Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Doping and the Mott-Kondo Scenario for Infinite-Layer Nickelate Superconductors

We give a brief review of the Mott-Kondo scenario and its consequence in the recently-discovered infinite-layer nickelate superconductors. We argue that the parent state is a self-doped Mott insulator and propose an effective t- J-K model to account for its low-energy properties. At small doping, the model describes a low carrier density Kondo system with incoherent Kondo scattering at finite temperatures, in good agreement with experimental observation of the logarithmic temperature dependence of electric resistivity. Upon increasing Sr doping, the model predicts a breakdown of the Kondo effect, which provides a potential explanation of the non-Fermi liquid behavior of the electric resistivity with a power law scaling over a wide range of the temperature. Unconventional superconductivity is shown to undergo a transition from nodeless (d+is)-wave to nodal d-wave near the critical doping due to competition of the Kondo and Heisenberg superexchange interactions. The presence of different pairing symmetry may be supported by recent tunneling measurements.
Single-Modal Entropy based Active Learning for Visual Question Answering

Constructing a large-scale labeled dataset in the real world, especially for high-level tasks (eg, Visual Question Answering), can be expensive and time-consuming. In addition, with the ever-growing amounts of data and architecture complexity, Active Learning has become an important aspect of computer vision research. In this work, we address Active Learning in the multi-modal setting of Visual Question Answering (VQA). In light of the multi-modal inputs, image and question, we propose a novel method for effective sample acquisition through the use of ad hoc single-modal branches for each input to leverage its information. Our mutual information based sample acquisition strategy Single-Modal Entropic Measure (SMEM) in addition to our self-distillation technique enables the sample acquisitor to exploit all present modalities and find the most informative samples. Our novel idea is simple to implement, cost-efficient, and readily adaptable to other multi-modal tasks. We confirm our findings on various VQA datasets through state-of-the-art performance by comparing to existing Active Learning baselines.
Test time Adaptation through Perturbation Robustness

Data samples generated by several real world processes are dynamic in nature \textit{i.e.}, their characteristics vary with time. Thus it is not possible to train and tackle all possible distributional shifts between training and inference, using the host of transfer learning methods in literature. In this paper, we tackle this problem of adapting to domain shift at inference time \textit{i.e.}, we do not change the training process, but quickly adapt the model at test-time to handle any domain shift. For this, we propose to enforce consistency of predictions of data sampled in the vicinity of test sample on the image manifold. On a host of test scenarios like dealing with corruptions (CIFAR-10-C and CIFAR-100-C), and domain adaptation (VisDA-C), our method is at par or significantly outperforms previous methods.
Thermodynamic stability and critical points in multicomponent mixtures with structured interactions

Theoretical work has shed light on the phase behavior of idealized mixtures of many components with random interactions. However, typical mixtures interact through particular physical features, leading to a structured, nonrandom interaction matrix of lower rank. Here, we develop a theoretical framework for such mixtures and derive mean-field conditions for thermodynamic stability and critical behavior. Irrespective of the number of components and features, this framework allows for a generally lower-dimensional representation in the space of features and proposes a principled way to coarse-grain multicomponent mixtures as binary mixtures. Moreover, it suggests a way to systematically characterize different series of critical points and their codimensions in mean-field. Since every pairwise interaction matrix can be expressed in terms of features, our work is applicable to a broad class of mean-field models.
Machine-learning techniques applied to three-year exposure of ANAIS–112

ANAIS is a direct dark matter detection experiment aiming at the confirmation or refutation of the DAMA/LIBRA positive annual modulation signal in the low energy detection rate, using the same target and technique. ANAIS–112, located at the Canfranc Underground Laboratory in Spain, is operating an array of 3×3 ultrapure NaI(Tl) crystals with a total mass of 112.5 kg since August 2017. The trigger rate in the region of interest (1-6 keV) is dominated by non-bulk scintillation events. In order to discriminate these noise events from bulk scintillation events, robust filtering protocols have been developed. Although this filtering procedure works very well above 2 keV, the measured rate from 1 to 2 keV is about 50% higher than expected according to our background model, and we cannot discard non-bulk scintillation events as responsible of that excess. In order to improve the rejection of noise events, a Boosted Decision Tree has been developed and applied. With this new PMT-related noise rejection algorithm, the ANAIS–112 background between 1 and 2 keV is reduced by almost 30%, leading to an increase in sensitivity to the annual modulation signal. The reanalysis of the three years of ANAIS–112 data with this technique is also presented.
Text-Based Person Search with Limited Data

