Miracle-dz/UniFAR-FTUs huggingface dataset:
Atlas-based segmentation of multiple sclerosis lesions in magnetic resonance imaging

This thesis deals with the segmentation of brain magnetic resonance imaging applied to multiple sclerosis patients. This disease is characterised by the presence of white matter lesions in this image modality. After a thorough analysis of the state-of-the-art on this topic, pointing out the importance of prior knowledge, and a subsequent review of atlas-based segmentation of brain imaging, we propose two different multiple sclerosis lesion segmentation pipelines based on the conclusions of these studies. The first one provides an initial tissue classification using a modified expectation-maximisation algorithm, which is later on refined with a lesion segmentation step based on thresholding and a regionwise false positive reduction approach. The second one focuses only on the segmentation of lesions and uses an ensemble classifier alongside a rich feature pool including image intensities, probabilistic atlas maps, an outlier map and contextual information. Both approaches are tested against a novel database comprising imaging data from three different hospitals with a variable lesion load per case. The evaluation, carried out in a quantitative and qualitative manner, includes a comparison and uses several metrics for detection and segmentation. The analysis of the results points out a better performance relative to state-of-the-art approaches, with a clear improvement on the first pipeline in terms of detection, and a clear improvement on the second pipeline in terms of segmentation
Code Llama: Open Foundation Models for Code

We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up to 100k tokens. 7B, 13B and 70B Code Llama and Code Llama - Instruct variants support infilling based on surrounding content. Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E. We release Code Llama under a permissive license that allows for both research and commercial use.
Try with Simpler - An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection

With the rapid development of deep learning (DL), the recent trend of log-based anomaly detection focuses on extracting semantic information from log events (i.e., templates of log messages) and designing more advanced DL models for anomaly detection. Indeed, the effectiveness of log-based anomaly detection can be improved, but these DL-based techniques further suffer from the limitations of more heavy dependency on training data (such as data quality or data labels) and higher costs in time and resources due to the complexity and scale of DL models, which hinder their practical use. On the contrary, the techniques based on traditional machine learning or data mining algorithms are less dependent on training data and more efficient, but produce worse effectiveness than DL-based techniques which is mainly caused by the problem of unseen log events (some log events in incoming log messages are unseen in training data) confirmed by our motivating study. Intuitively, if we can improve the effectiveness of traditional techniques to be comparable with advanced DL-based techniques, log-based anomaly detection can be more practical. Indeed, an existing study in the other area (i.e., linking questions posted on Stack Overflow) has pointed out that traditional techniques with some optimizations can indeed achieve comparable effectiveness with the state-of-the-art DL-based technique, indicating the feasibility of enhancing traditional log-based anomaly detection techniques to some degree. Inspired by the idea of “try-with-simpler”, we conducted the first empirical study to explore the potential of improving traditional techniques for more practical log-based anomaly detection. In this work, we optimized the traditional unsupervised PCA (Principal Component Analysis) technique by incorporating a lightweight semantic-based log representation in it, called SemPCA , and conducted an extensive study to investigate the potential of SemPCA for more practical log-based anomaly detection. By comparing seven log-based anomaly detection techniques (including four DL-based techniques, two traditional techniques, and SemPCA ) on both public and industrial datasets, our results show that SemPCA achieves comparable effectiveness as advanced supervised/semi-supervised DL-based techniques while being much more stable under insufficient training data and more efficient, demonstrating that the traditional technique can still excel after small but useful adaptation.
Epidemic spread, parameter sensitivity and vaccination strategies on a random graph with overlapping communities

Our main goal is to examine the role of communities in epidemic spread in a random graph model. More precisely, we consider a random graph model which consists of overlapping complete graphs, representing households, workplaces, school classes, and which also has a simple geometric structure. We study the model's sensitivity to infection parameters and other tunable parameters of the model, which might be helpful in finding efficient social distancing strategies. We also quantitatively compare different vaccination strategies to see which order is the best to defend the most vulnerable groups or the population in general, and how important it is to gather and use information on the position of infected individuals in the network.
On Chernoff Lower-Bound of Outage Threshold for Non-Central $\chi^{2}$-Distributed MIMO Beamforming Gain

