Miracle-dz/UniFAR-FTUs huggingface dataset:
Tunable and low-loss hyperbolic plasmon polaritons in T$_{d}\,$-WTe$_2$ single layer

Natural hyperbolic two-dimensional systems are fascinating new class of materials that could open new pathways to manipulate plasmon propagation and light-matter interactions. Here, we present a comprehensive study of optical response in T$_d\,$-WTe$_2$ by means of density functional and many-body perturbation theories. We show how monolayer WTe$_2$ with in-plane anisotropy sustains hyperbolic plasmon polaritons, which can be tuned via chemical doping and strain. The latter is able to extend the hyperbolic regime towards near-infrared with low losses. Moreover, with a moderate strain, WTe$_2$ can even be switched between elliptic and hyperbolic regimes. In addition, plasmons in WTe$_2$ are characterized with low losses owing to electron-phonon scatterings, which are responsible for the temperature dependence of plasmon linewidth. Interestingly, temperature can also be utilized to tune in-plane anisotropy of WTe$_2$ optical response.
Where Responsible AI meets Reality

Large and ever-evolving technology companies continue to invest more time and resources to incorporate responsible Artificial Intelligence (AI) into production-ready systems to increase algorithmic accountability. This paper examines and seeks to offer a framework for analyzing how organizational culture and structure impact the effectiveness of responsible AI initiatives in practice. We present the results of semi-structured qualitative interviews with practitioners working in industry, investigating common challenges, ethical tensions, and effective enablers for responsible AI initiatives. Focusing on major companies developing or utilizing AI, we have mapped what organizational structures currently support or hinder responsible AI initiatives, what aspirational future processes and structures would best enable effective initiatives, and what key elements comprise the transition from current work practices to the aspirational future.
A Step Towards Interpretable Authorship Verification

A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author. Authorship verification (AV) is a research branch in this field that deals with this question. Over the years, research activities in the context of AV have steadily increased, which has led to a variety of approaches trying to solve this problem. Many of these approaches, however, make use of features that are related to or influenced by the topic of the documents. Therefore, it may accidentally happen that their verification results are based not on the writing style (the actual focus of AV), but on the topic of the documents. To address this problem, we propose an alternative AV approach that considers only topic-agnostic features in its classification decision. In addition, we present a post-hoc interpretation method that allows to understand which particular features have contributed to the prediction of the proposed AV method. To evaluate the performance of our AV method, we compared it with ten competing baselines (including the current state of the art) on four challenging data sets. The results show that our approach outperforms all baselines in two cases (with a maximum accuracy of 84%), while in the other two cases it performs as well as the strongest baseline.
Graph Learning for Inverse Landscape Genetics

The problem of inferring unknown graph edges from numerical data at a graph's nodes appears in many forms across machine learning. We study a version of this problem that arises in the field of landscape genetics, where genetic similarity between organisms living in a heterogeneous landscape is explained by a weighted graph that encodes the ease of dispersal through that landscape. Our main contribution is an efficient algorithm for inverse landscape genetics, which is the task of inferring this graph from measurements of genetic similarity at different locations (graph nodes). Inverse landscape genetics is important in discovering impediments to species dispersal that threaten biodiversity and long-term species survival. In particular, it is widely used to study the effects of climate change and human development. Drawing on influential work that models organism dispersal using graph effective resistances (McRae 2006), we reduce the inverse landscape genetics problem to that of inferring graph edges from noisy measurements of these resistances, which can be obtained from genetic similarity data. Building on the NeurIPS 2018 work of Hoskins et al. (2018) on learning edges in social networks, we develop an efficient first-order optimization method for solving this problem. Despite its non-convex nature, experiments on synthetic and real genetic data establish that our method provides fast and reliable convergence, significantly outperforming existing heuristics used in the field. By providing researchers with a powerful, general purpose algorithmic tool, we hope our work will have a positive impact on accelerating work on landscape genetics.
Temporal evolution of the European forest sector carbon sink from 1950 to 1999