Text-based person search (TBPS) aims at retrieving a target person from an image gallery with a descriptive text query. Solving such a fine-grained cross-modal retrieval task is challenging, which is further hampered by the lack of large-scale datasets. In this paper, we present a framework with two novel components to handle the problems brought by limited data. Firstly, to fully utilize the existing small-scale benchmarking datasets for more discriminative feature learning, we introduce a cross-modal momentum contrastive learning framework to enrich the training data for a given mini-batch. Secondly, we propose to transfer knowledge learned from existing coarse-grained large-scale datasets containing image-text pairs from drastically different problem domains to compensate for the lack of TBPS training data. A transfer learning method is designed so that useful information can be transferred despite the large domain gap. Armed with these components, our method achieves new state of the art on the CUHK-PEDES dataset with significant improvements over the prior art in terms of Rank-1 and mAP. Our code is available at https://github.com/BrandonHanx/TextReID.
Meta-optimization for fully automated radiation therapy treatment planning

Objective. Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propose a meta-optimization framework, called MetaPlanner (MP). Approach. Our MP algorithm automates planning by performing meta-optimization of treatment planning hyperparameters. The algorithm uses a derivative-free method (i.e. parallel Nelder–Mead simplex search) to search for weight configurations that minimize a meta-scoring function. Meta-scoring is performed by constructing a tier list of the relevant considerations (e.g. dose homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical decision-making process. Additionally, we have made our source code publicly available via github. Main results. The proposed MP method is evaluated on two datasets (21 prostate cases and 6 head and neck cases) collected as part of clinical workflow. MP is applied to both IMRT and VMAT planning and compared to a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has comparable or better performance than manual VMAT planning for all evaluated metrics. Significance. Our proposed MP provides a general framework for fully automated treatment planning that produces high quality treatment plans. Our MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality.
Controllable and Compositional Generation with Latent-Space Energy-Based Models

Controllable generation is one of the key requirements for successful adoption of deep generative models in real-world applications, but it still remains as a great challenge. In particular, the compositional ability to generate novel concept combinations is out of reach for most current models. In this work, we use energy-based models (EBMs) to handle compositional generation over a set of attributes. To make them scalable to high-resolution image generation, we introduce an EBM in the latent space of a pre-trained generative model such as StyleGAN. We propose a novel EBM formulation representing the joint distribution of data and attributes together, and we show how sampling from it is formulated as solving an ordinary differential equation (ODE). Given a pre-trained generator, all we need for controllable generation is to train an attribute classifier. Sampling with ODEs is done efficiently in the latent space and is robust to hyperparameters. Thus, our method is simple, fast to train, and efficient to sample. Experimental results show that our method outperforms the state-of-the-art in both conditional sampling and sequential editing. In compositional generation, our method excels at zero-shot generation of unseen attribute combinations. Also, by composing energy functions with logical operators, this work is the first to achieve such compositionality in generating photo-realistic images of resolution 1024x1024. Code is available at https://github.com/NVlabs/LACE.
Eﬃcient Fully-Coherent

Hamiltonian simulation is a fundamental problem at the heart of quantum computation, and the associated simulation algorithms are useful building blocks for designing larger quantum algorithms. In order to be successfully concatenated into a larger quantum algorithm, a Hamiltonian simulation algorithm must succeed with arbitrarily high success probability 1 − δ while only requiring a single copy of the initial state, a property which we call fully-coherent . Although optimal Hamiltonian simulation has been achieved by quantum signal processing (QSP), with query complexity linear in time t and logarithmic in inverse error ln(1 /(cid:15) ), the corresponding algorithm is not fully-coherent as it only succeeds with probability close to 1 / 4. While this simulation algorithm can be made fully-coherent by employing amplitude ampliﬁcation at the expense of appending a ln(1 /δ ) multiplicative factor to the query complexity, here we develop a new fully-coherent Hamiltonian simulation algorithm that achieves a query complexity additive in ln(1 /δ ): Θ (cid:0) (cid:107)H(cid:107)| t | + ln(1 /(cid:15) ) + ln(1 /δ ) (cid:1) . We accomplish this by compressing the spectrum of the Hamiltonian with an aﬃne transformation, and applying to it a QSP polynomial that approximates the complex exponential only over the range of the compressed spectrum. We further numerically analyze the complexity of this algorithm and demonstrate its application to the simulation of the Heisenberg model in constant and time-dependent external magnetic ﬁelds. We believe that this eﬃcient fully-coherent Hamiltonian simulation algorithm can serve as a useful subroutine in quantum algorithms where maintaining coherence is paramount.
Multimessenger astronomy with a kHz-band gravitational-wave observatory

