Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasi-freestanding AA-stacked bilayer graphene induced by calcium intercalation of the graphene-silicon carbide interface

We study quasi-freestanding bilayer graphene on silicon carbide intercalated by calcium. The intercalation, and subsequent changes to the system, were investigated by low-energy electron diffraction, angle-resolved photoemission spectroscopy (ARPES) and density-functional theory (DFT). Calcium is found to intercalate only at the graphene-SiC interface, completely displacing the hydrogen terminating SiC. As a consequence, the system becomes highly n-doped. Comparison to DFT calculations shows that the band dispersion, as determined by ARPES, deviates from the band structure expected for Bernal-stacked bilayer graphene. Instead, the electronic structure closely matches AA-stacked bilayer graphene on calcium-terminated SiC, indicating a spontaneous transition from AB- to AA-stacked bilayer graphene following calcium intercalation of the underlying graphene-SiC interface.
A Novel Catalyst Scheme for Stochastic Minimax Optimization

This paper presents a proximal-point-based catalyst scheme for simple first-order methods applied to convex minimization and convex-concave minimax problems. In particular, for smooth and (strongly)-convex minimization problems, the proposed catalyst scheme, instantiated with a simple variant of stochastic gradient method, attains the optimal rate of convergence in terms of both deterministic and stochastic errors. For smooth and strongly-convex-strongly-concave minimax problems, the catalyst scheme attains the optimal rate of convergence for deterministic and stochastic errors up to a logarithmic factor. To the best of our knowledge, this reported convergence seems to be attained for the first time by stochastic first-order methods in the literature. We obtain this result by designing and catalyzing a novel variant of stochastic extragradient method for solving smooth and strongly-monotone variational inequality, which may be of independent interest.
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval

The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist between queries and relevant documents, previous work tries to combine multiple retrieval models to find as many relevant results as possible. The constructed ensembles, whether learned independently or jointly, do not care which component model is more suitable to an instance during training. Thus, they cannot fully exploit the capabilities of different types of retrieval models in identifying diverse relevance patterns. Motivated by this observation, in this paper, we propose a Mixture-of-Experts (MoE) model consisting of representative matching experts and a novel competitive learning mechanism to let the experts develop and enhance their expertise during training. Specifically, our MoE model shares the bottom layers to learn common semantic representations and uses differently structured upper layers to represent various types of retrieval experts. Our competitive learning mechanism has two stages: (1) a standardized learning stage to train the experts equally to develop their capabilities to conduct relevance matching; (2) a specialized learning stage where the experts compete with each other on every training instance and get rewards and updates according to their performance to enhance their expertise on certain types of samples. Experimental results on three retrieval benchmark datasets show that our method significantly outperforms the state-of-the-art baselines.
Resource savings from fault-tolerant circuit design

Using fault-tolerant constructions, computations performed with unreliable components can simulate their noiseless counterparts though the introduction of a modest amount of redundancy. Given the modest overhead required to achieve fault-tolerance, and the fact that increasing the reliability of basic components often comes at a cost, are there situations where fault-tolerance may be more economical? We present a general framework to account for this overhead cost in order to effectively compare fault-tolerant to non-fault-tolerant approaches for computation, in the limit of small logical error rates. Using this detailed accounting, we determine explicit boundaries at which fault-tolerant designs become more efficient than designs that achieve comparable reliability through direct consumption of resources. We find that the fault-tolerant construction is always preferred in the limit of high reliability in cases where the resources required to construct a basic unit grows faster than $\log(1 / \epsilon)$ asymptotically for small $\epsilon$.
T-Duality and Flavor Symmetries in Little String Theories

We explore the T-duality web of 6D Heterotic Little String Theories, focusing on flavor algebra reducing deformations. A careful analysis of the full flavor algebra, including Abelian factors, shows that the flavor rank is preserved under T-duality. This suggests a new T-duality invariant in addition to the Coulomb branch dimension and the two-group structure constants. We also engineer Little String Theories with non-simply laced flavor algebras, whose appearance we attribute to certain discrete 3-form fluxes in M-theory. Geometrically, these theories are engineered in F-theory with non-K\"ahler favorable K3 fibers. This geometric origin leads us to propose that freezing fluxes are preserved across T-duality. Along the way, we discuss various exotic models, including two inequivalent $\text{Spin(32)}/\mathbb{Z}_2$ models that are dual to the same $\text{E}_8 \times \text{E}_8$ theory, and a family of self-T-dual models.
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources

Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open-world classification. However, it is typically hard to collect real out-of-distribution (OOD) data for training a predictor capable of discerning ID and OOD patterns. This obstacle gives rise to data generation-based learning methods, synthesizing OOD data via data generators for predictor training without requiring any real OOD data. Related methods typically pre-train a generator on ID data and adopt various selection procedures to find those data likely to be the OOD cases. However, generated data may still coincide with ID semantics, i.e., mistaken OOD generation remains, confusing the predictor between ID and OOD data. To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection. Specifically, we can ensure that learning from such an auxiliary task is beneficial if the ID and the OOD parts have disjoint supports, with the help of a well-designed training procedure for the predictor. Accordingly, we propose a powerful data generation-based learning method named Auxiliary Task-based OOD Learning (ATOL) that can relieve the mistaken OOD generation. We conduct extensive experiments under various OOD detection setups, demonstrating the effectiveness of our method against its advanced counterparts.
FastLog: An End-to-End Method to Efficiently Generate and Insert Logging Statements

Logs play a crucial role in modern software systems, serving as a means for developers to record essential information for future software maintenance. As the performance of these log-based maintenance tasks heavily relies on the quality of logging statements, various works have been proposed to assist developers in writing appropriate logging statements. However, these works either only support developers in partial sub-tasks of this whole activity; or perform with a relatively high time cost and may introduce unwanted modifications. To address their limitations, we propose FastLog, which can support the complete logging statement generation and insertion activity, in a very speedy manner. Specifically, given a program method, FastLog first predicts the insertion position in the finest token level, and then generates a complete logging statement to insert. We further use text splitting for long input texts to improve the accuracy of predicting where to insert logging statements. A comprehensive empirical analysis shows that our method outperforms the state-of-the-art approach in both efficiency and output quality, which reveals its great potential and practicality in current real-time intelligent development environments.
Systematic conservation planning in human-dominated landscapes : maximizing efficiency in biodiversity conservation via carbon sequestration and land management

Investments in habitat and biodiversity conservation are critically needed given mounting effects of global climate change and unprecedented rates of ecological degradation and species extinction. However, as more regions of the world are converted to human use, we are also experiencing a shift in the traditional targets of conservation from protecting ‘ecologically intact’ landscapes to restoring degraded habitat by prioritizing conservation investments under triage. The overall goal of this thesis was to evaluate alternative ways of funding conservation initiatives. To reach that goal, I first used 1,770 avian point counts in a 2,520 km2 study area, remote-sensed data and models incorporating imperfect detectability to predict habitat occupancy in 47 widely-distributed native birds, which were also classified by experts according to their habitat association. Forest and Savannah association scores for these species were then used as weights in a composite distribution map of species communities. My results showed that composite maps of widespread indicators improve site prioritization by incorporating the behavioural and demographic responses of a diverse range of indicators to variation in patch size, configuration and adjacent human land use. Using these composite maps, I asked how the sale of forest carbon credits could reduce land acquisition costs, and how the alternate goals of maximizing α or β-diversity in focal communities could affect the prioritization of parcels for acquisition. My results indicate that carbon sales have the potential to enhance conservation outcomes in human-dominated landscapes by reducing the net acquisition costs of land conservation. Maximizing β versus α-diversity may further reduce costs by reducing the total area required to meet conservation targets and enhancing landscape heterogeneity. In cases where land purchase is not an option, private land conservation covenants can provide an alternative; although serious questions exist about long-term monitoring and enforcement costs of covenants given the risk that owners might violate or challenge them in court. My findings suggest that violation or dispute rate can substantially affect long-term costs of covenants and potentially surpass the cost of land purchase. Overall, I tested several ways to successfully fund conservation investments and highlight potential benefits and shortfalls of each.
On free boundary minimal submanifolds in geodesic balls in $\mathbb H^n$ and $\mathbb S^n_+$

We consider free boundary minimal submanifolds in geodesic balls in the hyperbolic space $\mathbb H^n$ and in the round upper hemisphere $\mathbb S^n_+$. Recently, Lima and Menezes have found a connection between free boundary minimal surfaces in geodesic balls in $\mathbb S^n_+$ and maximal metrics for a functional, defined on the set of Riemannian metrics on a given compact surface with boundary. This connection is similar to the connection between free boundary minimal submanifolds in Euclidean balls and the critical metrics of the functional \say{the $k$-th normalized Steklov eigenvalue}, introduced by Faser and Schoen. We define two natural functionals on the set of Riemannian metrics on a compact surface with boundary. One of these functionals is the high order generalization of the functional, introduced by Lima and Menezes. We prove that the critical metrics for these functionals arise as metrics induced by free boundary minimal immersions in geodesic balls in $\mathbb H^n$ and in $\mathbb S^n_+$, respectively. We also prove a converse statement. Besides that, we discuss the (Morse) index of free boundary minimal submanifolds in geodesic balls in $\mathbb H^n$ or $\mathbb S^n_+$. We show that the index of the critical spherical catenoids in these spaces is 4 and the index of a geodesic $k$-ball is $2(n-k)$. For the proof of these statements we introduce the notion of spectral index similarly to the case of free boundary minimal submanifolds in a unit ball in the Euclidean space.
Automated Camera Calibration via Homography Estimation with GNNs

