Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset

Early detection of esophagitis is important because this condition can progress to cancer if left untreated. However, the accuracies of different deep learning models in detecting esophagitis have yet to be compared. Thus, this study aimed to compare the accuracies of convolutional neural network models (GoogLeNet, ResNet-50, MobileNet V2, and MobileNet V3) in detecting esophagitis from the open Kvasir dataset of endoscopic images. Results showed that among the models, GoogLeNet achieved the highest F1-scores. Based on the average of true positive rate, MobileNet V3 predicted esophagitis more confidently than the other models. The results obtained using the models were also compared with those obtained using SHapley Additive exPlanations and Gradient-weighted Class Activation Mapping.
Extreme Q-Learning: MaxEnt RL without Entropy

Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by \emph{10+ points} on the challenging Franka Kitchen tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks. Visualizations and code can be found on our website at https://div99.github. io/XQL/.
A direct construction of optimal 2d-zcacs with flexible array size and large set size

In this paper, we propose a direct construction of optimal two-dimensional Z-complementary array code sets (2D-ZCACS) using multivariable functions (MVFs). In contrast to earlier works, the proposed construction allows for a flexible array size and a large set size. Additionally, the proposed design can be transformed into a one-dimensional Z-complementary code set (1D-ZCCS). Many of the 1D-ZCCS described in the literature appeared to be special cases of this proposed construction. At last, we compare our work with the current state of the art and then draw our conclusions.
Training trajectories, mini-batch losses and the curious role of the learning rate

Stochastic gradient descent plays a fundamental role in nearly all applications of deep learning. However its ability to converge to a global minimum remains shrouded in mystery. In this paper we propose to study the behavior of the loss function on fixed mini-batches along SGD trajectories. We show that the loss function on a fixed batch appears to be remarkably convex-like. In particular for ResNet the loss for any fixed mini-batch can be accurately modeled by a quadratic function and a very low loss value can be reached in just one step of gradient descent with sufficiently large learning rate. We propose a simple model that allows to analyze the relationship between the gradients of stochastic mini-batches and the full batch. Our analysis allows us to discover the equivalency between iterate aggregates and specific learning rate schedules. In particular, for Exponential Moving Average (EMA) and Stochastic Weight Averaging we show that our proposed model matches the observed training trajectories on ImageNet. Our theoretical model predicts that an even simpler averaging technique, averaging just two points a many steps apart, significantly improves accuracy compared to the baseline. We validated our findings on ImageNet and other datasets using ResNet architecture.
The Usability of Advanced Type Systems: Rust as a Case Study

Advanced type systems that enforce various correctness and safety guarantees--such as linear and ownership types--have a long history in the Programming Languages research community. Despite this history, a human-centered evaluation of these type systems and their usability was all but absent, with empirical evaluations limited to testing their expressiveness in programs written by experts, i.e. the creators of the type system. In the past few years, this has begun to change with the adoption of a version of affine types and ownership in the popular Rust programming language. With the increase in Rust's popularity, various studies have begun empirically evaluating the usability of Rust's Ownership and Lifetime rules, providing a breadth of qualitative and quantitative information on the usability of such type systems. They found that despite Rust's general success in achieving its promise of safety and performance, these rules come with a steep learning curve and have been repeatedly cited as a barrier to adopting Rust. In this report, I provide a brief history of linear types and region-based memory management, which directly inspired Rust's type system. I then introduce Rust's Ownership and Lifetime rules, and present the state-of-the-art in academic research into their usability. I discuss both theoretical arguments and empirical evidence for why these rules are difficult to learn and apply, and survey existing work on addressing some of these difficulties. I also draw from broader works in the HCI and CS Education communities to recommend future work in this area.
CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior

Speech-driven 3D facial animation has been widely studied, yet there is still a gap to achieving realism and vividness due to the highly ill-posed nature and scarcity of audio-visual data. Existing works typically formulate the cross-modal mapping into a regression task, which suffers from the regression-to-mean problem leading to over-smoothed facial motions. In this paper, we propose to cast speech-driven facial animation as a code query task in a finite proxy space of the learned codebook, which effectively promotes the vividness of the generated motions by reducing the cross-modal mapping uncertainty. The codebook is learned by self-reconstruction over real facial motions and thus embedded with realistic facial motion priors. Over the discrete motion space, a temporal autoregressive model is employed to sequentially synthesize facial motions from the input speech signal, which guarantees lip-sync as well as plausible facial expressions. We demonstrate that our approach outperforms current state-of-the-art methods both qualitatively and quantitatively. Also, a user study further justifies our superiority in perceptual quality. Code and video demo are available at https://doubiiu.github. io/projects/codetalker.
A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument

ABSTRACT We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it to measurements taken by the RAman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland. We give a detailed description of using a Bayesian method to retrieve temperature profiles including estimates of the uncertainty due to the network weights and the statistical uncertainty of the measurements. We trained our model using lidar measurements under different atmospheric conditions, and we tested our model using measurements not used for training the network. The computed temperature profiles extend over the altitude range of 0.7 km to 6 km. The mean bias estimate of our temperatures relative to the MeteoSwiss standard processing algorithm does not exceed 0.05 K at altitudes below 4.5 km, and does not exceed 0.08 K in an altitude range of 4.5 km to 6 km. This agreement shows that the neural network estimated temperature profiles are in excellent agreement with the standard algorithm. The method is robust and is able to estimate the temperature profiles with high accuracy for both clear and cloudy conditions. Moreover, the trained model can provide the statistical and model uncertainties of the estimated temperature profiles. Thus, the present study is a proof of concept that the trained NNs are able to generate temperature profiles along with a full-budget uncertainty. We present case studies showcasing the Bayesian neural network estimations for day and night measurements, as well as in clear and cloudy conditions. We have concluded that the proposed Bayesian neural network is an appropriate method for the statistical retrieval of temperature profiles.
Topics as Entity Clusters: Entity-based Topics from Language Models and Graph Neural Networks

Topic models aim to reveal the latent structure behind a corpus, typically conducted over a bag-of-words representation of documents. In the context of topic modeling, most vocabulary is either irrelevant for uncovering underlying topics or contains strong relationships with relevant concepts, impacting the interpretability of these topics. Furthermore, their limited expressiveness and dependency on language demand considerable computation resources. Hence, we propose a novel approach for cluster-based topic modeling that employs conceptual entities. Entities are language-agnostic representations of real-world concepts rich in relational information. To this end, we extract vector representations of entities from (i) an encyclopedic corpus using a language model; and (ii) a knowledge base using a graph neural network. We demonstrate that our approach consistently outperforms other state-of-the-art topic models across coherency metrics and find that the explicit knowledge encoded in the graph-based embeddings provides more coherent topics than the implicit knowledge encoded with the contextualized embeddings of language models.
Long-Time Behaviour of an Advection-Selection Equation

We study the long-time behaviour of the advection-selection equation $$\partial_tn(t,x)+\nabla \cdot \left(f(x)n(t,x)\right)=\left(r(x)-\rho(t)\right)n(t,x),\quad \rho(t)=\int_{\mathbb{R}^d}{n(t,x)dx}\quad t\geq 0, \; x\in \mathbb{R}^d,$$ with an initial condition $n(0, \cdot)=n^0$. In the field of adaptive dynamics, this equation typically describes the evolution of a phenotype-structured population over time. In this case, $x\mapsto n(t,x)$ represents the density of the population characterised by a phenotypic trait $x$, the advection term `$\nabla \cdot \left(f(x)n(t,x)\right)$' a cell differentiation phenomenon driving the individuals toward specific regions, and the selection term `$\left(r(x)-\rho(t)\right)n(t,x)$' the growth of the population, which is of logistic type through the total population size $\rho(t)=\int_{\mathbb{R}^d}{n(t,x)dx}$. In the one-dimensional case $x\in \mathbb{R}$, we prove that the solution to this equation can either converge to a weighted Dirac mass or to a function in $L^1$. Depending on the parameters $n^0$, $f$ and $r$, we determine which of these two regimes of convergence occurs, and we specify the weight and the point where the Dirac mass is supported, or the expression of the $L^1$-function which is reached.
Graphical quantum Clifford-encoder compilers from the ZX calculus

We present a quantum compilation algorithm that maps Clifford encoders, encoding maps for stabilizer quantum codes, to a unique graphical representation in the ZX calculus. Specifically, we develop a canonical form in the ZX calculus and prove canonicity as well as efficient reducibility of any Clifford encoder into the canonical form. The diagrams produced by our compiler visualize information propagation and entanglement structure of the encoder, revealing properties that may be obscured in the circuit or stabilizer-tableau representation. Consequently, our canonical representation may be an informative technique for the design of new stabilizer quantum codes via graph theory analysis.
Holographic recording in TiO2 nanoparticle-dispersed methacrylate photopolymer films

We demonstrate permanent holographic storage in the green with high diffraction efficiency and recording sensitivity in TiO2 nanoparticle-dispersed methacrylate photopolymer films. It is shown that the diffraction efficiency as well as the recording sensitivity significantly increase with an increase of nanoparticle concentration. It is also found that volumetric shrinkage during holographic exposure is noticeably suppressed by inclusion of the nanoparticles.
Vertical Structure of Local Fronts Observed in Kanto, Japan

