Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts, thus degrading their perceptual quality. In this paper, we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition, and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy, our method enables the discrimination against the compression domain, and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads.
Neural Population Geometry and Optimal Coding of Tasks with Shared Latent Structure

Humans and animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. Several recent works argue that the brain supports these abilities by forming neural representations that encode such latent structures in flexible, generalizable ways. However, it remains unclear what aspects of neural population activity are contributing to these computational capabilities. Here, we develop an analytical theory linking the mesoscopic statistics of a neural population's activity to generalization performance on a multi-task learning problem. To do this, we rely on a generative model in which different tasks depend on a common, unobserved latent structure and predictions are formed from a linear readout of a neural population's activity. We show that three geometric measures of the population activity determine generalization performance in these settings. Using this theory, we find that experimentally observed factorized (or disentangled) representations naturally emerge as an optimal solution to the multi-task learning problem. We go on to show that when data is scarce, optimal codes compress less informative latent variables, and when data is abundant, optimal codes expand this information in the state space. We validate predictions from our theory using biological and artificial neural network data. Our results therefore tie neural population geometry to the multi-task learning problem and make normative predictions of the structure of population activity in these settings.
Ultracold atomic spin mixtures in ultrastable magnetic field environments

Ultracold atomic spin mixtures develop rich and intriguing magnetic properties when an external radiation coherently couples different spin states. In particular, the coupled mixture may acquire a critical behavior when the spin interactions equal the coupling energy. However, atomic mixtures generally feature a relatively high sensitivity to magnetic fields that can set a limitation to the observable phenomena. In this article, we present an overview of experimental studies of magnetism based on superfluid multicomponent gases in an ultrastable magnetic field environment, which recently became available.
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond

We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new data selection approach based on $k$-means clustering and sensitivity sampling. Assuming access to an embedding representation of the data with respect to which the model loss is H\"older continuous, our approach provably allows selecting a set of ``typical'' $k + 1/\varepsilon^2$ elements whose average loss corresponds to the average loss of the whole dataset, up to a multiplicative $(1\pm\varepsilon)$ factor and an additive $\varepsilon \lambda \Phi_k$, where $\Phi_k$ represents the $k$-means cost for the input embeddings and $\lambda$ is the H\"older constant. We furthermore demonstrate the performance and scalability of our approach on fine-tuning foundation models and show that it outperforms state-of-the-art methods. We also show how it can be applied on linear regression, leading to a new sampling strategy that surprisingly matches the performances of leverage score sampling, while being conceptually simpler and more scalable.
Rearrangement of orbitals in KAgF3 due to Kugel-Khomskii mechanism: a Neutron diffraction and Density Functional Theory study

The crystal structure of KAgF3 was studied by powder neutron diffraction. KAgF3 exhibits at all temperatures an orthorhombic symmetry in space group Pnma that allows for several distortions with respect to the ideal cubic perovskite structure. At all temperatures there is a strong splitting of Ag-F distances parallel to the a,c planes that documents alternating occupation of holes in x2-y2 and z2-y2 orbitals. The orientation of the octahedron elongation and thereby the orbital order flips at a structural phase transition occurring around Ts=240K which is accompanied by a suppression of magnetic susceptibility. The orbital ordering is further enhanced in the low-temperature phase and the twisting of the AgF4 plaquettes forming the antiferromagnetic chains changes. DFT calculations show an enhancement of the magnetic interaction in the low temperature phase indicating that the transition and the orbital order are partially driven by the Kugel-Khomskii mechanism.
Approximating Simplet Frequency Distribution for Simplicial Complexes

Simplets, constituting elementary units within simplicial complexes (SCs), serve as foundational elements for the structural analysis of SCs. Previous efforts have focused on the exact count or approximation of simplet count rather than their frequencies, with the latter being more practical in large-scale SCs. This paper enables simplet frequency analysis of SCs by introducing the Simplet Frequency Distribution (SFD) vector. In addition, we present a bound on the sample complexity required for accurately approximating the SFD vector by any uniform sampling-based algorithm. We also present a simple algorithm for this purpose and justify the theoretical bounds with experiments on some random simplicial complexes.
Generating Effective Ensembles for Sentiment Analysis