Abstract Proposed next-generation networks of gravitational-wave observatories include dedicated kilohertz instruments that target neutron star science, such as the proposed Neutron Star Extreme Matter Observatory, NEMO. The original proposal for NEMO highlighted the need for it to exist in a network of gravitational-wave observatories to ensure detection confidence and sky localisation of sources. We show that NEMO-like observatories have significant utility on their own as coincident electromagnetic observations can provide the detection significance and sky localisation. We show that, with a single NEMO-like detector and expected electromagnetic observatories in the late 2020 s and early 2030 s such as the Vera C. Rubin observatory and SVOM, approximately 40% of all binary neutron star mergers detected with gravitational waves could be confidently identified as coincident multimessenger detections. We show that we expect $2^{+10}_{-1}{yr^{-1}}{}$ coincident observations of gravitational-wave mergers with gamma-ray burst prompt emission, $13^{+23}_{-10}{yr^{-1}}{}$ detections with kilonova observations, and $4^{+18}_{-3}{yr^{-1}}{}$ with broadband afterglows and kilonovae, where the uncertainties are 90% confidence intervals arising from uncertainty in current merger-rate estimates. Combined, this implies a coincident detection rate of $14^{+25}_{-11}{yr^{-1}}{}$ out to $300\,\mathrm{Mpc}$ . These numbers indicate significant science potential for a single kilohertz gravitational-wave detector operating without a global network of other gravitational-wave observatories.
Dynamic Bottleneck for Robust Self-Supervised Exploration

Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards. However, such methods are usually sensitive to environmental dynamics-irrelevant information, e.g., white-noise. To handle such dynamics-irrelevant information, we propose a Dynamic Bottleneck (DB) model, which attains a dynamics-relevant representation based on the information-bottleneck principle. Based on the DB model, we further propose DB-bonus, which encourages the agent to explore state-action pairs with high information gain. We establish theoretical connections between the proposed DB-bonus, the upper confidence bound (UCB) for linear case, and the visiting count for tabular case. We evaluate the proposed method on Atari suits with dynamics-irrelevant noises. Our experiments show that exploration with DB bonus outperforms several state-of-the-art exploration methods in noisy environments.
Classification of Encrypted IoT Traffic despite Padding and Shaping

It is well-known that when IoT traffic is unencrypted it is possible to identify the active devices based on their TCP/IP headers. And when traffic is encrypted, packet-sizes and timings can still be used to do so. To defend against such fingerprinting, traffic padding and shaping were introduced. In this paper we show that even with these mitigations, the privacy of IoT consumers can still be violated. The main tool we use in our analysis is the full distribution of packet-size---as opposed to commonly used statistics such as mean and variance. We evaluate the performance of a local adversary, such as a snooping neighbor or a criminal, against 8~different padding methods. We show that our classifiers achieve perfect (100% accuracy) classification using the full packet-size distribution for low-overhead methods, whereas prior works that rely on statistical metadata achieved lower rates even when no padding and shaping were used. We also achieve an excellent classification rate even against high-overhead methods. We further show how an external adversary such as a malicious ISP or a government intelligence agency, who only sees the padded and shaped traffic as it goes through a VPN, can accurately identify the subset of active devices with Recall and Precision of at least 96%. Finally, we also propose a new method of padding we call the Dynamic STP (DSTP) that incurs significantly less per-packet overhead compared to other padding methods we tested and guarantees more privacy to IoT consumers.
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information

End-to-end learning robotic manipulation with high data efficiency is one of the key challenges in robotics. The latest methods that utilize human demonstration data and unsupervised representation learning has proven to be a promising direction to improve RL learning efficiency. The use of demonstration data also allows"warming-up"the RL policies using offline data with imitation learning or the recently emerged offline reinforcement learning algorithms. However, existing works often treat offline policy learning and online exploration as two separate processes, which are often accompanied by severe performance drop during the offline-to-online transition. Furthermore, many robotic manipulation tasks involve complex sub-task structures, which are very challenging to be solved in RL with sparse reward. In this work, we propose a unified offline-to-online RL framework that resolves the transition performance drop issue. Additionally, we introduce goal-aware state information to the RL agent, which can greatly reduce task complexity and accelerate policy learning. Combined with an advanced unsupervised representation learning module, our framework achieves great training efficiency and performance compared with the state-of-the-art methods in multiple robotic manipulation tasks.
Copper recovery improvement in an industrial flotation circuit: A case study of Sarcheshmeh copper mine