Estimates of the role of the European terrestrial biosphere in the global carbon cycle still vary by a factor 10. This is due to differences in methods and assumptions employed, but also due to difference in reference periods of the studies. The magnitude of the sink varies between years because of inter‐annual variation of short‐term climate, but also due to long‐term trends in development of the vegetation and its management. For this purpose, we present the results of an application of a carbon bookkeeping model to the forest sector of the European forests from 1950 to 1999. The analysis includes the compartments trees, soils, and wood products. The model uses statistics on European (30 countries excl. CIS) stemwood volume increment, forest area change, fellings, wood products and their international trade, and natural disturbances, supplemented with conversion coefficients, soil parameters and information on management.
Isolation of cancer-derived extracellular vesicle subpopulations by a size-selective microfluidic platform

Extracellular vesicles (EVs) play an important role in intercellular communication. Recently, there has been increasing interest in EVs as potential diagnostic biomarkers and therapeutic vehicles. However, the molecular properties and cargo information of EV subpopulations have not yet been fully investigated due to lack of reliable and reproducible EV separation technology. Current approaches have faced difficulties with efficiently isolating EVs from biofluids, especially subpopulations of small EVs. Here, we report an EV isolation method based on a size-selective microfluidic platform (ExoSMP) via nanomembrane filtration and electrophoretic force. This unique platform offers an enhanced approach to sorting a heterogeneous population of EVs based on size, with the additional advantages of being label-free and low-cost, and featuring a short processing time (<1 h), and convenient integration with downstream analysis. In this research, we used ExoSMP to demonstrate the isolation of cancer-derived small EVs (30–120 nm) with high recovery (94.2%) and reproducibility at an optimum sample flow rate. Furthermore, we investigated isolation of EV subpopulations by altering nanomembrane combinations with different pore size combinations (50 and 100 nm, 30 and 100 nm, 30 and 200 nm, and 30 and 50 nm). This ExoSMP technique can serve as a standardized EV isolation/separation tool, facilitating the clinical prospects of EVs and opening up a new avenue for future point-of-care applications in liquid biopsies.
Dynamic Information Flow Tracking for Detection of Advanced Persistent Threats: A Stochastic Game Approach

Advanced Persistent Threats (APTs) are stealthy customized attacks by intelligent adversaries. This paper deals with the detection of APTs that infiltrate cyber systems and compromise specifically targeted data and/or infrastructures. Dynamic information flow tracking is an information trace-based detection mechanism against APTs that taints suspicious information flows in the system and generates security analysis for unauthorized use of tainted data. In this paper, we develop an analytical model for resource-efficient detection of APTs using an information flow tracking game. The game is a nonzero-sum, turn-based, stochastic game with asymmetric information as the defender cannot distinguish whether an incoming flow is malicious or benign and hence has only partial state observation. We analyze equilibrium of the game and prove that a Nash equilibrium is given by a solution to the minimum capacity cut set problem on a flow-network derived from the system, where the edge capacities are obtained from the cost of performing security analysis. Finally, we implement our algorithm on the real-world dataset for a data exfiltration attack augmented with false-negative and false-positive rates and compute an optimal defender strategy.
Holomorphically finitely generated Hopf algebras and quantum Lie groups

We study topological Hopf algebras that are holomorphically finitely generated (HFG) as Frechet Arens-Micheal algebras in the sense of Pirkovskii. Some of them but not all can be obtained from affine Hopf algebras by an application of the analytization functor. We find that a commutative HFG Hopf algebra is always an algebra of holomorphic functions on a complex Lie (in fact, Stein) group and prove that the corresponding categories are equivalent. Akbarov associated with a compactly generated complex Lie group $G$ the cocommutative topological Hopf algebra ${\mathscr A}_{exp}(G)$ of exponential analytic functionals. We show that it is HFG but not every cocommutative HFG Hopf algebra is of this form. In the case when $G$ is connected, a structure theorem based on previous results of the author is established and the analytic structure of ${\mathscr A}_{exp}(G)$ (that depends on the large-scale geometry of $G$) is described. We also consider some interesting examples including complex-analytic forms of classical $\hbar$-adic quantum groups.
The Generalized Lasso with Nonlinear Observations and Generative Priors

