Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic quantum wires in extended quasiparticle picture

A one-dimensional quantum wire of Fermions is considered and ground state properties are calculated in the high density regime within the extended quasiparticle picture and Born approximation. Expanding the two-particle Green functions determines the selfenergy and the polarization as well as the response function on the same footing. While the on-shell selfenergies are strictly zero due to Pauli-blocking of elastic scattering, the off-shell behaviour shows a rich structure of a gap in the damping of excitation which is closed when the momentum approaches the Fermi one. The consistent spectral function is presented completing the first two energy-weighted sum rules. The excitation spectrum shows a splitting due to holons and antiholons as non-Fermi liquid behaviour. A renormalization procedure is proposed by subtracting an energy constant to render the Fock exchange energy finite. The effective mass derived from meanfield shows a dip as onset of Peierls instability. The correlation energy is calculated with the help of the extended quasiparticle picture which accounts for off-shell effects. The corresponding response function leads to the same correlation energy as the selfenergy in agreement with perturbation theory. The reduced density matrix or momentum distribution is calculated with the help of a Pad\'e regularization repairing deficiencies of the perturbation theory. A seemingly finite step at the Fermi energy indicating Fermi-liquid behaviour is repaired in this way.
Localization and integrability breaking in weakly interacting Floquet circuits

We present a family of Floquet circuits that can interpolate between non-interacting qubits, free propagation, generic interacting, and dual-unitary dynamics. We identify the operator entanglement entropy of the two-qubit gate as a good quantitative measure of the interaction strength. We test the persistence of localization in the vicinity of the non-interacting point by probing spectral statistics, decay of autocorrelators, and measuring entanglement growth. The finite-size analysis suggests that the many-body localized regime does not persist in the thermodynamic limit. Instead, our results are compatible with an integrability-breaking phenomenon.
For how many iterations should we run Markov chain Monte Carlo?

Standard Markov chain Monte Carlo (MCMC) admits three fundamental control parameters: the number of chains, the length of the warmup phase, and the length of the sampling phase. These control parameters play a large role in determining the amount of computation we deploy. In practice, we need to walk a line between achieving sufficient precision and not wasting precious computational resources and time. We review general strategies to check the length of the warmup and sampling phases, and examine the three control parameters of MCMC in the contexts of CPU- and GPU-based hardware. Our discussion centers around three tasks: (1) inference about a latent variable, (2) computation of expectation values and quantiles, and (3) diagnostics to check the reliability of the estimators. This chapter begins with general recommendations on the control parameters of MCMC, which have been battle-tested over the years and often motivate defaults in Bayesian statistical software. Usually we do not know ahead of time how a sampler will interact with a target distribution, and so the choice of MCMC algorithm and its control parameters, tend to be based on experience, re-evaluated after simulations have been obtained and analyzed. The second part of this chapter provides a theoretical motivation for our recommended approach, with pointers to some concerns and open problems. We also examine recent developments on the algorithmic and hardware fronts, which motivate new computational approaches to MCMC.
Individualized Policy Evaluation and Learning under Clustered Network Interference

While there now exists a large literature on policy evaluation and learning, much of prior work assumes that the treatment assignment of one unit does not affect the outcome of another unit. Unfortunately, ignoring interference may lead to biased policy evaluation and ineffective learned policies. For example, treating influential individuals who have many friends can generate positive spillover effects, thereby improving the overall performance of an individualized treatment rule (ITR). We consider the problem of evaluating and learning an optimal ITR under clustered network interference (also known as partial interference) where clusters of units are sampled from a population and units may influence one another within each cluster. Unlike previous methods that impose strong restrictions on spillover effects, the proposed methodology only assumes a semiparametric structural model where each unit's outcome is an additive function of individual treatments within the cluster. Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR. We show that this estimator is substantially more efficient than the standard inverse probability weighting estimator, which does not impose any assumption about spillover effects. We derive the finite-sample regret bound for a learned ITR, showing that the use of our efficient evaluation estimator leads to the improved performance of learned policies. Finally, we conduct simulation and empirical studies to illustrate the advantages of the proposed methodology.
Forward χ2 Divergence Based Variational Importance Sampling