Over the past few decades, a significant rise of camera-based applications for traffic monitoring has occurred. Governments and local administrations are increasingly relying on the data collected from these cameras to enhance road safety and optimize traffic conditions. However, for effective data utilization, it is imperative to ensure accurate and automated calibration of the involved cameras. This paper proposes a novel approach to address this challenge by leveraging the topological structure of intersections. We propose a framework involving the generation of a set of synthetic intersection viewpoint images from a bird's-eye-view image, framed as a graph of virtual cameras to model these images. Using the capabilities of Graph Neural Networks, we effectively learn the relationships within this graph, thereby facilitating the estimation of a homography matrix. This estimation leverages the neighbourhood representation for any real-world camera and is enhanced by exploiting multiple images instead of a single match. In turn, the homography matrix allows the retrieval of extrinsic calibration parameters. As a result, the proposed framework demonstrates superior performance on both synthetic datasets and real-world cameras, setting a new state-of-the-art benchmark.
Preserving Privacy in GANs Against Membership Inference Attack

Generative Adversarial Networks (GANs) have been widely used for generating synthetic data for cases where there is a limited size real-world data set or when data holders are unwilling to share their data samples. Recent works showed that GANs, due to overfitting and memorization, might leak information regarding their training data samples. This makes GANs vulnerable to Membership Inference Attacks (MIAs). Several defense strategies have been proposed in the literature to mitigate this privacy issue. Unfortunately, defense strategies based on differential privacy are proven to reduce extensively the quality of the synthetic data points. On the other hand, more recent frameworks such as PrivGAN and PAR-GAN are not suitable for small-size training data sets. In the present work, the overfitting in GANs is studied in terms of the discriminator, and a more general measure of overfitting based on the Bhattacharyya coefficient is defined. Then, inspired by Fano’s inequality, our first defense mechanism against MIAs is proposed. This framework, which requires only a simple modification in the loss function of GANs, is referred to as the maximum entropy GAN or MEGAN and significantly improves the robustness of GANs to MIAs. As a second defense strategy, a more heuristic model based on minimizing the information leaked from the generated samples about the training data points is presented. This approach is referred to as mutual information minimization GAN (MIMGAN) and uses a variational representation of the mutual information to minimize the information that a synthetic sample might leak about the whole training data set. Applying the proposed frameworks to some commonly used data sets against state-of-the-art MIAs reveals that the proposed methods can reduce the accuracy of the adversaries to the level of random guessing accuracy with a small reduction in the quality of the synthetic data samples.
Environmental degradation, poverty, and sustainable development : a case study of rural Mexico and the community of Ayotitlán

This thesis was written by Darcy Tetreault. It is entitled “Environmental Degradation, Poverty, and Sustainable Development; A Case Study of Rural Mexico and the Community of Ayotitlan. ” It begins by exploring the theoretical issues surrounding sustainable development; this is followed by a study of rural Mexico that investigates links between immediate causes of environmental degradation and broader social, economic, and political conditions; and it ends with a case study of a marginalized indigenous community within rural Mexico: Ayotitlan. The findings of this research suggest that the model of development that is currently being pursued by the Mexican government is not sustainable (either ecologically or socially), and that, in order to overcome the countiy’s serious environmental problems, major structural adjustments need to be made so as to give marginalized people more control over their local natural resources, and more say in national-level decision-making.
Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data

Numbers are crucial for various real-world domains such as finance, economics, and science. Thus, understanding and reasoning with numbers are essential skills for language models to solve different tasks. While different numerical benchmarks have been introduced in recent years, they are limited to specific numerical aspects mostly. In this paper, we propose a hierarchical taxonomy for numerical reasoning skills with more than ten reasoning types across four levels: representation, number sense, manipulation, and complex reasoning. We conduct a comprehensive evaluation of state-of-the-art models to identify reasoning challenges specific to them. Henceforth, we develop a diverse set of numerical probes employing a semi-automated approach. We focus on the tabular Natural Language Inference (TNLI) task as a case study and measure models' performance shifts. Our results show that no model consistently excels across all numerical reasoning types. Among the probed models, FlanT5 (few-/zero-shot) and GPT-3.5 (few-shot) demonstrate strong overall numerical reasoning skills compared to other models. Label-flipping probes indicate that models often exploit dataset artifacts to predict the correct labels.
Data Science from 1963 to 2012