In this paper, we study the problem of signal estimation from noisy non-linear measurements when the unknown $n$-dimensional signal is in the range of an $L$-Lipschitz continuous generative model with bounded $k$-dimensional inputs. We make the assumption of sub-Gaussian measurements, which is satisfied by a wide range of measurement models, such as linear, logistic, 1-bit, and other quantized models. In addition, we consider the impact of adversarial corruptions on these measurements. Our analysis is based on a generalized Lasso approach (Plan and Vershynin, 2016). We first provide a non-uniform recovery guarantee, which states that under i.i. d. ~Gaussian measurements, roughly $O\left(\frac{k}{\epsilon^2}\log L\right)$ samples suffice for recovery with an $\ell_2$-error of $\epsilon$, and that this scheme is robust to adversarial noise. Then, we apply this result to neural network generative models, and discuss various extensions to other models and non-i.i. d. ~measurements. Moreover, we show that our result can be extended to the uniform recovery guarantee under the assumption of a so-called local embedding property, which is satisfied by the 1-bit and censored Tobit models.
PCR-based Synthesis of Codon Optimized cry2Aa Gene for Production of Shoot and Fruit Borer (Leucinodes orbonalis) Resistant Eggplant (Solanum melongena L.) Cultivars

II ACKNOWLEDGMENT IV TABLE OF CONTENTS VI LIST OF TABLES VIII LIST OF FIGURES IX Chapter 1: Introduction 1 Mode of damage by the borer 2 Symptoms of damage 2 cry genes 3 Mode of action of cry genes 5 Transformation of eggplant 6 Codon optimization 7 PCR-mediated gene synthesis 9 Dual asymmetric (DA) PCR 10 Overlap extension (OE) PCR 11 Purpose 11 References 13 Chapter 2: Transformation of eggplant 22 Introduction 22 Materials and methods 23 Plant Material 23 Seed Sterilization 24 Seed Germination 24 Preparation of Agrobacterium 24 Transformation 25 1) Leaf Disc Method 25 2) Transformation of the stumps 27 GFP testing of primary transformants 27 PCR analysis 28 Results 29 Transformation results 29 GFP expression results 30 Integration of T-DNA in T0 plants 30 GFP expression in T1 progeny 31 Discussion 32 References 36 Chapter 3: PCR mediated optimization of cry2Aa gene for expression in eggplant 58 Introduction 58 Materials and methods 60 Synthetic gene design 61
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping

Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem. The current methods assume that a single low-resolution image can only yield a single high-resolution image. In addition, all of these methods use low-resolution images that were artificially generated through simple bilinear down-sampling. We argue that, first and foremost, the problem of SISR is an one-to-many mapping problem between the low-resolution and all possible candidate high-resolution images and we address the challenging task of learning how to realistically degrade and down-sample high-resolution images. To circumvent this problem, we propose SR-NAM which utilizes the Non-Adversarial Mapping (NAM) technique. Furthermore, we propose a degradation model that learns how to transform high-resolution images to low-resolution images that resemble realistically taken low-resolution photos. Finally, some qualitative results for the proposed method along with the weaknesses of SR-NAM are included.
String baryon in four-dimensional 
N=2
 supersymmetric QCD from the 2D-4D correspondence