Maximizing the log-likelihood is a crucial aspect of learning latent variable models, and variational inference (VI) stands as the commonly adopted method. However, VI can encounter challenges in achieving a high log-likelihood when dealing with complicated posterior distributions. In response to this limitation, we introduce a novel variational importance sampling (VIS) approach that directly estimates and maximizes the log-likelihood. VIS leverages the optimal proposal distribution, achieved by minimizing the forward $\chi^2$ divergence, to enhance log-likelihood estimation. We apply VIS to various popular latent variable models, including mixture models, variational auto-encoders, and partially observable generalized linear models. Results demonstrate that our approach consistently outperforms state-of-the-art baselines, both in terms of log-likelihood and model parameter estimation.
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder

Generative models of observations under interventions have been a vibrant topic of interest across machine learning and the sciences in recent years. For example, in drug discovery, there is a need to model the effects of diverse interventions on cells in order to characterize unknown biological mechanisms of action. We propose the Sparse Additive Mechanism Shift Variational Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and interpretability for perturbation models. SAMS-VAE models the latent state of a perturbed sample as the sum of a local latent variable capturing sample-specific variation and sparse global variables of latent intervention effects. Crucially, SAMS-VAE sparsifies these global latent variables for individual perturbations to identify disentangled, perturbation-specific latent subspaces that are flexibly composable. We evaluate SAMS-VAE both quantitatively and qualitatively on a range of tasks using two popular single cell sequencing datasets. In order to measure perturbation-specific model-properties, we also introduce a framework for evaluation of perturbation models based on average treatment effects with links to posterior predictive checks. SAMS-VAE outperforms comparable models in terms of generalization across in-distribution and out-of-distribution tasks, including a combinatorial reasoning task under resource paucity, and yields interpretable latent structures which correlate strongly to known biological mechanisms. Our results suggest SAMS-VAE is an interesting addition to the modeling toolkit for machine learning-driven scientific discovery.
Design and Performance Analysis of a Class of Generalized Predictive Controllers

The design and structure of generalized predictive control (GPC) are not simple and intuitive. The performance analysis does not deeply analyze how the controller parameters affect the system characteristics and the relationship between the tracking error caused by the noise and the selected controller parameters. This paper proposes a generalized predictive control, and its design is simple and intuitive for unnecessary solving the Diophantine equation. Then the relationship between desired output, disturbance, and system output is analyzed by the characteristic equation and steady-state analysis. Based on this, the study presents research findings on the steady state of the system and verifies them through simulations. Furthermore, this paper introduces GPC with disturbance compensation and incremental generalized minimum variance control (IGMVC) with disturbance compensation. The conditions for the elimination of disturbance are presented in theory and simulation for the first time.
Future Changes of Drought and Flood Events in China under a Global Warming Scenario

Abstract This study investigates the impact of global warming on drought/flood patterns in China at the end of the 21st century based on the simulations of 22 global climate models and a regional climate model (RegCM3) under the SRES (Special Report on Emissions Scenarios) A1B scenario. The standardized precipitation index (SPI), which has well performance in monitoring the drought/ flood characteristics (in terms of their intensity, duration, and spatial extent) in China, is used in this study. The projected results of 22 coupled models and the RegCM3 simulation are consistent. These models project a decrease in the frequency of droughts in most parts of northern China and a slight increase in the frequency in some parts of southern China. Considering China as a whole, the spatial extents of droughts are projected to be significantly reduced. In contrast, future flood events over most parts of China are projected to occur more frequently with stronger intensity and longer duration than those prevalent currently. Additionally, the spatial extents of flood events are projected to significantly increase.
Coulomb impurities in graphene driven by ultrashort electromagnetic pulses: Excitation, ionization, and pair creation

We provide a theory for electronic transitions induced by ultrashort electromagnetic pulses in two-dimensional artificial relativistic atoms which are created by a charged impurity in a gapped graphene monolayer. Using a non-perturbative sudden-perturbation approximation, we derive and discuss analytical expressions for the probabilities for excitation, ionization and electron-hole pair creation in this system.
Learned layered coding for Successive Refinement in the Wyner-Ziv Problem

We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the aid of correlated side information. This setup refers to the successive refinement of the Wyner-Ziv coding problem. Assuming ideal Slepian-Wolf coding, our approach employs recurrent neural networks (RNNs) to learn layered encoders and decoders for the quadratic Gaussian case. The models are trained by minimizing a variational bound on the rate-distortion function of the successively refined Wyner-Ziv coding problem. We demonstrate that RNNs can explicitly retrieve layered binning solutions akin to scalable nested quantization. Moreover, the rate-distortion performance of the scheme is on par with the corresponding monolithic Wyner-Ziv coding approach and is close to the rate-distortion bound.
Trace and Index of Dirac-Schr\"odinger Operators on Open Space with Operator Potentials

