Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss-based Objective and Penalizing Priors for Model Selection Problems

Many Bayesian model selection problems, such as variable selection or cluster analysis, start by setting prior model probabilities on a structured model space. Based on a chosen loss function between models, model selection is often performed with a Bayes estimator that minimizes the posterior expected loss. The prior model probabilities and the choice of loss both highly affect the model selection results, especially for data with small sample sizes, and their proper calibration and careful reflection of no prior model preference are crucial in objective Bayesian analysis. We propose risk equilibrium priors as an objective choice for prior model probabilities that only depend on the model space and the choice of loss. Under the risk equilibrium priors, the Bayes action becomes indifferent before observing data, and the family of the risk equilibrium priors includes existing popular objective priors in Bayesian variable selection problems. We generalize the result to the elicitation of objective priors for Bayesian cluster analysis with Binder's loss. We also propose risk penalization priors, where the Bayes action chooses the simplest model before seeing data. The concept of risk equilibrium and penalization priors allows us to interpret prior properties in light of the effect of loss functions, and also provides new insight into the sensitivity of Bayes estimators under the same prior but different loss. We illustrate the proposed concepts with variable selection simulation studies and cluster analysis on a galaxy dataset.
Ecosystem transformations in response to environmental fluctuations

Ecosystems, which are intricate amalgams of biological communities and their surrounding environments, continually evolve under the influence of their myriad interactions. The world is currently facing intensifying environmental fluctuations. Understanding general trends in ecosystem transformations in response to environmental fluctuations and elucidating the underlying mechanisms are thus critical challenges. In this study, we used a model ecosystem approach to investigate ecosystem alterations caused by escalating environmental fluctuations. We analyzed two distinct models: a stochastic ecosystem model with a spatial structure, and a differential equation model for resource competition. We found that environmental fluctuations tend to shift multi-species coexistence toward the dominance of specific species. We also categorized biological species as specialists or generalists and discovered that which of these groups becomes the dominant species depends on the intensity and frequency of environmental fluctuations. We also determined that a qualitative change in the diversity-stability relationship depends on the period of environmental fluctuations. These results underscore the need to explicitly consider the type of perturbation when discussing ecological transitions and the stability of ecosystems. Our findings advance understanding of the mechanisms underlying how environmental changes reshape ecosystems and offer insights into ecosystem sustainability in the face of future environmental perturbations.
Transfer Attacks and Defenses for Large Language Models on Coding Tasks

Modern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks including writing and reasoning about code. They improve upon previous neural network models of code, such as code2seq or seq2seq, that already demonstrated competitive results when performing tasks such as code summarization and identifying code vulnerabilities. However, these previous code models were shown vulnerable to adversarial examples, i.e. small syntactic perturbations that do not change the program's semantics, such as the inclusion of"dead code"through false conditions or the addition of inconsequential print statements, designed to"fool"the models. LLMs can also be vulnerable to the same adversarial perturbations but a detailed study on this concern has been lacking so far. In this paper we aim to investigate the effect of adversarial perturbations on coding tasks with LLMs. In particular, we study the transferability of adversarial examples, generated through white-box attacks on smaller code models, to LLMs. Furthermore, to make the LLMs more robust against such adversaries without incurring the cost of retraining, we propose prompt-based defenses that involve modifying the prompt to include additional information such as examples of adversarially perturbed code and explicit instructions for reversing adversarial perturbations. Our experiments show that adversarial examples obtained with a smaller code model are indeed transferable, weakening the LLMs' performance. The proposed defenses show promise in improving the model's resilience, paving the way to more robust defensive solutions for LLMs in code-related applications.
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization

Parameter-efficient fine-tuning (PEFT) techniques make it possible to efficiently adapt a language model to create"expert"models that specialize to new tasks or domains. Recent techniques in model merging and compositional generalization leverage these expert models by dynamically composing modules to improve zero/few-shot generalization. Despite the efficiency of PEFT methods, the size of expert models can make it onerous to retrieve expert models per query over high-latency networks like the Internet or serve multiple experts on a single GPU. To address these issues, we present ComPEFT, a novel method for compressing fine-tuning residuals (task vectors) of PEFT based models. ComPEFT employs sparsification and ternary quantization to reduce the size of the PEFT module without performing any additional retraining while preserving or enhancing model performance. In extensive evaluation across T5, T0, and LLaMA-based models with 200M - 65B parameters, ComPEFT achieves compression ratios of 8x - 50x. In particular, we show that ComPEFT improves with scale - stronger models exhibit higher compressibility and better performance. For example, we show that ComPEFT applied to LLaMA outperforms QLoRA by 4.16% on MMLU with a storage size reduction of up to 26x. In addition, we show that the compressed experts produced by ComPEFT maintain few-shot compositional generalization capabilities, facilitate efficient communication and computation, and exhibit enhanced performance when merged. Lastly, we provide an analysis of different method components, compare it with other PEFT methods, and test ComPEFT's efficacy for compressing the residual of full-finetuning. Our code is available at https://github.com/prateeky2806/compeft.
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer

The density of states (DOS) is a spectral property of crystalline materials, which provides fundamental insights into various characteristics of the materials. While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy. That is, DOS is not solely determined by the crystalline material but also by the energy levels, which has been neglected in previous works. In this paper, we propose to integrate heterogeneous information obtained from the crystalline materials and the energies via a multi-modal transformer, thereby modeling the complex relationships between the atoms in the crystalline materials and various energy levels for DOS prediction. Moreover, we propose to utilize prompts to guide the model to learn the crystal structural system-specific interactions between crystalline materials and energies. Extensive experiments on two types of DOS, i.e., Phonon DOS and Electron DOS, with various real-world scenarios demonstrate the superiority of DOSTransformer. The source code for DOSTransformer is available at https://github.com/HeewoongNoh/DOSTransformer.
Meticulously Selecting 1% of the Dataset for Pre-training! Generating Differentially Private Images Data with Semantics Query

Differential Privacy (DP) image data synthesis, which leverages the DP technique to generate synthetic data to replace the sensitive data, allowing organizations to share and utilize synthetic images without privacy concerns. Previous methods incorporate the advanced techniques of generative models and pre-training on a public dataset to produce exceptional DP image data, but suffer from problems of unstable training and massive computational resource demands. This paper proposes a novel DP image synthesis method, termed PRIVIMAGE, which meticulously selects pre-training data, promoting the efficient creation of DP datasets with high fidelity and utility. PRIVIMAGE first establishes a semantic query function using a public dataset. Then, this function assists in querying the semantic distribution of the sensitive dataset, facilitating the selection of data from the public dataset with analogous semantics for pre-training. Finally, we pre-train an image generative model using the selected data and then fine-tune this model on the sensitive dataset using Differentially Private Stochastic Gradient Descent (DP-SGD). PRIVIMAGE allows us to train a lightly parameterized generative model, reducing the noise in the gradient during DP-SGD training and enhancing training stability. Extensive experiments demonstrate that PRIVIMAGE uses only 1% of the public dataset for pre-training and 7.6% of the parameters in the generative model compared to the state-of-the-art method, whereas achieves superior synthetic performance and conserves more computational resources. On average, PRIVIMAGE achieves 30.1% lower FID and 12.6% higher Classification Accuracy than the state-of-the-art method. The replication package and datasets can be accessed online.
Towards a general description of the cavitation threshold in acoustic systems

Traditionally, the occurrence of cavitation has been related to the ratio between flow velocity and pressure gradient in the case of hydrodynamic cavitation, or some combination of vapor pressure and surface tension. However, both formulations present a large discrepancy with experimental data for cases in which cavitation is induced by acoustic waves. The present study aims to identify a more suitable cavitation threshold for such cases. The methodology adopted in this work consists of a combination of visualization with high-speed cameras and direct measurements using a hydrophone. The data collected confirmed that the vapor pressure is not a proper indicator of cavitation occurrence for an acoustic system characterized by high frequencies. The main reason behind the inability of vapor pressure to predict incipient cavitation in acoustic systems is that they evolve very quickly toward strong gradients in pressure, and the quasi-static assumptions used by traditional models are not valid. Instead, the system evolves towards a metastable state [Brennen, 2013], where the liquid exhibits an elastic behavior and can withstand negative pressures. A new cavitation number accounting for the tensile strength of the liquid was defined. An acoustic analogy is also proposed for the description, with the same framework, of an impulsive cavitation phenomenon.
Shock Excitation in Narrow-line Regions Powered by AGN Outflows

Outflows in an active galactic nucleus (AGN) are considered to play a key role in the evolution of the host galaxy through transfer of a large amount of energy. A narrow-line region (NLR) in the AGN is composed of ionized gas extending from parsec to kiloparsec scales. It has been suggested that shocks are required to ionize the NLR gas. If AGN outflows generate these shocks, they will sweep through the NLR, and the outflow energy will be transferred into a galaxy-scale region. In order to study the contribution of the AGN outflow to the NLR-scale shock, we measure the [Fe ii]λ12570/[P ii]λ11886 line ratio, which is a good tracer of shocks, using near-infrared spectroscopic observations with the Warm INfrared Echelle spectrograph to Realize Extreme Dispersion and sensitivity (WINERED) mounted on the New Technology Telescope. In 13 Seyfert galaxies we observed, the [Fe ii] and [P ii] lines were detected in 12 and 6 targets, respectively. The [Fe ii]/[P ii] ratios in 4 targets were found to be higher than 10, which implies the existence of shocks. We also found that the shock is likely to exist where an ionized outflow, i.e., a blue wing in [S iii]λ9533, is present. Our result implies that the ionized outflow present in an NLR-scale region sweeps through the interstellar medium and generates a shock.
Superconductivity and Charge-density-wave-like Transition in Th2Cu4As5