This paper focuses on two cases of a local front that formed ahead of a synoptic cold front under a southwesterly inflow in the Kanto region. Vertical sounding data provided by an observation network of the Tsukuba Area Precipitation Studies (TAPS) enabled a detailed investigation of the mesoscale structure in the vicinity of the local front from its formation through dissipation. Despite some differences in synoptic features, the local front exhibited essential similarities in vertical structure between the two cases, as well as in horizontal structure. The local front, with WSW-ENE orientation, was well defined even in the daytime. A stable layer with a thickness of about 400 m formed on the cold side of the developed local front. As in the upper layer of warm inflow, southwesterly winds prevailed in this stable layer, except for the lowest 50-200 m. The maintenance mechanism of the local front is discussed. The evolution of the local front largely depends on the development of the stable layer on its cold side. In both cases, frontal formation began in the nighttime. A nocturnal cooling on the land surface contributed to forming a cold air mass on the northern side of the local front. In addition, the southwesterly inflow caused warming just above the surface-based cooling layer, which intensified the stratification. Furthermore, the observed vertical structure suggests that the locally modified circulation affected the frontal evolution. Two orographic effects on the circulation, 1) intensification of the inflow over the eastern part, and 2) downslope wind over the western part, both related to the mountains in the west of the Kanto region are expected. Especially, the downslope wind can reasonably explain the daytime persistence of the local front through the enhancement of stratification by adiabatic warming, and resultant influences on the mass transport.
Cloning Dropouts: Implications for Galaxy Evolution at High Redshift

The evolution of high-redshift galaxies in the two Hubble Deep Fields, HDF-N and HDF-S, is investigated using a cloning technique that replicates z ~ 2-3 U dropouts to higher redshifts, allowing a comparison with the observed B and V dropouts at higher redshifts (z ~ 4-5). We treat each galaxy selected for replication as a set of pixels that are k-corrected to higher redshift, accounting for resampling, shot noise, surface-brightness dimming, and the cosmological model. We find evidence for size evolution (a 1.7-fold increase) from z ~ 5 to 2.7 for flat geometries (ΩM + ΩΛ = 1.0). Simple scaling laws for this cosmology predict that size evolution scales as (1 + z)-1, consistent with our result. The UV luminosity density shows a similar increase (×1.85) from z ~ 5 to 2.7, with minimal evolution in the distribution of intrinsic colors for the dropout population. In general, these results indicate less evolution than was previously reported, and therefore a higher luminosity density at z ~ 4-5 (~50% higher) than other estimates. We argue that the present technique is the preferred way to understand evolution across samples with differing selection functions, the most relevant difference here being the color cuts and surface brightness thresholds [e.g., due to the (1 + z)4 cosmic surface brightness dimming effect].
Robust adaptive tracking control of uncertain discrete time systems

In this paper, the problem of robust adaptive tracking for uncertain discrete‐time systems is considered from the slowly varying systems point of view. The class of uncertain discrete‐time systems considered is subjected to both 𝓁∞ to 𝓁∞ bounded unstructured uncertainty and external additive bounded disturbances. A priori knowledge of the dynamic model of the reference signal to be tracked is not completely known. For such problem, an indirect adaptive tracking controller is obtained by frozen‐time controllers that at each time optimally robustly stabilize the estimated models of the plant and minimize the worst‐case steady‐state absolute value of the tracking error of the estimated model over the model uncertainty. Based on 𝓁∞ to 𝓁∞ stability and performance of slowly varying system found in the literature, the proposed adaptive tracking scheme is shown to have good robust stability. Moreover, a computable upper bound on the size of the unstructured uncertainty permitted by the adaptive system and a computable tight upper bound on asymptotic robust steady‐state tracking performance are provided. Copyright © 2005 John Wiley & Sons, Ltd.
Coupling or decoupling bed and flow dynamics: Fast and slow sediment waves at high Froude numbers

The effect of coupling or decoupling bed and flow dynamics is analyzed in the framework of a linear analysis of the stability of a uniform, rotational, two-dimensional flow in an infinitely wide channel with a bed composed by incoherent sediments. It is shown that results obtained with the coupled analysis in term of instability of slow sediment waves of the dune/antidune kind are quite similar to the results obtained making use of the “quasisteady hypothesis,” which forms the basis of most of the existing linear stability analyses of bedforms and formally justifies the decoupling procedure. Small differences can be observed, for Froude numbers of O(1), in the surroundings of the marginal curves that bound the instability regions, mainly due to the removal of the artificial resonance that is introduced in the analysis when the quasisteady hypothesis is enforced. The decoupled approach, however, completely wipes out a mode of instability associated with fast-moving sediment waves, which appear at high Frou.
Exact Bernoulli–Euler dynamic stiffness matrix for a range of tapered beams