In recent years, transformer models have revolutionized Natural Language Processing (NLP), achieving exceptional results across various tasks, including Sentiment Analysis (SA). As such, current state-of-the-art approaches for SA predominantly rely on transformer models alone, achieving impressive accuracy levels on benchmark datasets. In this paper, we show that the key for further improving the accuracy of such ensembles for SA is to include not only transformers, but also traditional NLP models, despite the inferiority of the latter compared to transformer models. However, as we empirically show, this necessitates a change in how the ensemble is constructed, specifically relying on the Hierarchical Ensemble Construction (HEC) algorithm we present. Our empirical studies across eight canonical SA datasets reveal that ensembles incorporating a mix of model types, structured via HEC, significantly outperform traditional ensembles. Finally, we provide a comparative analysis of the performance of the HEC and GPT-4, demonstrating that while GPT-4 closely approaches state-of-the-art SA methods, it remains outperformed by our proposed ensemble strategy.
Towards Decoding Brain Activity During Passive Listening of Speech

The aim of the study is to investigate the complex mechanisms of speech perception and ultimately decode the electrical changes in the brain accruing while listening to speech. We attempt to decode heard speech from intracranial electroencephalographic (iEEG) data using deep learning methods. The goal is to aid the advancement of brain-computer interface (BCI) technology for speech synthesis, and, hopefully, to provide an additional perspective on the cognitive processes of speech perception. This approach diverges from the conventional focus on speech production and instead chooses to investigate neural representations of perceived speech. This angle opened up a complex perspective, potentially allowing us to study more sophisticated neural patterns. Leveraging the power of deep learning models, the research aimed to establish a connection between these intricate neural activities and the corresponding speech sounds. Despite the approach not having achieved a breakthrough yet, the research sheds light on the potential of decoding neural activity during speech perception. Our current efforts can serve as a foundation, and we are optimistic about the potential of expanding and improving upon this work to move closer towards more advanced BCIs, better understanding of processes underlying perceived speech and its relation to spoken speech.
Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study

Large Language Models (LLMs) have gained significant attention in the software engineering community. Nowadays developers have the possibility to exploit these models through industrial-grade tools providing a handy interface toward LLMs, such as OpenAI's ChatGPT. While the potential of LLMs in assisting developers across several tasks has been documented in the literature, there is a lack of empirical evidence mapping the actual usage of LLMs in software projects. In this work, we aim at filling such a gap. First, we mine 1,501 commits, pull requests (PRs), and issues from open-source projects by matching regular expressions likely to indicate the usage of ChatGPT to accomplish the task. Then, we manually analyze these instances, discarding false positives (i.e., instances in which ChatGPT was mentioned but not actually used) and categorizing the task automated in the 467 true positive instances (165 commits, 159 PRs, 143 issues). This resulted in a taxonomy of 45 tasks which developers automate via ChatGPT. The taxonomy, accompanied with representative examples, provides (i) developers with valuable insights on how to exploit LLMs in their workflow and (ii) researchers with a clear overview of tasks that, according to developers, could benefit from automated solutions.
LLM Inference Unveiled: Survey and Roofline Model Insights

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is vibrant, there hasn't been a concise framework that analyzes the various methods of LLM Inference to provide a clear understanding of this domain. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems, such as why LLMs are memory-bound, how much memory and computation they need, and how to choose the right hardware. We systematically collate the latest advancements in efficient LLM inference, covering crucial areas such as model compression (e.g., Knowledge Distillation and Quantization), algorithm improvements (e.g., Early Exit and Mixture-of-Expert), and both hardware and system-level enhancements. Our survey stands out by analyzing these methods with roofline model, helping us understand their impact on memory access and computation. This distinctive approach not only showcases the current research landscape but also delivers valuable insights for practical implementation, positioning our work as an indispensable resource for researchers new to the field as well as for those seeking to deepen their understanding of efficient LLM deployment. The analyze tool, LLM-Viewer, is open-sourced.
On some features of Quadratic Unconstrained Binary Optimization with random coefficients