We develop trace and index formulas for a Dirac-Schr\"odinger operator on open space of dimension $d\geq 3$ with a potential given by a family of self-adjoint unbounded operators acting on a infinite dimensional Hilbert space $H$. The index formula generalizes the Callias Index Theorem to unbounded operators, while the trace formula generalizes existing trace formulas of $d=1$ to higher dimensions.
Evolution of reciprocity with limited payoff memory

Direct reciprocity is a mechanism for the evolution of cooperation in repeated social interactions. According to this literature, individuals naturally learn to adopt conditionally cooperative strategies if they have multiple encounters with their partner. Corresponding models have greatly facilitated our understanding of cooperation, yet they often make strong assumptions on how individuals remember and process payoff information. For example, when strategies are updated through social learning, it is commonly assumed that individuals compare their average payoffs. This would require them to compute (or remember) their payoffs against everyone else in the population. To understand how more realistic constraints influence direct reciprocity, we consider the evolution of conditional behaviors when individuals learn based on more recent experiences. Even in the most extreme case that they only take into account their very last interaction, we find that cooperation can still evolve. However, such individuals adopt less generous strategies, and they tend to cooperate less often than in the classical setup with average payoffs. Interestingly, once individuals remember the payoffs of two or three recent interactions, cooperation rates quickly approach the classical limit. These findings contribute to a literature that explores which kind of cognitive capabilities are required for reciprocal cooperation. While our results suggest that some rudimentary form of payoff memory is necessary, it already suffices to remember a few interactions.
SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map

Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting the accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-world scenarios, leading to missed detections or false positives detections in loop closure. To address these issues, we first propose a object-level data association method based on multi-level verification, which can associate 2D semantic features of current frame with 3D objects landmarks of map. Next, taking advantage of these association relations, we introduce a semantic loop closure method based on quadric-level object map topology, which represents scenes through the topological graph of objects and achieves accurate loop closure at a wide field of view by comparing differences in the topological graphs. Finally, we integrate these two methods into a complete object-aware SLAM system. Qualitative experiments and ablation studies demonstrate the effectiveness and robustness of the proposed object-level data association algorithm. Quantitative experiments show that our semantic loop closure method outperforms existing state-of-the-art methods in terms of precision, recall and localization accuracy metrics.
Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading

High-frequency trading is prevalent, where automated decisions must be made quickly to take advantage of price imbalances and patterns in price action that forecast near-future movements. While many algorithms have been explored and tested, analytical methods fail to harness the whole nature of the market environment by focusing on a limited domain. With the evergrowing machine learning field, many large-scale end-to-end studies on raw data have been successfully employed to increase the domain scope for profitable trading but are very difficult to replicate. Combining deep learning on the order books with reinforcement learning is one way of breaking down large-scale end-to-end learning into more manageable and lightweight components for reproducibility, suitable for retail trading. The following work focuses on forecasting returns across multiple horizons using order flow imbalance and training three temporal-difference learning models for five financial instruments to provide trading signals. The instruments used are two foreign exchange pairs (GBPUSD and EURUSD), two indices (DE40 and FTSE100), and one commodity (XAUUSD). The performances of these 15 agents are evaluated through backtesting simulation, and successful models proceed through to forward testing on a retail trading platform. The results prove potential but require further minimal modifications for consistently profitable trading to fully handle retail trading costs, slippage, and spread fluctuation.
Template bank to search for exotic gravitational wave signals from astrophysical compact binaries