We study non-Abelian vortex strings in four-dimensional (4D) $\mathcal{N}=2$ supersymmetric QCD with the $\mathrm{U}(N=2)$ gauge group and ${N}_{f}=4$ flavors of quark hypermultiplets. It has recently been shown that these vortices behave as critical superstrings. The spectrum of closed string states in the associated string theory was found and interpreted as a spectrum of hadrons in 4D $\mathcal{N}=2$ supersymmetric QCD. In particular, the lowest string state appears to be a massless Bogomol'nyi-Prasad-Sommerfield (BPS) ``baryon. '' Here we show the occurrence of this stringy baryon using a purely field-theoretic method. To this end we study the conformal world-sheet theory on the non-Abelian string---the so-called weighted $\mathcal{N}=(2,2)$ supersymmetric $\mathbb{C}\mathbb{P}$ model. Its target space is given by the six-dimensional noncompact Calabi-Yau space ${Y}_{6}$, the conifold. We use a mirror description of the model to study the BPS kink spectrum and its transformations on curves (walls) of marginal stability. Then we use the 2D-4D correspondence to show that the deformation of the complex structure of the conifold is associated with the emergence of a nonperturbative Higgs branch in 4D theory which opens up at strong coupling. The modulus parameter on this Higgs branch is the vacuum expectation value of the massless BPS baryon previously found in string theory.
On Optimism in Model-Based Reinforcement Learning

The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL), often coming with strong theoretical guarantees. However, it remains a challenge to scale these approaches to the deep RL paradigm, which has achieved a great deal of attention in recent years. In this paper, we introduce a tractable approach to optimism via noise augmented Markov Decision Processes (MDPs), which we show can obtain a competitive regret bound: $\tilde{\mathcal{O}}( |\mathcal{S}|H\sqrt{|\mathcal{S}||\mathcal{A}| T } )$ when augmenting using Gaussian noise, where $T$ is the total number of environment steps. This tractability allows us to apply our approach to the deep RL setting, where we rigorously evaluate the key factors for success of optimistic model-based RL algorithms, bridging the gap between theory and practice.
Recurrent Neutrino Emission from Supermassive Black Hole Mergers

The recent detection of possible neutrino emission from the blazar TXS 0506+056 was the first high-energy neutrino associated with an astrophysical source, making this special type of active galaxies a promising neutrino emitter. The fact that two distinct episodes of neutrino emission were detected with a separation of around 3 yr raises the possibility that emission could be periodic. Periodic emission is expected from supermassive binary black hole systems due to jet precession close to the binary’s merger. Here, we show that, if TXS 0506+056 is a binary source, then the next neutrino flare could already have occurred, possibly still hidden in IceCube’s not-yet-analyzed data. We derive the binary properties that would lead to the detection of gravitational waves from this system by the Laser Interferometer Space Antenna (LISA) over the next decade. Our results for the first time quantify the timescale of these correlations for the example of TXS 0506+056, connecting the possible neutrino and gravitational-wave signatures of such sources.
A polynomial time and space heuristic algorithm for T-count

An important part of reaping computational advantage from a quantum computer is to reduce the quantum resources needed to implement a desired quantum algorithm. Quantum algorithms that are too large to be practical on noisy intermediate scale quantum devices will require fault-tolerant error correction. This work focuses on reducing the physical cost of implementing quantum algorithms when using the state-of-the-art fault-tolerant quantum error correcting codes, in particular, those for which implementing the T gate consumes vastly more resources than the other gates in the gate set. More specifically, in this paper we consider the group of unitaries that can be exactly implemented by a quantum circuit consisting of the Clifford + T gate set. The Clifford + T gate set is a universal gate set and in this group, using state-of-the-art surface codes, the T gate is by far the most expensive component to implement fault-tolerantly. So it is important to minimize the number of T gates necessary for a fault-tolerant implementation. Our primary interest is to compute a circuit for a given n-qubit unitary U, using the minimum possible number of T gates (called the T-count of unitary U). We consider the problem COUNT-T, the optimization version of which aims to find the T-count of U. In its decision version the goal is to decide if the T-count is at most some positive integer m. Given an oracle for COUNT-T, we can compute a T-count-optimal circuit in time polynomial in the T-count and dimension of U. We give a provable classical algorithm that solves COUNT-T (decision) in time ON2(c−1)⌈mc⌉poly(m,N) and space ON2⌈mc⌉poly(m,N) , where N = 2 n and c ⩾ 2. This gives a space-time trade-off for solving this problem with variants of meet-in-the-middle techniques. We also introduce an asymptotically faster multiplication method that shaves a factor of N 0.7457 off of the overall complexity. Lastly, beyond our improvements to the rigorous algorithm, we give a heuristic algorithm that outputs a T-count-optimal circuit and has space and time complexity poly(m, N), under some assumptions. In our heuristic algorithm we developed a novel way of pruning the search space. While our heuristic method still scales exponentially with the number of qubits (though with a lower exponent), there is a large improvement by going from exponential to polynomial scaling with m. We implemented our heuristic algorithm with up to 4 qubit unitaries and obtained a significant improvement in time. For all benchmark and random unitaries we studied, the T-count returned by our algorithm is at most the T-count of their circuits shown in previous papers.
Near-Optimal Reinforcement Learning with Self-Play

