Miracle-dz/UniFAR-FTUs huggingface dataset:
Imrpoving Strain Estimation in Breast Ultrasound Images Using Novel 1.5D Approach (Simulation and In-vivo results

Ultrasound elastography is the method to image the elasticity of compliant tissues due to a mechanical compression applied to it. In elastography, the local strain of explored tissue is estimated by analyzing the echo signals. This is accomplished by a sonographer who uses ultrasound transducer to apply pressure on the tissue area causing displacement of the tissue. A set of data is obtained before and after the compression. This stress along the axial direction not only causes deformation in that direction but it can also cause lateral deformation in the tissue. This relative deformation is later calculated using tracking algorithms. Finally, estimating the elasticity, images are formed. In this paper, we introduce a novel strain estimator. The proposed 1.5D strain estimator uses 1D windows for swift computations and searches the lateral direction for taking the non-axial movement into account. We have investigated the performance of our estimator using simulated and in-vivo data of breast tissue. The performance of our method is verified by comparing with different methods up to 16\% applied strain. The proposed estimator has exhibited a superior performance compared to the conventional methods. The image quality significantly improves, especially in the presence of higher applied strain, when the non-axial motion is significant.
Gaussian Processes on Distributions based on Regularized Optimal Transport

We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport. We propose an Hilbertian embedding of the space of probabilities using their Sinkhorn potentials, which are solutions of the dual entropic relaxed optimal transport between the probabilities and a reference measure $\mathcal{U}$. We prove that this construction enables to obtain a valid kernel, by using the Hilbert norms. We prove that the kernel enjoys theoretical properties such as universality and some invariances, while still being computationally feasible. Moreover we provide theoretical guarantees on the behaviour of a Gaussian process based on this kernel. The empirical performances are compared with other traditional choices of kernels for processes indexed on distributions.
A Near-Sensor Processing Accelerator for Approximate Local Binary Pattern Networks

In this work, a high-speed and energy-efficient comparator-based <underline>N</underline>ear-<underline>S</underline>ensor <underline>L</underline>ocal <underline>B</underline>inary <underline>P</underline>attern accelerator architecture (NS-LBP) is proposed to execute a novel local binary pattern deep neural network. First, inspired by recent LBP networks, we design an approximate, hardware-oriented, and multiply-accumulate (MAC)-free network named Ap-LBP for efficient feature extraction, further reducing the computation complexity. Then, we develop NS-LBP as a processing-in-SRAM unit and a parallel in-memory LBP algorithm to process images near the sensor in a cache, remarkably reducing the power consumption of data transmission to an off-chip processor. Our circuit-to-application co-simulation results on MNIST and SVHN datasets demonstrate minor accuracy degradation compared to baseline CNN and LBP-network models, while NS-LBP achieves 1.25 GHz and an energy-efficiency of 37.4 TOPS/W. NS-LBP reduces energy consumption by 2.2× and execution time by a factor of 4× compared to the best recent LBP-based networks.
Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction

Aspect Based Sentiment Analysis is the most granular form of sentiment analysis that can be performed on the documents / sentences. Besides delivering the most insights at a finer grain, it also poses equally daunting challenges. One of them being the shortage of labelled data. To bring in value right out of the box for the text data being generated at a very fast pace in today’s world, unsupervised aspect-based sentiment analysis allows us to generate insights without investing time or money in generating labels. From topic modelling approaches to recent deep learning-based aspect extraction models, this domain has seen a lot of development. One of the models that we improve upon is ABAE that reconstructs the sentences as a linear combination of aspect terms present in it, In this research we explore how we can use information from another unsupervised model to regularize ABAE, leading to better performance. We contrast it with baseline rule based ensemble and show that the ensemble methods work better than the individual models and the regularization based ensemble performs better than the rule-based one.
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition

Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted multi-task learning to aid IDRR through explicit discourse relation recognition (EDRR) or utilized dependencies between discourse relation labels to constrain model predictions. But these methods still performed poorly on fine-grained IDRR and even utterly misidentified on most of the few-shot discourse relation classes. To address these problems, we propose a novel Prompt-based Connective Prediction (PCP) method for IDRR. Our method instructs large-scale pre-trained models to use knowledge relevant to discourse relation and utilizes the strong correlation between connectives and discourse relation to help the model recognize implicit discourse relations. Experimental results show that our method surpasses the current state-of-the-art model and achieves significant improvements on those fine-grained few-shot discourse relation. Moreover, our approach is able to be transferred to EDRR and obtain acceptable results. Our code is released in https://github.com/zh-i9/PCP-for-IDRR.
Language Model Decoding as Likelihood–Utility Alignment