The cumulative distribution function (CDF) of a non-central $\chi^{2}$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For adaptive transmitters, it is important but mathematically challenging to determine the outage threshold for an extreme target outage probability (e.g., 10−5 or less). This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. The Cher-LB is then employed to predict the multi-antenna transmitter beamforming-gain in ultra-reliable and low-latency communication, concerning the first-order Markov time-varying channel. It is exhibited that, with the proposed Cher-LB, pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.
Sparks of Large Audio Models: A Survey and Outlook

This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the field of audio signal processing. Audio processing, with its diverse signal representations and a wide range of sources--from human voices to musical instruments and environmental sounds--poses challenges distinct from those found in traditional Natural Language Processing scenarios. Nevertheless, \textit{Large Audio Models}, epitomized by transformer-based architectures, have shown marked efficacy in this sphere. By leveraging massive amount of data, these models have demonstrated prowess in a variety of audio tasks, spanning from Automatic Speech Recognition and Text-To-Speech to Music Generation, among others. Notably, recently these Foundational Audio Models, like SeamlessM4T, have started showing abilities to act as universal translators, supporting multiple speech tasks for up to 100 languages without any reliance on separate task-specific systems. This paper presents an in-depth analysis of state-of-the-art methodologies regarding \textit{Foundational Large Audio Models}, their performance benchmarks, and their applicability to real-world scenarios. We also highlight current limitations and provide insights into potential future research directions in the realm of \textit{Large Audio Models} with the intent to spark further discussion, thereby fostering innovation in the next generation of audio-processing systems. Furthermore, to cope with the rapid development in this area, we will consistently update the relevant repository with relevant recent articles and their open-source implementations at https://github.com/EmulationAI/awesome-large-audio-models.
Agroclimatic aspects of rainfed agriculture in the Sudano-Sahelian zone

Characteristics of the rainfall in the Sudano-Sahelian Zone (SSZ) such as temporal and spatial variability, persistency, and geographical patterns of variability are described. A brief review of rainfall intensities, infiltration, and runoff is presented. Cumulative frequency distribution of maximum and minimum air temperatures at the time of sowing and harvesting of crops in the SSZ show that maximum temperatures at the time of sowing could exceed 40 deg C. Such high temperatures, together with wind erosion, can cause crop establishment problems. Maps of potential evapotranspiration and growing-season length are presented. The application of agroclimatic information for cropping strategies in the SSZ is described. A significant relationship is established between the onset of rains and the length of growing season for several locations based on which a new concept of "Weather-responsive crop management tactics" is proposed. The application of rainfall and drought probabilities and water balance is discussed.
BridgeData V2: A Dataset for Robot Learning at Scale

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research on scalable robot learning. BridgeData V2 contains 60,096 trajectories collected across 24 environments on a publicly available low-cost robot. BridgeData V2 provides extensive task and environment variability, leading to skills that can generalize across environments, domains, and institutions, making the dataset a useful resource for a broad range of researchers. Additionally, the dataset is compatible with a wide variety of open-vocabulary, multi-task learning methods conditioned on goal images or natural language instructions. In our experiments, we train 6 state-of-the-art imitation learning and offline reinforcement learning methods on our dataset, and find that they succeed on a suite of tasks requiring varying amounts of generalization. We also demonstrate that the performance of these methods improves with more data and higher capacity models, and that training on a greater variety of skills leads to improved generalization. By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods. Project page at https://rail-berkeley.github. io/bridgedata
APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT

We study diverse skill discovery in reward-free environments, aiming to discover all possible skills in simple grid-world environments where prior methods have struggled to succeed. This problem is formulated as mutual training of skills using an intrinsic reward and a discriminator trained to predict a skill given its trajectory. Our initial solution replaces the standard one-vs-all (softmax) discriminator with a one-vs-one (all pairs) discriminator and combines it with a novel intrinsic reward function and a dropout regularization technique. The combined approach is named APART: Diverse Skill Discovery using All Pairs with Ascending Reward and Dropout. We demonstrate that APART discovers all the possible skills in grid worlds with remarkably fewer samples than previous works. Motivated by the empirical success of APART, we further investigate an even simpler algorithm that achieves maximum skills by altering VIC, rescaling its intrinsic reward, and tuning the temperature of its softmax discriminator. We believe our findings shed light on the crucial factors underlying success of skill discovery algorithms in reinforcement learning.
Probing early modification of gravity with Planck, ACT and SPT