Quadratic Unconstrained Binary Optimization (QUBO or UBQP) is concerned with maximizing/minimizing the quadratic form $H(J, \eta) = W \sum_{i,j} J_{i,j} \eta_{i} \eta_{j}$ with $J$ a matrix of coefficients, $\eta \in \{0, 1\}^N$ and $W$ a normalizing constant. In the statistical mechanics literature, QUBO is a lattice gas counterpart to the Sherrington--Kirkpatrick spin glass model. Finding the optima of $H$ is an NP-hard problem. Several problems in combinatorial optimization and data analysis can be mapped to QUBO in a straightforward manner. In the combinatorial optimization literature, random instances of QUBO are often used to test the effectiveness of heuristic algorithms. Here we consider QUBO with random coefficients and show that if the $J_{i,j}$'s have zero mean, then, after proper normalization, the minimum and maximum \emph{per particle} of $H$ do not depend on the details of the distribution of the couplings and are concentrated around their expected values. Further, with the help of numerical simulations, we give estimates of the minimum and maximum of the objective function and provide some insight into the structure of the minimizer and the maximizer of $H$. We argue that also this structure is rather robust. Our findings hold in the diluted case where each of the $J_{i,j}$'s is allowed to be zero with probability going to $1$ as $N \to \infty$ in a suitable way.
The optimizing mode classification stabilization of sampled stochastic jump systems via an improved hill-climbing algorithm based on Q-learning

This paper addresses the stabilization problem of stochastic jump systems (SJSs) closed by a generally sampled controller. Because of the controller's switching and state both sampled, it is challenging to study its stabilization. A new stabilizing method deeply depending on the mode classifications is proposed to deal with the above sampling situation, whose quantity is equal to a Stirling number of the second kind. For the sake of finding the best stabilization effect among all the classifications, a convex optimization problem is developed, whose globally solution is proved to be existent and can be computed by an augmented Lagrangian function. More importantly, in order to further reduce the computation complexity but retaining a better performance as much as possible, a novelly improved hill-climbing algorithm is established by applying the Q-learning technique to provide an optimal attenuation coefficient. A numerical example is offered so as to verify the effectiveness and superiority of the methods proposed in this study.
Creating Suspenseful Stories: Iterative Planning with Large Language Models

Automated story generation has been one of the long-standing challenges in NLP. Among all dimensions of stories, *suspense* is very common in human-written stories but relatively under-explored in AI-generated stories. While recent advances in large language models (LLMs) have greatly promoted language generation in general, state-of-the-art LLMs are still unreliable when it comes to suspenseful story generation. We propose a novel iterative-prompting-based planning method that is grounded in two theoretical foundations of story suspense from cognitive psychology and narratology. This theory-grounded method works in a fully zero-shot manner and does not rely on any supervised story corpora. To the best of our knowledge, this paper is the first attempt at suspenseful story generation with LLMs. Extensive human evaluations of the generated suspenseful stories demonstrate the effectiveness of our method.
Models are likely to underestimate increase in heavy rainfall in the extratropical regions with high rainfall intensity

Model projections of regional changes in heavy rainfall are uncertain. On timescales of few decades, internal variability plays an important role and therefore poses a challenge to detect robust model response in heavy rainfall to rising temperatures. We use spatial aggregation to reduce the major role of internal variability and evaluate the heavy rainfall response to warming temperatures with observations. We show that in the regions with high rainfall intensity and for which gridded observations exist, most of the models underestimate the historical scaling of heavy rainfall and the land fraction with significant positive heavy rainfall scalings during the historical period. The historical behavior is correlated with the projected heavy rainfall intensification across models allowing to apply an observational constraint, i.e., to calibrate multimodel ensembles with observations in order to narrow the range of projections. The constraint suggests a substantially stronger intensification of future heavy rainfall than the multimodel mean.
Interactive Mars Image Content-Based Search with Interpretable Machine Learning