We report the synthesis, crystal structure, and physical properties of a novel ternary compound, Th$_2$Cu$_4$As$_5$. The material crystallizes in a tetragonal structure with lattice parameters $a=4.0716(1)$ {\AA} and $c=24.8131(4)$ {\AA}. Its structure can be described as an alternating stacking of fluorite-type Th$_2$As$_2$ layers with antifluorite-type double-layered Cu$_4$As$_3$ slabs. The measurement of electrical resistivity, magnetic susceptibility and specific heat reveals that Th$_2$Cu$_4$As$_5$ undergoes bulk superconducting transition at 4.2 K. Moreover, all these physical quantities exhibit anomalies at 48 K, where the Hall coefficient change the sign. These findings suggest a charge-density-wave-like (CDW) transition, making Th$_2$Cu$_4$As$_5$ a rare example for studying the interplay between CDW and superconductivity.
Heavy Metals Pollution of Water and Sediment in Mada River, Nigeria

Sediment acts as a sink for heavy metals, which may be remobilized into the water column. The research was carried out to assess seasonal distribution and impact of heavy metals (Zn, Mn, Ni, Fe, Cu, Cr, Cd) concentrations in sediment on the overlying water. Water and bed sediment samples were collected from Mada River during rainy and dry seasons and analyzed for the heavy metal concentrations, using atomic absorption spectrophotometer (AAS). Metal levels in water were higher during the dry season, except for the decrease in Fe (3.79±0.94 mg/L) and Mn (0.20±0.12 mg/L) concentrations. Metal levels in sediment decreased during the dry season, however, Zn (0.25±0.02 mg/kg) and Ni (0.025±0.02 mg/kg) showed an increase in concentrations. Metal levels were higher in water and sediment during dry and rainy seasons respectively. Sediment contained higher metal concentrations compared to water. Partition coefficients (Kd) values during the dry season were less than 1 except for Mn (1.35), but greater than 1 during rainy season, excluding Zn (0.39) and Ni (0.77). Correlations between metal concentrations in sediment and water were positively strong for Cu (0.7825) and Mn (0.9428). Cr, Fe and Mn levels in water and sediment were significantly different (P = .05) for both seasons. Remobilization of metals from sediment surfaces onto overlying water might account for the low Kd values and higher metal levels in water during the dry season. Original Research Article Tukura; JSRR, 6(2): 157-164, 2015; Article no.JSRR. 2015.140 158
Volumetric 3d Point Cloud Attribute Compression: Learned Polynomial Bilateral Filter for Prediction

We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \mapsto \mathbb{R}$, we quantize and encode parameters $\theta$ that characterize $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf(x))$ at known 3D points $\mathbf(x)$ at the decoder. Specifically, parameters $\theta$ are quantized coefficients of B-spline basis vectors $\mathbf{\Phi}_l$ (for order $p \geq 2$) that span the function space $\mathcal{F}_l^{(p)}$ at a particular resolution $l$, which are coded from coarse to fine resolutions for scalability. In this work, we focus on the prediction of finer-grained coefficients given coarser-grained ones by learning parameters of a polynomial bilateral filter (PBF) from data. PBF is a pseudo-linear filter that is signal-dependent with a graph spectral interpretation common in the graph signal processing (GSP) field. We demonstrate PBF's predictive performance over a linear predictor inspired by MPEG standardization over a wide range of point cloud datasets.
Waves in space-dependent and time-dependent materials: a systematic comparison

Waves in space-dependent and time-dependent materials obey similar wave equations, with interchanged time- and space-coordinates. However, since the causality conditions are the same in both types of material (i.e., without interchangement of coordinates), the solutions are dissimilar. We present a systematic treatment of wave propagation and scattering in 1D space-dependent and time-dependent materials. After a review of reflection and transmission coefficients, we discuss Green's functions and simple wave field representations for both types of material. Next we discuss propagation invariants, i.e., quantities that are independent of the space coordinate in a space-dependent material (such as the net power-flux density) or of the time coordinate in a time-dependent material (such as the net field-momentum density). A discussion of reciprocity theorems leads to the well-known source-receiver reciprocity relation for the Green's function of a space-dependent material and a new source-receiver reciprocity relation for the Green's function of a time-dependent material. A discussion of general wave field representations leads to the well-known expression for Green's function retrieval from the correlation of passive measurements in a space-dependent material and a new expression for Green's function retrieval in a time-dependent material. After an introduction of a matrix-vector wave equation, we discuss propagator matrices for both types of material. Since the initial condition for a propagator matrix in a time-dependent material follows from the boundary condition for a propagator matrix in a space-dependent material by interchanging the time- and space-coordinates, the propagator matrices for both types of material are interrelated in the same way. This also applies to representations and reciprocity theorems involving propagator matrices.
Fuzzy Dark Matter Dynamics and the Quasiparticle Hypothesis