Modeled searches of gravitational wave signals from compact binary mergers rely on template waveforms determined by the theory of general relativity (GR). Once a signal is detected, one generally performs the model agnostic test of GR, either looking for consistency between the GR waveform and data or introducing phenomenological deviations to detect the departure from GR. The non-trivial presence of beyond-GR physics can alter the waveform and could be missed by the GR template-based searches. A recent study [Phys. Rev. D 107, 024017 (2023)] targeted the binary black hole merger, assuming the parametrized deviation in lower post-Newtonian terms and demonstrated a mild effect on the search sensitivity. Surprisingly, for the search space of binary neutron star (BNS) systems where component masses range from 1 to $2.4\:\rm{M}_\odot$ and parametrized deviations span $1\sigma$ width of the deviation parameters measured from the GW170817 event, the GR template bank is highly ineffectual for detecting the non-GR signals. Here, we present a new hybrid method to construct a non-GR template bank for the BNS search space. The hybrid method uses the geometric approach of three-dimensional lattice placement to cover most of the parameter space volume, followed by the random method to cover the boundary regions of parameter space. We find that the non-GR bank size is $\sim$15 times larger than the conventional GR bank and is effectual towards detecting non-GR signals in the target search space.
WAR AND AGRICULTURE: THREE DECADES OF CROPLAND LAND COVER CHANGE IN CENTRAL IRAQ

War and institutional changes are important drivers of land use and land cover change, and agricultural regions are the most susceptible to those types of changes. Though agriculture has been a vital component of Iraq’s economy, there is limited information on the effects of war, sanctions, and instability on agricultural land use. This research sought to determine if the effects of three decades of war in Iraq are observable in terms of cultivated area using Landsat TM and ETM+ imagery for a single scene near Baghdad. We created multitemporal features of NDVI to determine cultivated area during different periods of conflict and then compared those areas to determine which period experienced the greatest amount of change. The results showed that little change occurred between the IranIraq War, an inter-war period, and initially following the Gulf War; however, cultivated area increased by 68% during the period of UN sanctions.
Linking the Sanskrit WordNet to the Vedic Dependency Treebank: a pilot study

The Sanskrit WordNet is a resource currently under development, whose core was induced from a Vedic text sample semantically annotated by means of an ontology mapped on the Princeton WordNet synsets. Building on a previous case study on Ancient Greek (Zanchi et al. 2021), we show how sentence frames can be extracted from morphosyntactically parsed corpora by linking an existing dependency treebank of Vedic Sanskrit to verbal synsets in the Sanskrit WordNet. Our case study focuses on two verbs of asking, yāc- and prach-, featuring a high degree of variability in sentence frames. Treebanks enhanced with WordNet-based semantic information revealed to be of crucial help in motivating sentence frame alternations.
Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach

Focusing on recognition of multi-word expressions (MWEs), we address the problem of recording MWEs in WordNet. In fact, not all MWEs recorded in that lexical database could with no doubt be considered as lexicalised (e.g. elements of wordnet taxonomy, quantifier phrases, certain collocations). In this paper, we use a cross-encoder approach to improve our earlier method of distinguishing between lexicalised and non-lexicalised MWEs found in WordNet using custom-designed rule-based and statistical approaches. We achieve F1-measure for the class of lexicalised word combinations close to 80%, easily beating two baselines (random and a majority class one). Language model also proves to be better than a feature-based logistic regression model.
On the Acquisition of WordNet Relations in Portuguese from Pretrained Masked Language Models

This paper studies the application of pretrained BERT in the acquisition of synonyms, antonyms, hypernyms and hyponyms in Portuguese. Masked patterns indicating those relations were compiled with the help of a service for validating semantic relations, and then used for prompting three pretrained BERT models, one multilingual and two for Portuguese (base and large). Predictions for the masks were evaluated in two different test sets. Results achieved by the monolingual models are interesting enough for considering these models as a source for enriching wordnets, especially when predicting hypernyms of nouns. Previously reported performances on prediction were improved with new patterns and with the large model. When it comes to selecting the related word from a set of four options, performance is even better, but not enough for outperforming the selection of the most similar word, as computed with static word embeddings.
Towards Effective Correction Methods Using WordNet Meronymy Relations

In this paper, we analyse and compare several correction methods of knowledge resources with the purpose of improving the abilities of systems that require commonsense reasoning with the least possible human-effort. To this end, we cross-check the WordNet meronymy relation member against the knowledge encoded in a SUMO-based first-order logic ontology on the basis of the mapping between WordNet and SUMO. In particular, we focus on the knowledge in WordNet regarding the taxonomy of animals and plants. Despite being created manually, these knowledge resources — WordNet, SUMO and their mapping — are not free of errors and discrepancies. Thus, we propose three correction methods by semi-automatically improving the alignment between WordNet and SUMO, by performing some few corrections in SUMO and by combining the above two strategies. The evaluation of each method includes the required human-effort and the achieved improvement on unseen data from the WebChild project, that is tested using first-order logic automated theorem provers.