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm remain unclear. Previous works only compare decoding algorithms in narrow scenarios, and their findings do not generalize across tasks. We argue that the misalignment between the model’s likelihood and the task-specific notion of utility is the key factor in understanding the effectiveness of decoding algorithms. To structure the discussion, we introduce a taxonomy of misalignment mitigation strategies (MMSs), providing a unifying view of decoding as a tool for alignment. The MMS taxonomy groups decoding algorithms based on their implicit assumptions about likelihood–utility misalignment, yielding general statements about their applicability across tasks. Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm. Crucially, our analysis is the first to relate likelihood-based decoding algorithms with algorithms that rely on external information, such as value-guided methods and prompting, and covers the most diverse set of tasks to date. Code, data, and models are available at https://github.com/epfl-dlab/understanding-decoding.
Stability of the Néel quantum critical point in the presence of Dirac fermions

We investigate the stability of the N\'eel quantum critical point of two-dimensional quantum antiferromagnets, described by a non-linear $\sigma$ model (NL$\sigma$M), in the presence of a Kondo coupling to $N_f$ flavours of two-component Dirac fermion fields. The long-wavelength order parameter fluctuations are subject to Landau damping by electronic particle-hole fluctuations. Using momentum-shell RG, we demonstrate that the Landau damping is weakly irrelevant at the N\'eel quantum critical point, despite the fact that the corresponding self-energy correction dominates over the quadratic gradient terms in the IR limit. In the ordered phase, the Landau damping increases under the RG, indicative of damped spin-wave excitations. Although the Kondo coupling is weakly relevant, sufficiently strong Landau damping renders the N\'eel quantum critical point quasi-stable for $N_f\ge 4$ and thermodynamically stable for $N_f<4$. In the latter case, we identify a new multi-critical point which describes the transition between the N\'eel critical and Kondo run-away regimes. The symmetry breaking at this fixed point results in the opening of a gap in the Dirac fermion spectrum. Approaching the multi-critical point from the disordered phase, the fermionic quasiparticle residue vanishes, giving rise to non-Fermi-liquid behavior.
A obstrução de Euler de uma função

Our goal in this work is to study the Euler obstruction of a function, this concept was defined by J.-P. Brasselet, D. Massey, A. J. Parameswaran and J. Seade, and it generalizes two important concepts, the Euler obstruction defined by R. D. MacPherson and the Milnor number of a function. The main result of this study shows the relation between the Euler obstruction and the Euler obstruction of a function.
Bump Morphology of the CMAGIC Diagram

We apply the color–magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color–magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter, ω XY , the size of the “bump” feature near maximum brightness for arbitrary filters X and Y. We find a significant correlation between the slope of the linear region, β XY , in the CMAGIC diagram and ω XY . These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally, ω XY is computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature and β XY can be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities.
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications

We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner's objective is not convex-concave (and so the minimax theorem does not apply), we give a simple algorithm that can compete with the setting in which the adversary must announce their action first, with optimally diminishing regret. We demonstrate the power of our framework by using it to (re)derive optimal bounds and efficient algorithms across a variety of domains, ranging from multicalibration to a large set of no regret algorithms, to a variant of Blackwell's approachability theorem for polytopes with fast convergence rates. As a new application, we show how to ``(multi)calibeat'' an arbitrary collection of forecasters -- achieving an exponentially improved dependence on the number of models we are competing against, compared to prior work.
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning

Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steering a chatbot's output by plugging in an appropriate prompt. Gradient-based methods are often used to perturb the prompts. However, some language models are not even available to the public. In this work, we first explored the combination of prompting and reinforcement learning (RL) to steer models' generation without accessing any of the models' parameters. Second, to reduce the training effort and enhance the generalizability to the unseen task, we apply multi-task learning to make the model learn to generalize to new tasks better. The experiment results show that our proposed method can successfully control several state-of-the-art (SOTA) dialogue models without accessing their parameters. Furthermore, the model demonstrates the strong ability to quickly adapt to an unseen task in fewer steps than the baseline model.
Algorithms for Matrix Multiplication via Sampling and Opportunistic Matrix Multiplication

