Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimally Controllable Perceptual Lossy Compression

Recent studies in lossy compression show that distortion and perceptual quality are at odds with each other, which put forward the tradeoff between distortion and perception (D-P). Intuitively, to attain different perceptual quality, different decoders have to be trained. In this paper, we present a nontrivial finding that only two decoders are sufficient for optimally achieving arbitrary (an infinite number of different) D-P tradeoff. We prove that arbitrary points of the D-P tradeoff bound can be achieved by a simple linear interpolation between the outputs of a minimum MSE decoder and a specifically constructed perfect perceptual decoder. Meanwhile, the perceptual quality (in terms of the squared Wasserstein-2 distance metric) can be quantitatively controlled by the interpolation factor. Furthermore, to construct a perfect perceptual decoder, we propose two theoretically optimal training frameworks. The new frameworks are different from the distortion-plus-adversarial loss based heuristic framework widely used in existing methods, which are not only theoretically optimal but also can yield state-of-the-art performance in practical perceptual decoding. Finally, we validate our theoretical finding and demonstrate the superiority of our frameworks via experiments. Code is available at: https://github.com/ZeyuYan/Controllable-Perceptual-Compression
On the Limitations of Stochastic Pre-processing Defenses

Defending against adversarial examples remains an open problem. A common belief is that randomness at inference increases the cost of finding adversarial inputs. An example of such a defense is to apply a random transformation to inputs prior to feeding them to the model. In this paper, we empirically and theoretically investigate such stochastic pre-processing defenses and demonstrate that they are flawed. First, we show that most stochastic defenses are weaker than previously thought; they lack sufficient randomness to withstand even standard attacks like projected gradient descent. This casts doubt on a long-held assumption that stochastic defenses invalidate attacks designed to evade deterministic defenses and force attackers to integrate the Expectation over Transformation (EOT) concept. Second, we show that stochastic defenses confront a trade-off between adversarial robustness and model invariance; they become less effective as the defended model acquires more invariance to their randomization. Future work will need to decouple these two effects. We also discuss implications and guidance for future research.
The Importance of Subcellular Structures to the Modeling of Biological Cells in the Context of Computational Bioelectromagnetics Simulations

Numerical investigation of the interaction of electromagnetic fields with eukaryotic cells requires specifically adapted computer models. Virtual microdosimetry, used to investigate exposure, requires volumetric cell models, which are numerically challenging. For this reason, a method is presented here to determine the current and volumetric loss densities occurring in single cells and their distinct compartments in a spatially accurate manner as a first step toward multicellular models within the microstructure of tissue layers. To achieve this, 3D models of the electromagnetic exposure of generic eukaryotic cells of different shape (i.e. spherical and ellipsoidal) and internal complexity (i.e. different organelles) are performed in a virtual, finite element method‐based capacitor experiment in the frequency range from 10 Hz to 100 GHz. In this context, the spectral response of the current and loss distribution within the cell compartments is investigated and any effects that occur are attributed either to the dispersive material properties of these compartments or to the geometric characteristics of the cell model investigated in each case. In these investigations, the cell is represented as an anisotropic body with an internal distributed membrane system of low conductivity that mimics the endoplasmic reticulum in a simplified manner. This will be used to determine which details of the cell interior need to be modeled, how the electric field and the current density will be distributed in this region, and where the electromagnetic energy is absorbed in the microstructure regarding electromagnetic microdosimetry. Results show that for 5 G frequencies, membranes make a significant contribution to the absorption losses. © 2023 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.
An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation

This paper proposes a methodology to empirically validate an agent-based model (ABM) that generates artificial financial time series data comparable with realworld financial data. The approach is based on comparing the results of the ABM against the stylised facts – the statistical properties of the empirical timeseries of financial data. The stylised facts appear to be universal and are observed across different markets, financial instruments and time periods, hence they can serve to validate models of financial markets. If a given model does not consistently replicate these stylised facts, then we can reject it as being empirically inadequate. We discuss each stylised fact, the empirical evidence for it, and introduce appropriate metrics for testing the presence of these in model generated data. Moreover we investigate the ability of our model to correctly reproduce these stylised facts. We validate our model against a comprehensive list of empirical phenomena that qualify as a stylised fact, of both low and high frequency financial data that can be addressed by means of a relatively simple ABM of financial markets. This procedure is able to show whether the model, as an abstraction of reality, has a meaningful empirical counterpart and the significance of this analysis for the purposes of ABM validation and their empirical reliability.
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