Bernoulli-Euler theory and Bessel functions are used to obtain explicit expressions for the exact dynamic stiffnesses for axial, torsional and flexural vibrations of any beam which is tapered such that A varies as yn and GJ and I both vary as y(n + 2), where A, GJ and I have their usual meanings; y = (cx/L) + 1; c is a constant such that c > − 1; L is the length of the beam; and x is the distance from one end of the beam. Numerical checks give better than seven-figure agreement with the stiffnesses obtained by extrapolation from stepped beams with 400 and 500 uniform elements. A procedure is given for calculating the number of natural frequencies exceeded by any trial frequency when the ends of the member are clamped. This enables an existing algorithm to be used to obtain the natural frequencies of structures which contain tapered members.
Adipokine levels and their association with clinical disease severity in patients with dengue

Adipokines have not been studied in acute dengue, despite their emerging role in inducing and regulating inflammation. Therefore, we sought to identify adipokine levels in patients with varying severities of acute dengue to understand their role in disease pathogenesis. We determined the levels of leptin, resistin, omentin, adiponectin, as well as IFNb, and NS1 using quantitative ELISA in patients with dengue fever (DF=49) and dengue haemorrhagic fever (DHF=22) at admission (febrile phase) and at the time of discharge (recovery phase). The viral loads and serotypes of all samples were quantified using quantitative real-time RT-PCR. Resistin levels (p =0.04) and omentin (p=0.006) levels were significantly higher in patients who developed DHF. Omentin levels in the febrile phase also correlated with the AST (Spearman's r=0.38, p=0.001) and ALT levels (Spearman's r=0.24, p=0.04); as well as serum leptin levels with both AST (Spearman's r=0.27, p=0.02) and ALT (Spearman's r=0.28, p=0.02). Serum adiponectin levels in the febrile phase did not correlate with any of the other adipokines or with liver enzymes, but inversely correlated with CRP levels (Spearman's r=-0.31, p=0.008). Although not significant (p=0.14) serum IFN{beta} levels were lower in the febrile phase in those who progressed to develop DHF (median 0, IQR 0 to 39.4 pg/ml), compared to those who had DF (median 37.1, IQR 0 to 65.6 pg.ml). The data suggest that adipokines are likely to play a role in the pathogenesis of dengue, which should be further explored for the potential to be used as prognostic markers and as therapeutic targets.
SPECTROSCOPIC STUDIES OF POLARONIC AND BIPOLARONIC SPECIES IN N-DOPED POLY(PARAPHENYLENEVINYLENE)

In this paper, we present a detailed study of the vibrational properties of neutral and $n$-doped polyparaphenylene-vinylene (PPV). Resonance Raman scattering and infrared-absorption results are analyzed and interpreted by the use of dynamical calculations based on a valence force-field model. Experimental data include those obtained from related oligomers, i.e., trans-stilbene and distyrylbenzene with the assumption of similar geometry parameters. Force constants are optimized according to assignments of Raman and ir vibrational bands of the series of compounds. In the case of sodium-doped PPV, new Raman bands are observed showing different resonance behaviors. These new features are investigated as a function of the doping level, leading to the conclusion that polarons and bipolarons of short lengths coexist in thermodynamical equilibrium. Doping-induced bands are interpreted in terms of changes in carbon-carbon force constants. Such modifications in the electronic distribution agree well with the quinoid character expected upon doping on the polymeric chains.
Persistent confined migration confers permanent nuclear and functional changes in migrating cells

Nuclear deformability plays a critical role in cell migration. During this process, the remodeling of internal components of the nucleus has a direct impact on DNA damage and cell behavior; however, how persistent migration promotes nuclear changes leading to phenotypical and functional consequences remains poorly understood. Here, we described that the persistent migration through physical barriers was sufficient to promote permanent modifications in migratory-altered cells. We found that lamin B1 altered its localization, concomitant with morphological and transcriptional changes. Migratory-altered cells showed alterations in cellular functions such as DNA repair and cell migration. We applied biochemical and biophysical approaches to identify that confined conditions altered the biomechanical response of the nucleus. Mechanistically, we determined that actin dynamics controlled the redistribution of lamin, and the basal levels of DNA damage in migratory-altered cells. Our observations reveal a novel role for confined cell conditions in consistent nuclear and genomic alterations that might handle the genetic instability and cellular heterogeneity in aging diseases and cancer. Highlights Persistent confined migration promotes permanent mophological changes. Lamin B1 is redistribution in the nucleus of migratory altered cells. Migratory-altered cells exhibit transcriptional and functional changes related to cell migration and survival. Actin polymerization controls nuclear changes induced by cell migration.