clustering methods improve the performance of clustering tasks by jointly optimizing deep representation learning and clustering. While numerous deep clustering algorithms have been proposed, most of them rely on artificially constructed pseudo targets for performing clustering. This construction process requires some prior knowledge, and it is challenging to determine a suitable pseudo target for clustering. To address this issue, we propose a deep embedding clustering algorithm driven by sample stability (DECS), which eliminates the requirement of pseudo targets. Specifically, we start by constructing the initial feature space with an autoencoder and then learn the cluster-oriented embedding feature constrained by sample stability. The sample stability aims to explore the deterministic relationship between samples and all cluster centroids, pulling samples to their respective clusters and keeping them away from other clusters with high determinacy. We analyzed the convergence of the loss using Lipschitz continuity in theory, which verifies the validity of the model. The experimental results on five datasets illustrate that the proposed method achieves superior performance compared to state-of-the-art clustering approaches.
Look Around! Unexpected gains from training on environments in the vicinity of the target

Solutions to Markov Decision Processes (MDP) are often very sensitive to state transition probabilities. As the estimation of these probabilities is often inaccurate in practice, it is important to understand when and how Reinforcement Learning (RL) agents generalize when transition probabilities change. Here we present a new methodology to evaluate such generalization of RL agents under small shifts in the transition probabilities. Specifically, we evaluate agents in new environments (MDPs) in the vicinity of the training MDP created by adding quantifiable, parametric noise into the transition function of the training MDP. We refer to this process as Noise Injection, and the resulting environments as $\delta$-environments. This process allows us to create controlled variations of the same environment with the level of the noise serving as a metric of distance between environments. Conventional wisdom suggests that training and testing on the same MDP should yield the best results. However, we report several cases of the opposite -- when targeting a specific environment, training the agent in an alternative noise setting can yield superior outcomes. We showcase this phenomenon across $60$ different variations of ATARI games, including PacMan, Pong, and Breakout.