ABSTRACT Obtaining high recovery in copper flotation plants has been always under investigation in recent years. Misreported copper into tailing dramatically declines copper recovery. This study aims to find the possible reasons of copper loss to tailing and present the methods to improve the performance of the Sarcheshmeh copper plant flotation circuit. This work was performed in two phases. In the first stage, two surveys were carried out in an industrial plant for evaluation of floatability of two different types of copper ore (sulfide and mixed copper ores comprising sulfide and oxide minerals) and investigation of misreported copper into tailing. In the second stage, influence of particle size distribution in different levels, the collector (Z11 + R407), and sulfidisation agent (NaHS) dosages were investigated on the floatability of mixed copper ore, and possible improvement of copper recovery ores was evaluated by a series of batch flotation experiments. Almost 95% of overall copper loss in final tailing took place in the rougher circuit. The overall recovery of mixed copper ores was obtained 8% less than the other survey. It was found that an important factor of recovery reduction was due to increasing oxide copper proportion in rougher feed. Results of size-by-size recovery analysis showed that the highest sulfide copper loss occurred in coarse particles (>74 µm) and the highest oxide copper loss happened in fine particles (<9 µm). From batch flotation experiments, it was found that the ultimate recovery increased to a certain value with increasing the collector dosage (50 g/t) and thereafter reduced. Increasing the NaHS dosage to 800 g/t resulted in 4.5% increase in recovery from 79.3% to 83.8% with a significant increase in grade. It is observed that size reduction of coarse particles (>74 μm) of rougher tailing and then their flotation have significant effect on increasing overall recovery from 79.3% to 82.2%. Experimental studies showed that the use of the proposed methods with considering the optimized conditions in the plant can be led to an increase in copper recovery from 79% to 87% without any decrease in grade.
Higher Coleman Theory

We develop local cohomology techniques to study the finite slope part of the coherent cohomology of Shimura varieties. The local cohomology groups we consider are a generalization of overconvergent modular forms, and they are defined by using a stratification on the Shimura variety obtained from the Bruhat stratification on a flag variety via the Hodge-Tate period map. We construct a spectral sequence from the local cohomologies to the classical cohomology and use it to obtain classicality and vanishing results. We also develop a theory of p-adic families and construct eigenvarieties. As an application, we prove some new properties of Galois representations arising from certain non-regular algebraic cuspidal automorphic representations.
Real-M: Towards Speech Separation on Real Mixtures

In recent years, deep learning based source separation has achieved impressive results. Most studies, however, still evaluate separation models on synthetic datasets, while the performance of state-of-the-art techniques on in-the-wild speech data remains an open question. This paper contributes to fill this gap in two ways. First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures. Secondly, we address the problem of performance evaluation of real-life mixtures, where the ground truth is not available. We bypass this issue by carefully designing a blind Scale-Invariant Signal-to-Noise Ratio (SI-SNR) neural estimator. Through a user study, we show that our estimator reliably evaluates the separation performance on real mixtures, i.e. we observe that the performance predictions of the SI-SNR estimator correlate well with human opinions. Moreover, when evaluating popular speech separation models, we observe that the performance trends predicted by our estimator on the REAL-M dataset closely follow the performance trends achieved on synthetic benchmarks.
Galactic Chemical Evolution of Radioactive Isotopes with an s-process Contribution

Analysis of inclusions in primitive meteorites reveals that several short-lived radionuclides (SLRs) with half-lives of 0.1–100 Myr existed in the early solar system (ESS). We investigate the ESS origin of 107Pd, 135Cs, and 182Hf, which are produced by slow neutron captures (the s-process) in asymptotic giant branch (AGB) stars. We modeled the Galactic abundances of these SLRs using the OMEGA+ galactic chemical evolution (GCE) code and two sets of mass- and metallicity-dependent AGB nucleosynthesis yields (Monash and FRUITY). Depending on the ratio of the mean-life τ of the SLR to the average length of time between the formations of AGB progenitors γ, we calculate timescales relevant for the birth of the Sun. If τ/γ ≳ 2, we predict self-consistent isolation times between 9 and 26 Myr by decaying the GCE predicted 107Pd/108Pd, 135Cs/133Cs, and 182Hf/180Hf ratios to their respective ESS ratios. The predicted 107Pd/182Hf ratio indicates that our GCE models are missing 9%–73% of 107Pd and 108Pd in the ESS. This missing component may have come from AGB stars of higher metallicity than those that contributed to the ESS in our GCE code. If τ/γ ≲ 0.3, we calculate instead the time (T LE) from the last nucleosynthesis event that added the SLRs into the presolar matter to the formation of the oldest solids in the ESS. For the 2 M ⊙, Z = 0.01 Monash model we find a self-consistent solution of T LE = 25.5 Myr.
The Neumann problem for fully nonlinear SPDE

We generalize the notion of pathwise viscosity solutions, put forward by Lions and Souganidis to study fully nonlinear stochastic partial differential equations, to equations set on a sub-domain with Neumann boundary conditions. Under a convexity assumption on the domain, we obtain a comparison theorem which yields existence and uniqueness of solutions as well as continuity with respect to the driving noise. As an application, we study the long time behaviour of a stochastically perturbed mean-curvature flow in a cylinder-like domain with right angle contact boundary condition.