This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn the optimal policy by playing against itself without any direct supervision. In a tabular episodic Markov game with $S$ states, $A$ max-player actions and $B$ min-player actions, the best existing algorithm for finding an approximate Nash equilibrium requires $\tilde{\mathcal{O}}(S^2AB)$ steps of game playing, when only highlighting the dependency on $(S,A,B)$. In contrast, the best existing lower bound scales as $\Omega(S(A+B))$ and has a significant gap from the upper bound. This paper closes this gap for the first time: we propose an optimistic variant of the \emph{Nash Q-learning} algorithm with sample complexity $\tilde{\mathcal{O}}(SAB)$, and a new \emph{Nash V-learning} algorithm with sample complexity $\tilde{\mathcal{O}}(S(A+B))$. The latter result matches the information-theoretic lower bound in all problem-dependent parameters except for a polynomial factor of the length of each episode. In addition, we present a computational hardness result for learning the best responses against a fixed opponent in Markov games---a learning objective different from finding the Nash equilibrium.
How to Use Fewer Markers in Admixture Studies

Summary Swiss Fleckvieh has been established from 1970 as a composite of Simmental and Red Holstein Friesian cattle. Breed composition is currently reported based on pedigree information. Information on ancestry informative molecular markers potentially provides more accurate information. For the analysis Illumina Bovine SNP50 Beadchip data for 495 bulls were used. Markers were selected based on diff erence in allele frequencies in the pure populations, using FST as an indicator. Performance of sets with decreasing number of markers was compared. Th e scope of the study was to see how much we can reduce the number of markers based on FST to get a reliability that is close to that with the full set of markers. On these sets of markers hidden Markov models (HMM) and methods used in genomic selection (BayesB, partial least squares regression, LASSO variable selection) were applied. Correlations of admixture levels were estimated and compared with admixture levels based on pedigree information. FST chosen SNP gave very high correlations with pedigree based admixture. Only when using 96 and 48 SNP with the highest F ST , correlations dropped to 0.92 and 0.90, respectively.
A mechanistic model for describing dynamic multi-nutrient, light, temperature interactions in phytoplankton

Developed by combining components simplified from previously developed mechanistic models, a complete dynamic model is described for simulating the growth of phytoplankton as functions of ammonium, nitrate, light, iron, silicon, phosphorus and temperature. Components may be safely added or deleted to the base model, describing ammonium‐nitrate‐light interactions, to suit particular modelling scenarios. Biomass is described in terms of C and cells, while chlorophyll is also a state variable enabling the simulation of changes in Chl a:C with photoacclimation. The model is capable of simulating variable silicon deposition (diatoms) and C cell ‐1 with Si, Fe, or P limitations. Mechanisms for inclusion of temperature control of nutrient transport, growth rate and cell size are given. The model is suitable for placement in ecosystem models, containing various components that can be readily modified to tune the simulation to mimic the behaviour of specific algal groups or species. Most of those components have biological significance and can be estimated from experiments or by analysis of existing data.