The combination of Monte Carlo methods and deep learning has recently led to efﬁcient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical ex-planations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements.
Guiding visual attention in deep convolutional neural networks based on human eye movements

Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data. As recent advances in deep learning seem to decrease this similarity, computational neuroscience is challenged to reverse-engineer the biological plausibility to obtain useful models. While previous studies have shown that biologically inspired architectures are able to amplify the human-likeness of the models, in this study, we investigate a purely data-driven approach. We use human eye tracking data to directly modify training examples and thereby guide the models’ visual attention during object recognition in natural images either toward or away from the focus of human fixations. We compare and validate different manipulation types (i.e., standard, human-like, and non-human-like attention) through GradCAM saliency maps against human participant eye tracking data. Our results demonstrate that the proposed guided focus manipulation works as intended in the negative direction and non-human-like models focus on significantly dissimilar image parts compared to humans. The observed effects were highly category-specific, enhanced by animacy and face presence, developed only after feedforward processing was completed, and indicated a strong influence on face detection. With this approach, however, no significantly increased human-likeness was found. Possible applications of overt visual attention in DCNNs and further implications for theories of face detection are discussed.
D-CIPHER: Discovery of Closed-form PDEs

Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientists to model and better understand natural phenomena. Discovering these equations directly from data is challenging because it requires modeling relationships between various derivatives that are not observed in the data ( equation-data mismatch ) and it involves searching across a huge space of possible equations. Current approaches make strong assumptions about the form of the equation and thus fail to discover many well-known systems. Moreover, many of them resolve the equation-data mismatch by estimating the derivatives, which makes them inadequate for noisy and infrequently sampled systems. To this end, we propose D-CIPHER, which is robust to measurement artifacts and can uncover a new and very general class of differential equations. We further design a novel optimization procedure, CoLLie, to help D-CIPHER search through this class efﬁciently. Finally, we demonstrate empirically that it can discover many well-known equations that are beyond the capabilities of current methods.
Sensitivity Analysis of Reinforced Concrete Frame Structures Under Near-Fault Pulse-Like Ground Motions Using a Broadband Simulation Method

ABSTRACT The effect of pulse characteristic of near-fault ground motions on multistory reinforced concrete frame structures is investigated by means of a synthetic broadband ground motion model (SBGM). A simplified analytical pulse model is used to describe the low-frequency components of ground motions, while a stochastic simulation method based on wavelet packet is adopted to simulate the high-frequency components of ground motions. First, the time histories, response spectrum, and structural response of synthetic ground motions are compared with those corresponding to recorded near-fault ground motions. The good agreement indicates that the SBGM model used in this article is a suitable and efficient tool for evaluating the structural response. Sensitivity analysis on structural response of 2-, 4-, 8-, and 12-story building models to the SBGM model input parameters is then performed to identify the key ground-motion parameters which control the main structural response indicators, that is, interstory drift ratio and maximum interstory drift ratio. Final results indicate that the amplitude of velocity pulse in the SBGM model has a significant influence on structural drift demands. Nevertheless, for low-rise buildings, the maximum drift demands would effectively decrease with increasing proportion of high-frequency components. Moreover, the ratio of pulse period to structural fundamental period and number of oscillations impose a large drift demands to the structure under near-fault pulse-like ground motions.
Rethinking Unsupervised Neural Superpixel Segmentation

Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied. Essentially, such methods generate superpixels by convolutional neural network (CNN) employed on a single image, and such CNNs are trained without any labels or further information. Thus, such approach relies on the incorporation of priors, typically by designing an objective function that guides the solution towards a meaningful superpixel segmentation. In this paper we propose three key elements to improve the efficacy of such networks: (i) the similarity of the soft superpixelated image compared to the input image, (ii) the enhancement and consideration of object edges and boundaries and (iii) a modified architecture based on atrous convolution, which allow for a wider field of view, functioning as a multi-scale component in our network. By experimenting with the BSDS500 dataset, we find evidence to the significance of our proposal, both qualitatively and quantitatively.1
Mining S-PLUS for Metal-poor Stars in the Milky Way