Karppa&Kaski (2019) proposed a novel ``broken"or ``opportunistic"matrix multiplication algorithm, based on a variant of Strassen's algorithm, and used this to develop new algorithms for Boolean matrix multiplication, among other tasks. Their algorithm can compute Boolean matrix multiplication in $O(n^{2.778})$ time. While asymptotically faster matrix multiplication algorithms exist, most such algorithms are infeasible for practical problems. We describe an alternative way to use the broken multiplication algorithm to approximately compute matrix multiplication, either for real-valued or Boolean matrices. In brief, instead of running multiple iterations of the broken algorithm on the original input matrix, we form a new larger matrix by sampling and run a single iteration of the broken algorithm on it. Asymptotically, our algorithm has runtime $O(n^{2.763})$, a slight improvement over the Karppa-Kaski algorithm. Since the goal is to obtain new practical matrix-multiplication algorithms, we also estimate the concrete runtime for our algorithm for some large-scale sample problems. It appears that for these parameters, further optimizations are still needed to make our algorithm competitive.
Gauge/string duality applied to heavy-ion collisions: limitations, insights and prospects

Over the last decade a fruitful interplay has developed between analyses of strongly coupled non-Abelian plasmas via the gauge/string duality and the phenomenology of the quark–gluon plasma created in heavy-ion collisions. The reasons why the gauge/string duality is not a precision tool for QCD physics at present are reviewed, with emphasis on conceptual issues. It is then argued that, nevertheless, the duality can provide valuable insights at both the quantitative and the qualitative levels. This is illustrated with a few examples and concluded with a brief discussion of future prospects.
Probabilities of voids

Une methode de determination des densites de probabilite des vides spheriques dans la distribution a grande echelle des galaxies est presentee. Une application a des modeles de matiere sombre froide a agencement poissonien, biaises ou non-biaises, est realisee. La dependance de la probabilite de vides sur le facteur de biais est analysee.
Bias in low-multipole CMB reconstructions

The large-angle, low multipole cosmic microwave background (CMB) provides a unique view of the largest angular scales in the Universe. Study of these scales is hampered by the facts that we have only one Universe to observe, only a few independent samples of the underlying statistical distribution of these modes, and an incomplete sky to observe due to the interposing Galaxy. Techniques for reconstructing a full sky from partial sky data are well known and have been applied to the large angular scales. In this work we critically study the reconstruction process and show that, in practise, the reconstruction is biased due to leakage of information from the region obscured by foregrounds to the region used for the reconstruction. We conclude that, despite being suboptimal in a technical sense, using the unobscured region without reconstructing is the most robust measure of the true CMB sky. We also show that for noise free data reconstructing using the usual optimal, unbiased estimator may be employed without smoothing thus avoiding the leakage problem. Unfortunately, directly applying this to real data with noise and residual, unmasked foregrounds yields highly biased reconstructions requiring further care to apply this method successfully to real-world CMB.
HST EMISSION LINE GALAXIES AT z ∼ 2: COMPARING PHYSICAL PROPERTIES OF LYMAN ALPHA AND OPTICAL EMISSION LINE SELECTED GALAXIES

We compare the physical and morphological properties of z ∼ 2 Lyα emitting galaxies (LAEs) identified in the HETDEX Pilot Survey and narrow band studies with those of z ∼ 2 optical emission line selected galaxies (oELGs) identified via HST WFC3 infrared grism spectroscopy. Both sets of galaxies extend over the same range in stellar mass ( 7.5 < log M / M ⊙ < 10.5 ? > ), size (0.5 < R < 3.0 kpc), and star formation rate ( ∼ 1 < SFR < 100 M ⊙ ? > yr−1). Remarkably, a comparison of the most commonly used physical and morphological parameters—stellar mass, half-light radius, UV slope, SFR, ellipticity, nearest neighbor distance, star formation surface density, specific SFR, [O iii] luminosity, and [O iii] equivalent width—reveals no statistically significant differences between the populations. This suggests that the processes and conditions which regulate the escape of Lyα from a z ∼ 2 star-forming galaxy do not depend on these quantities. In particular, the lack of dependence on the UV slope suggests that Lyα emission is not being significantly modulated by diffuse dust in the interstellar medium. We develop a simple model of Lyα emission that connects LAEs to all high-redshift star-forming galaxies where the escape of Lyα depends on the sightline through the galaxy. Using this model, we find that mean solid angle for Lyα escape is &OHgr; Ly &agr; = 2.4 ± 0.8 ? > steradians; this value is consistent with those calculated from other studies.
A neural mechanism underlying predictive visual motion processing in patients with schizophrenia