Dark matter may be composed of ultra-light bosons whose de Broglie wavelength in galaxies is λ ∼ 1 kpc. The standard model for this fuzzy dark matter (FDM) is a complex scalar field that obeys the Schrödinger-Poisson equations. The wavelike nature of FDM leads to fluctuations in the gravitational field that can pump energy into the stellar components of a galaxy. Heuristic arguments and theoretical analyses suggest that these fluctuations can be modelled by replacing FDM with a system of quasiparticles (QPs). We test this hypothesis by comparing self-consistent simulations of a Schrödinger field with those using a system of QPs in one spatial dimension. Simulations of pure FDM systems allow us to derive a phenomenological relation between the number of QPs that is required to model FDM with a given de Broglie wavelength. We also simulate systems of FDM and stars and find that the FDM pumps energy into the stars whether it is described by QPs or a Schrödinger field with the FDM adiabatically contracting and the stellar system adiabatically expanding. However, we find that QPs overestimate dynamical heating.
Asthma Polymorphic Loci genotyping in Madeira population: identification of potential genetic markers of disease susceptibility, severity and clinical relevance

The IL4 gene is located on chromosome 5q23.3-31.2. Polymorphisms within this cytokine gene, like the derivative allele T of IL4-590, have been reported as being associated to elevated IgE serum levels and asthma. In the present work, the allelic and genotypic frequency of the IL4-590 and IL4 RP2 polymorphisms was carried out in 599 individuals from Madeira, Azores, Portugal mainland, Cape Verde and Guinea-Bissau and in a sample of 101 asthmatics from Madeira population. In all populations the polymorphisms were in LD and presented a significant dissimilar allelic and genotypic distribution (p<0.05) except between mainland Portugal and Madeira when compared to Azores. Significant differences regarding both loci were found between Madeira population and the group of asthmatics. Genotype 183183TT frequency is higher for African populations while 253253CC prevails in Caucasian populations. The existence of a Hardy-Weinberg Disequilibrium in Guinea-Bissau population not observed in neutral markers leads to the hypothesis of natural selection occurring in these loci probably associated to a rapid population growth an hypothesis strengthened by neutral STRs D5S818 and CSF1PO gene diversity.
Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model

Feedforward generalizable models for implicit shape reconstruction from unoriented point cloud present multiple advantages, including high performance and inference speed. However, they still suffer from generalization issues, ranging from underfitting the input point cloud, to misrepresenting samples outside of the training data distribution, or with toplogies unseen at training. We propose here an efficient mechanism to remedy some of these limitations at test time. We combine the inter-shape data prior of the network with an intra-shape regularization prior of a Nystr\"om Kernel Ridge Regression, that we further adapt by fitting its hyperprameters to the current shape. The resulting shape function defined in a shape specific Reproducing Kernel Hilbert Space benefits from desirable stability and efficiency properties and grants a shape adaptive expressiveness-robustness trade-off. We demonstrate the improvement obtained through our method with respect to baselines and the state-of-the-art using synthetic and real data.
Driven-dissipative Bose-Einstein condensation and the upper critical dimension

Driving and dissipation can stabilize Bose-Einstein condensates. Using Keldysh field theory, we analyze this phenomenon for Markovian systems that can comprise on-site two-particle driving, on-site single-particle and two-particle loss, as well as edge-correlated pumping. Above the upper critical dimension, mean-field theory shows that pumping and two-particle driving induce condensation right at the boundary between the stable and unstable regions of the non-interacting theory. With nonzero two-particle driving, the condensate is gapped. This picture is consistent with the recent observation that, without symmetry constraints beyond invariance under single-particle basis transformations, all gapped quadratic bosonic Liouvillians belong to the same phase. For systems below the upper critical dimension, the edge-correlated pumping penalizes high-momentum fluctuations, rendering the theory renormalizable. We perform the one-loop renormalization group analysis, finding a condensation transition inside the unstable region of the non-interacting theory. Interestingly, its critical behavior is determined by a Wilson-Fisher-like fixed point with universal correlation-length exponent $\nu=0.6$ in three dimensions.
Critical edge statistics for deformed GinUEs

For an additive perturbation of the complex Ginibre ensemble under a deterministic matrix $X_0$, under certain assumption on $X_0$ we prove that there are only two types of local statistical patterns at the spectral edge--GinUE statistics and critical statistics, which corresponds to regular or quadratic vanishing spectral points. The latter, as a non-Hermitian analogue of Pearcey statistics in random matrix theory, characterizes a new point process on the complex plane and will be our major goal in this paper.