We consider a model of early modified gravity (EMG) that was recently proposed as a candidate to resolve the Hubble tension. The model consists of a scalar field σ with a nonminimal coupling (NMC) to the Ricci curvature of the form F(σ) = M pl 2+ξσ 2 and an effective mass induced by a quartic potential V(σ) = λσ 4/4. We present the first analyses of the EMG model in light of the latest ACT DR4 and SPT-3G data in combination with full Planck data, and find a ≳ 2σ preference for a non-zero EMG contribution from a combination of primary CMB data alone, mostly driven by ACT-DR4 data. This is different from popular `Early Dark Energy' models, which are detected only when the high-ℓ information from Planck temperature is removed. We find that the NMC plays a key role in controlling the evolution of density perturbations that is favored by the data over the minimally coupled case. Including measurements of supernovae luminosity distance from Pantheon+, baryonic acoustic oscillations and growth factor from BOSS, and CMB lensing of Planck leaves the preference unaffected. In the EMG model, the tension with SH 0ES is alleviated from ∼ 6σ to ∼ 3σ. Further adding SH 0ES data raises the detection of the EMG model above 5σ.
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning

Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge, it can also hinder local learning by interfering with local objectives. To address this problem, we propose a novel method, Federated Stabilized Orthogonal Learning (FedSOL), which adopts an orthogonal learning strategy to balance the two conflicting objectives. FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically, FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios.
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images. Starting from the Qwen-LM as a foundation, we endow it with visual capacity by the meticulously designed (i) visual receptor, (ii) input-output interface, (iii) 3-stage training pipeline, and (iv) multilingual multimodal cleaned corpus. Beyond the conventional image description and question-answering, we implement the grounding and text-reading ability of Qwen-VLs by aligning image-caption-box tuples. The resulting models, including Qwen-VL and Qwen-VL-Chat, set new records for generalist models under similar model scales on a broad range of visual-centric benchmarks (e.g., image captioning, question answering, visual grounding) and different settings (e.g., zero-shot, few-shot). Moreover, on real-world dialog benchmarks, our instruction-tuned Qwen-VL-Chat also demonstrates superiority compared to existing vision-language chatbots. Code, demo and models are available at https://github.com/QwenLM/Qwen-VL.
Machine learning in parameter estimation of nonlinear systems

Accurately estimating parameters in complex nonlinear systems is crucial across scientific and engineering fields. We present a novel approach for parameter estimation using a neural network with the Huber loss function. This method taps into deep learning's abilities to uncover parameters governing intricate behaviors in nonlinear equations. We validate our approach using synthetic data and predefined functions that model system dynamics. By training the neural network with noisy time series data, it fine-tunes the Huber loss function to converge to accurate parameters. We apply our method to damped oscillators, Van der Pol oscillators, Lotka-Volterra systems, and Lorenz systems under multiplicative noise. The trained neural network accurately estimates parameters, evident from closely matching latent dynamics. Comparing true and estimated trajectories visually reinforces our method's precision and robustness. Our study underscores the Huber loss-guided neural network as a versatile tool for parameter estimation, effectively uncovering complex relationships in nonlinear systems. The method navigates noise and uncertainty adeptly, showcasing its adaptability to real-world challenges.
Optimizing Neural Network Scale for ECG Classification

We study scaling convolutional neural networks (CNNs), specifically targeting Residual neural networks (ResNet), for analyzing electrocardiograms (ECGs). Although ECG signals are time-series data, CNN-based models have been shown to outperform other neural networks with different architectures in ECG analysis. However, most previous studies in ECG analysis have overlooked the importance of network scaling optimization, which significantly improves performance. We explored and demonstrated an efficient approach to scale ResNet by examining the effects of crucial parameters, including layer depth, the number of channels, and the convolution kernel size. Through extensive experiments, we found that a shallower network, a larger number of channels, and smaller kernel sizes result in better performance for ECG classifications. The optimal network scale might differ depending on the target task, but our findings provide insight into obtaining more efficient and accurate models with fewer computing resources or less time. In practice, we demonstrate that a narrower search space based on our findings leads to higher performance.
SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data