Psychotic symptoms may be traced back to sensory sensitivity. Thereby, visual motion (VM) processing particularly has been suggested to be impaired in schizophrenia (SCZ). In healthy brains, VM underlies predictive processing within hierarchically structured systems. However, less is known about predictive VM processing in SCZ. Therefore, we performed fMRI during a VM paradigm with three conditions of varying predictability, i.e., predictable-, random-, and arbitrary motion. The study sample comprised 17 SCZ patients and 23 healthy controls. We calculated general linear model (GLM) analysis to assess group differences in VM processing across motion conditions. Here, we identified significantly lower activity in right temporoparietal junction (TPJ) for SCZ patients. Therefore, right TPJ was set as seed for connectivity analyses. For patients, across conditions we identified increased connections to higher regions, namely medial prefrontal cortex, or paracingulate gyrus. Healthy subjects activated sensory regions as area V5, or superior parietal lobule. Since TPJ operates as hub modulating network shifts, aberrant functional connectivity with higher structures may thus reflect a compensatory mechanism co-occurring to impaired TPJ activity in SCZ. In sum, these altered neural patterns provide a framework for future studies focusing on predictive VM processing to identify potential biomarkers of psychosis. Keywords: Temporo-parietal junction, functional connectivity, sensory dysfunction, vision, hypersensitivity, attention, contextual updating, psychosis, prediction error
A Ramsey Bound on Jordan Stable Sets

Jordan [2006] defined ‘pillage games’, a class of cooperative games whose dominance operator represents a ‘power function’ constrained by monotonicity axioms. In this environment, he proved that stable sets must be finite. We bound their cardinality above by a Ramsey number and show this bound to be tight for two agents. More generally, it is not tight as it does not make use of structural information across partial orders on the stable set.
Managing the Choice of Surrogate Variables and the Use of Approximation Models to Optimize Expensive Functions

Abstract : We propose research in three basic areas. First, it is standard in engineering practice to use approximation models of expensive simulations to drive nonlinear programming algorithms. An open question, which we will investigate using well-established notions from the literature on trust-region methods, is how to manage the interplay between optimization and the fidelity of the approximation models to insure that the process converges to a reasonable solution of the original design problem. It is also standard in engineering design to reduce the dimension of the optimization problem to be solved by using a technique known as variable linking. We plan to generalize this concept using some recent ideas from the optimization community to design robust ways to decompose a single iteration of any optimization method into a multi-phase process. First, an algorithm is applied to solve subproblems posed on subspaces of lower dimension, and then to solve the full problem on the affine hull defined by the current iterate and the solutions found for the subproblems. Crucial questions remain about the convergence of a practical implementation of this process. Finally, we will continue our research to extend and analyze pattern search methods. Pattern search methods can be successfully applied when only ranking (ordinal) information is available and when derivatives are either unavailable or unreliable. Since these are situations that occur in the design problems of interest, we propose to continue our investigation of pattern search methods. In particular, we will examine robust extensions to handle problems with bound constraints.
Synthesis, characterization, and applications of gold nanoparticles in development of plasmonic optical fiber-based sensors

Gold nanoparticles (Au-NPs) are readily used nanoparticles which finds applications in fields like biosensors, drug delivery, optical bioimaging and many state of art systems used for detection. In the recent years fiber optic sensors have seen utilization of Au-NPs along with other nanoparticles for implementation of sensors for sensing various biomolecules like cholesterol, glucose, and uric acid. The cancer cells, creatinine and bacteria can also be detected with the fiber optic sensors. Given the significance of Au-NPs in fiber optic sensors, the current work is a review of the synthesis, the common methods used for characterization, and the applications of Au-NPs. It is important to discuss and analyse the work reported in the literature to understand the trend and gaps in developing plasmonic optical fiber sensors.