Consensus on the definition of data science remains low despite the widespread establishment of academic programs in the field and continued demand for data scientists in industry. Definitions range from rebranded statistics to data-driven science to the science of data to simply the application of machine learning to so-called big data to solve real-world problems. Current efforts to trace the history of the field in order to clarify its definition, such as Donoho's"50 Years of Data Science"(Donoho 2017), tend to focus on a short period when a small group of statisticians adopted the term in an unsuccessful attempt to rebrand their field in the face of the overshadowing effects of computational statistics and data mining. Using textual evidence from primary sources, this essay traces the history of the term to the 1960s, when it was first used by the US Air Force in a surprisingly similar way to its current usage, to 2012, the year that Harvard Business Review published the enormously influential article"Data Scientist: The Sexiest Job of the 21st Century"(Davenport and Patil 2012), while the American Statistical Association acknowledged a profound disconnect between statistics and data science. Among the themes that emerge from this review are (1) the long-standing opposition between data analysts and data miners that continues to animate the field, (2) an established definition of the term as the practice of managing and processing scientific data that has been occluded by recent usage, and (3) the phenomenon of data impedance -- the disproportion between surplus data, indexed by phrases like data deluge and big data, and the limitations of computational machinery and methods to process them. This persistent condition appears to have motivated the use of the term and the field itself since its beginnings.
Trace Metals and Hydraulic Characterization of Soils and Groundwater Around Ajakanga Dumpsite in Ibadan Metropolis, Southwest Nigeria.

Trace metals concentrations and hydraulic characteristics of soil were studied with a view to assessing the impact of the Ajakanga dumpsite in Ibadan, Southwest Nigeria, on soil and groundwater. Forty one surface soil samples were taken at the depth of 0.2 cm, eleven samples each of disturbed and undisturbed soils along two (2) vertical profiles at up-slope and down-slope pits within the dumpsite premises. Eight groundwater samples were also collected. Digestion of 0.5 gm each of fraction (< 63 µm) of the soil samples was carried out. Both the digested soil and groundwater samples were analysed for Zn, Cu, Mn, Pb, Cd, Ni, As, Co and Cr using Buck Model 205 Atomic Absorption Spectrophotometer (AAS). The undisturbed soils were used for the determination of hydraulic properties. The data collected were interpreted using appropriate qualitative and quantitative statistical techniques. Trace elements results for soil showed average values for Zn (29.30 ± 16.1 mg/L), Pb (14.72 ± 0.84 mg/L), Cd (2.49 ± 0.48 mg/L), Ni (2.03 ± 1.12 mg/L), As (0.84 ± 0.22 mg/L), Co (0.16 ± 0.10 mg/L) and Cr (0.92 ± 1.40 mg/L) that were higher than those of control site values suggesting contamination. Grain size analysis showed that the clay size particles average abundance was higher in the B-level of the down-slope soil profile with mean value of 5.07±0.82 % compared with mean value of 4.85 ± 0.59 % at the up-slope pit. Evaluated results showed that the B-level is more polluted than the A and C levels probably due to adsorbsion effect of clay-sized materials that are more abundant in the B-level. Groundwater trace elements average concentration values were Mn (0.31± 0.74), Pb (0.03 ± 0.04), Cd (0.02 ± 0.03 mg/L), As (0.06 ±0.03 mg/L) and Ni (0.13 ± 0.08mg/L) which were higher than the WHO (2007) permissible levels especially in samples taken close to the dumpsite. The study concluded that the soils and groundwater samples close to Ajakanga open dumpsites were more polluted than those taken far away. The soil contamination was well pronounced within 0 – 3.5 m of the vertical soil profile at the dumpsite vicinity. Keywords: Trace Metals, Soil, Groundwater, Hydraulic Characteristics, Dumpsite, Environmental Impact.
Emergence of Abstract State Representations in Embodied Sequence Modeling

Decision making via sequence modeling aims to mimic the success of language models, where actions taken by an embodied agent are modeled as tokens to predict. Despite their promising performance, it remains unclear if embodied sequence modeling leads to the emergence of internal representations that represent the environmental state information. A model that lacks abstract state representations would be liable to make decisions based on surface statistics which fail to generalize. We take the BabyAI environment, a grid world in which language-conditioned navigation tasks are performed, and build a sequence modeling Transformer, which takes a language instruction, a sequence of actions, and environmental observations as its inputs. In order to investigate the emergence of abstract state representations, we design a"blindfolded"navigation task, where only the initial environmental layout, the language instruction, and the action sequence to complete the task are available for training. Our probing results show that intermediate environmental layouts can be reasonably reconstructed from the internal activations of a trained model, and that language instructions play a role in the reconstruction accuracy. Our results suggest that many key features of state representations can emerge via embodied sequence modeling, supporting an optimistic outlook for applications of sequence modeling objectives to more complex embodied decision-making domains.