We propose Subject-Conditional Relation Detection SCoRD, where conditioned on an input subject, the goal is to predict all its relations to other objects in a scene along with their locations. Based on the Open Images dataset, we propose a challenging OIv6-SCoRD benchmark such that the training and testing splits have a distribution shift in terms of the occurrence statistics of $\langle$subject, relation, object$\rangle$ triplets. To solve this problem, we propose an auto-regressive model that given a subject, it predicts its relations, objects, and object locations by casting this output as a sequence of tokens. First, we show that previous scene-graph prediction methods fail to produce as exhaustive an enumeration of relation-object pairs when conditioned on a subject on this benchmark. Particularly, we obtain a recall@3 of 83.8% for our relation-object predictions compared to the 49.75% obtained by a recent scene graph detector. Then, we show improved generalization on both relation-object and object-box predictions by leveraging during training relation-object pairs obtained automatically from textual captions and for which no object-box annotations are available. Particularly, for $\langle$subject, relation, object$\rangle$ triplets for which no object locations are available during training, we are able to obtain a recall@3 of 33.80% for relation-object pairs and 26.75% for their box locations.
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition

Audio classification aims at recognizing audio signals, including speech commands or sound events. However, current audio classifiers are susceptible to perturbations and adversarial attacks. In addition, real-world audio classification tasks often suffer from limited labeled data. To help bridge these gaps, previous work developed neuro-inspired convolutional neural networks (CNNs) with sparse coding via the Locally Competitive Algorithm (LCA) in the first layer (i.e., LCANets) for computer vision. LCANets learn in a combination of supervised and unsupervised learning, reducing dependency on labeled samples. Motivated by the fact that auditory cortex is also sparse, we extend LCANets to audio recognition tasks and introduce LCANets++, which are CNNs that perform sparse coding in multiple layers via LCA. We demonstrate that LCANets++ are more robust than standard CNNs and LCANets against perturbations, e.g., background noise, as well as black-box and white-box attacks, e.g., evasion and fast gradient sign (FGSM) attacks.
Characterization of the gravitational wave spectrum from sound waves within the sound shell model

We compute the gravitational wave (GW) spectrum sourced by the sound waves produced during a first-order phase transition during radiation domination. The correlator of the velocity field is evaluated according to the sound shell model. In our derivation, we include the effects of the expansion of the Universe, showing their importance, in particular for sourcing processes with time duration comparable to the Hubble time. From the exact solution of the GW sourcing integral, we find a causal growth at small frequencies, $\Omega_{\rm GW} \sim k^3$, possibly followed by a linear regime $\Omega_{\rm GW} \sim k$ at intermediate $k$, depending on the phase transition parameters. Around the peak, we find a steep growth that approaches the $k^9$ scaling found in the sound shell model. This growth causes a bump around the GW spectrum peak, which may represent a distinctive feature of GWs produced from acoustic motion, since nothing similar has been observed for vortical turbulence. Nevertheless, we find that the $k^9$ scaling is much less extended than expected in the literature, and it does not necessarily appear. The dependence on the duration of the source, $\tau_{\rm fin} - \tau_*$, is quadratic at small frequencies $k$, and proportional to $\ln^2 (\tau_{\rm fin} {\cal H}_*)$ for an expanding Universe. At frequencies around the peak, the growth is suppressed by a factor $\Upsilon = 1 - 1/(\tau_{\rm fin} {\cal H}_*)$, which becomes linear for short duration. We discuss the linear or quadratic dependence on the source duration for stationary processes, which affects the amplitude of the GW spectrum, both in the causality tail and at the peak, showing that the assumption of stationarity is a very relevant one, as far as the GW spectral shape is concerned. Finally, we present a general semi-analytical template of the resulting GW spectrum, which depends on the parameters of the phase transition.
Quasiclassical integrability condition in AKNS scheme

In this paper, we study the condition of quasiclassical integrability of soliton equations. This condition states that the Hamiltonian structure of equations, which govern propagation of high-frequency wave packets, is preserved by the dispersionless flow independently of initial conditions. If this condition is fulfilled, then the carrier wave number of any packet is a certain function of local values of the dispersionless variables pertained to the soliton equations under consideration. We show for several examples that this function together with the dispersion relation for linear harmonic waves determine the quasiclassical limit of the Lax pair functions in the scalar representation of the Ablowitz-Kaup-Newell-Segur scheme.