The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity. In this paper, we leverage a prototype-based architecture to enable users to understand and validate the evidence used by a classifier trained on images from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to providing explanations, we investigate the diversity and correctness of evidence used by the content-based classifier. The work presented in this paper will be deployed on the PDS Image Atlas, replacing its non-interpretable counterpart.
An Interpretable Evaluation of Entropy-based Novelty of Generative Models

The massive developments of generative model frameworks and architectures require principled methods for the evaluation of a model's novelty compared to a reference dataset or baseline generative models. While the recent literature has extensively studied the evaluation of the quality, diversity, and generalizability of generative models, the assessment of a model's novelty compared to a baseline model has not been adequately studied in the machine learning community. In this work, we focus on the novelty assessment under multi-modal generative models and attempt to answer the following question: Given the samples of a generative model $\mathcal{G}$ and a reference dataset $\mathcal{S}$, how can we discover and count the modes expressed by $\mathcal{G}$ more frequently than in $\mathcal{S}$. We introduce a spectral approach to the described task and propose the Kernel-based Entropic Novelty (KEN) score to quantify the mode-based novelty of distribution $P_\mathcal{G}$ with respect to distribution $P_\mathcal{S}$. We analytically interpret the behavior of the KEN score under mixture distributions with sub-Gaussian components. Next, we develop a method based on Cholesky decomposition to compute the KEN score from observed samples. We support the KEN-based quantification of novelty by presenting several numerical results on synthetic and real image distributions. Our numerical results indicate the success of the proposed approach in detecting the novel modes and the comparison of state-of-the-art generative models.
High-Fidelity Neural Phonetic Posteriorgrams

A phonetic posteriorgram (PPG) is a time-varying categorical distribution over acoustic units of speech (e.g., phonemes). PPGs are a popular representation in speech generation due to their ability to disentangle pronunciation features from speaker identity, allowing accurate reconstruction of pronunciation (e.g., voice conversion) and coarse-grained pronunciation editing (e.g., foreign accent conversion). In this paper, we demonstrably improve the quality of PPGs to produce a state-of-the-art interpretable PPG representation. We train an off-the-shelf speech synthesizer using our PPG representation and show that high-quality PPGs yield independent control over pitch and pronunciation. We further demonstrate novel uses of PPGs, such as an acoustic pronunciation distance and fine-grained pronunciation control.
FlipHash: A Constant-Time Consistent Range-Hashing Algorithm

Consistent range-hashing is a technique used in distributed systems, either directly or as a subroutine for consistent hashing, commonly to realize an even and stable data distribution over a variable number of resources. We introduce FlipHash, a consistent range-hashing algorithm with constant time complexity and low memory requirements. Like Jump Consistent Hash, FlipHash is intended for applications where resources can be indexed sequentially. Under this condition, it ensures that keys are hashed evenly across resources and that changing the number of resources only causes keys to be remapped from a removed resource or to an added one, but never shuffled across persisted ones. FlipHash differentiates itself with its low computational cost, achieving constant-time complexity. We show that FlipHash beats Jump Consistent Hash's cost, which is logarithmic in the number of resources, both theoretically and in experiments over practical settings.
Cost of quantum secret key

In this paper, we develop the resource theory of quantum secret key. Operating under the assumption that entangled states with zero distillable key do not exist, we define the key cost of a quantum state, and device. We study its properties through the lens of a quantity that we call the key of formation. The main result of our paper is that the regularized key of formation is an upper bound on the key cost of a quantum state. The core protocol underlying this result is privacy dilution, which converts states containing ideal privacy into ones with diluted privacy. Next, we show that the key cost is bounded from below by the regularized relative entropy of entanglement, which implies the irreversibility of the privacy creation-distillation process for a specific class of states. We further focus on mixed-state analogues of pure quantum states in the domain of privacy, and we prove that a number of entanglement measures are equal to each other for these states, similar to the case of pure entangled states. The privacy cost and distillable key in the single-shot regime exhibit a yield-cost relation, and basic consequences for quantum devices are also provided.