This work presents the medium-resolution (R ∼ 1500) spectroscopic follow-up of 522 low-metallicity star candidates from the Southern Photometric Local Universe Survey (S-PLUS). The objects were selected from narrowband photometry, taking advantage of the metallicity-sensitive S-PLUS colors. The follow-up observations were conducted with the Blanco and Gemini South telescopes, using the COSMOS and GMOS spectrographs, respectively. The stellar atmospheric parameters (T eff, logg , and [Fe/H]), as well as carbon and α-element abundances, were calculated for the program stars in order to assess the efficacy of the color selection. Results show that 92−3+2% of the observed stars have [Fe/H] ≤ −1.0, 83−3+3% have [Fe/H] ≤ −2.0, and 15−3+3% have [Fe/H] ≤ −3.0, including two ultra metal-poor stars ([Fe/H] ≤ −4.0). The 80th percentile for the metallicity cumulative distribution function of the observed sample is [Fe/H] = −2.04. The sample also includes 68 carbon-enhanced metal-poor stars. Based on the calculated metallicities, further S-PLUS color cuts are proposed, which can increase the fractions of stars with [Fe/H] ≤ −1.0 and ≤ −2.0 to 98% and 88%, respectively. Such high success rates enable targeted high-resolution spectroscopic follow-up efforts, as well as provide selection criteria for fiber-fed multiplex spectroscopic surveys.
Neurotoxin: Durable Backdoors in Federated Learning

Due to their decentralized nature, federated learning (FL) systems have an inherent vulnerability during their training to adversarial backdoor attacks. In this type of attack, the goal of the attacker is to use poisoned updates to implant so-called backdoors into the learned model such that, at test time, the model's outputs can be fixed to a given target for certain inputs. (As a simple toy example, if a user types"people from New York"into a mobile keyboard app that uses a backdoored next word prediction model, then the model could autocomplete the sentence to"people from New York are rude"). Prior work has shown that backdoors can be inserted into FL models, but these backdoors are often not durable, i.e., they do not remain in the model after the attacker stops uploading poisoned updates. Thus, since training typically continues progressively in production FL systems, an inserted backdoor may not survive until deployment. Here, we propose Neurotoxin, a simple one-line modification to existing backdoor attacks that acts by attacking parameters that are changed less in magnitude during training. We conduct an exhaustive evaluation across ten natural language processing and computer vision tasks, and we find that we can double the durability of state of the art backdoors.
Spectra of Lindbladians on the inﬁnite line: From non-Hermitian to full evolution via tridiagonal Laurent matrices

We determine the spectra of single-particle translation-invariant Lindblad operators on the inﬁnite line. In the case where the Hamiltonian is given by the discrete Laplacian and the Lindblad operators are rank r , ﬁnite range and translates of each other, we obtain a representation of the Lindbladian as a direct integral of ﬁnite range bi-inﬁnite Laurent matrices with rank- r -perturbations. By analyzing the direct integral we rigorously determine the complete spectrum in the general case and calculate it explicitly for several types of dissipation e.g. dephasing, coherent hopping. We further use the detailed information about the spectrum to prove gaplessness, absence of residual spectrum and a condition for convergence of ﬁnite volume spectra to their inﬁnite volume counterparts. We ﬁnally extend the discussion to the case of the Anderson Hamiltonian, which enables us to study a Lindbladian recently associated to localization in open quantum systems.
A Temperature Trend for Clouds and Hazes in Exoplanet Atmospheres

The transmission spectra of exoplanet atmospheres observed with the Hubble Space Telescope (HST) in the near-infrared range (1.1–1.65 μm) frequently show evidence for some combination of clouds and hazes. Identification of systematic trends in exoplanet clouds and hazes is potentially important for understanding atmospheric composition and temperature structure. Here we report on the analysis of spectral modulation using a large, uniformly processed sample of HST/WFC3 transit spectra from 62 exoplanets. The spectral retrieval includes the capability to detect and represent atmospheres in which the composition departs from thermochemical equilibrium. By using this unique catalog and measuring the dampening of spectral modulations compared to strictly clear atmospheres, we identify two populations. One is completely cloud/haze free spanning a wide temperature range, while the other population, identified as “partial cloud/hazes,” follows a trend from mostly cloudy/hazy around 500 K to mostly clear at ∼1500 K. We also find that a partially transparent aerosol component is frequently present and that it is typically vertically distributed throughout the atmospheric column. Our findings also suggest that while clouds and hazes are common in exoplanet atmospheres, the majority of planets have some level of detectable spectral modulation. Additionally, the empirical trend that clouds and hazes are minimized at 1460.86 K −405+316 revealed in our catalog has predictive utility for modeling the performance of large-scale transiting exoplanets survey, such as planned with the Ariel mission. This trend can also be used for making a probability-based forecast of spectral modulation for a given source in the context of future JWST observations. Future observations including the optical and/or a broader spectral coverage may be useful to further quantify the trend reported here.
A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement

Preserving a patient's identity is a challenge for automatic, speech-based diagnosis of mental health disorders. In this paper, we address this issue by proposing adversarial disentanglement of depression characteristics and speaker identity. The model used for depression classification is trained in a speaker-identity-invariant manner by minimizing depression prediction loss and maximizing speaker prediction loss during training. The effectiveness of the proposed method is demonstrated on two datasets - DAIC-WOZ (English) and CONVERGE (Mandarin), with three feature sets (Mel-spectrograms, raw-audio signals, and the last-hidden-state of Wav2vec2.0), using a modified DepAudioNet model. With adversarial training, depression classification improves for every feature when compared to the baseline. Wav2vec2.0 features with adversarial learning resulted in the best performance (F1-score of 69.2% for DAIC-WOZ and 91.5% for CONVERGE). Analysis of the class-separability measure (J-ratio) of the hidden states of the DepAudioNet model shows that when adversarial learning is applied, the backend model loses some speaker-discriminability while it improves depression-discriminability. These results indicate that there are some components of speaker identity that may not be useful for depression detection and minimizing their effects provides a more accurate diagnosis of the underlying disorder and can safeguard a speaker's identity.
Closed-form Error Propagation on the SE_n(3) Group for Invariant Extended Kalman Filtering with Applications to VINS

Pose estimation is important for robotic perception, path planning, etc. Robot poses can be modeled on matrix Lie groups and are usually estimated via filter-based methods. In this paper, we establish the closed-form formula for the error propagation for the Invariant extended Kalman filter (IEKF) in the presence of random noises and apply it to vision-aided inertial navigation. We evaluate our algorithm via numerical simulations and experiments on the OPENVINS platform. Both simulations and the experiments performed on the public EuRoC MAV datasets demonstrate that our algorithm outperforms some state-of-art filter-based methods such as the quaternion-based EKF, first estimates Jacobian EKF, etc.
PII: S1010-7940(99)00097-4

Objective: Coronary artery disease is the main cause of mortality and morbidity in patients on renal therapy replacement. The aim of this study was to de®ne peri-operative risk and long term results of coronary artery bypass grafts (CABG) in dialysis patients. Methods: this retrospective study included 82 patients in chronic dialysis who underwent CABG between 1978 and 1997. The mean age was 61 ^ 10 years (range 28±81 years), 84% of the patients were male and the average duration of dialysis was 57 months (range 1±148 months). Combined procedures were carotid endarterectomy in one case, left ventricular aneurysm resection in one and valvular replacement in 10 (nine aortic and one mitral replacements). The operation was elective in 42 patients (51%) and urgent in the others. Previous myocardial infarction was found in 37 patients (45%) and left ventricular ejection fraction (LVEF) at less than 45% in 15 patients (18%); 23 patients (28%) were in NYHA class III or IV and regarding angina functional status, 77% in CCS class 3 or 4. Follow-up was complete. Statistical analysis included 30 and pre and peri-operative data. Statistical analysis used Chi-square analysis or Fisher's exact test, and the Mann±Whitney test when appropriate. The estimated probability of survival, including postoperative mortality, was calculated by the method of Kaplan±Meyer, and the Log-Rank test used to compare the results. Results: the hospital mortality was 14.6 % (n  12). Ischemic time and ECC time were signi®cantly lengthened in dead patients (P  0:01). Moreover, use of internal mammary artery was directly related to lower hospital mortality (P  0:02). For previous myocardial infarction, LVEF at less than 45%, diabetes and combined procedure, a P-value of # 0.1 was calculated. The follow-up ranged from 1 to 140 months (mean 36 months). There were 39 late deaths. The survival rates (included hospital mortality) were 71^5%, 56^6% and 39^6% at 1, 3 and 5 years, respectively. All surviving patients improved their functional status and had symptomatic relief. Statistical analysis showed signi®cant difference in favor of long term survival for patients younger than 60 years, LVEF . 45% and NYHA class I or II. Conclusion: these data con®rm that CABG in patients with renal replacement therapy is associated with an high operative and long term mortality. However it allows an improvement of functional status, and so, let possible duration of dialysis. It may be expected that more active prevention and detection of coronary disease might improve these results. q 1999 Elsevier Science B.V. All rights reserved.