Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the effect of plasmochemically activated aqueous solutions on the bioactivation process of sea buckthorn seeds

The studies revealed the regularities of obtaining bioactivated sea buckthorn seeds using plasma-chemically activated aqueous solutions during germination. Sea buckthorn seeds were chosen as the research object. Plasma-chemically activated aqueous solutions were used to activate the germination process. This made it possible to solve the problem of processing waste from the production of sea buckthorn oil, and also contributed to obtaining a high-quality component of food products. Experimental studies have proven the effectiveness of using plasma-chemically activated aqueous solutions as effective intensifiers and disinfectants for the process of bioactivation of sea buckthorn seeds. It is shown that their use intensifies the germination of sea buckthorn seeds, contributes to a more active accumulation of biologically valuable components in the seeds. Plasma-chemically activated aqueous solutions with a peroxide concentration of 300–700 mg/l were used. An increase in the geometric parameters of seeds, namely length by 8.5–14.9 % and width by 3.7–14.8 %, was recorded. The germination energy increased by 5–13 % and germination capacity by 5–14 %. The composition of sea buckthorn seeds, both derived raw material and bioactivated, was investigated. Studies have shown that bioactivated seeds contain an increased amount of highly valuable substances. The protein content increased by 4 % compared to sea buckthorn seeds and by 1.7 % compared to the control. The lipid content increased by 2 and 1.1 %. An increase in the content of vitamins was noted: B1, B2, C, A, E, R. The amount of amino acids increased by 9–13 % compared to the control, and compared to the original raw material – by 1.5–3.5 times. In addition, plasma-chemically activated aqueous solutions effectively disinfected the raw material. The presented technology can be used in the food processing industry
Functional, antioxidant, and sensory properties of mixed‐fruit (pitaya, watermelon, and mint) and pitaya wines

Abstract Fermentation of fruits offers a diverse range of flavors, smells, and colors. Colored fruits are rich in naturally occurring pigments, such as betacyanin. Hence, they are considered to possess powerful antioxidant activities. However, in wine production, such pigments often diversify the flavor and color of the wine. The objective of this study was to compare the quality of two types of wines: a single‐fruit (pitaya) wine and a mixed‐fruit wine that contains watermelon (Citrullus lanatus), mint (Mintha spicata), and pitaya (Hylocereus costaricensis). In this study, fresh pitaya, watermelon, and mint leaves were fermented using Saccharomyces cerevisiae. Juice extracts underwent fermentation at room temperature for 7 days under dark conditions. Physicochemical changes, such as pH, sugar content, specific gravity, and alcohol content, were observed daily. The antioxidant activities were measured by the 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) assay, the ferric reducing antioxidant power (FRAP) assay, and total phenolic contents (TPCs). After 14 days of fermentation, the alcohol contents of mixed and pitaya wine were 11.22% (v/v) and 11.25%, respectively. The total sugar content of the mixed wine was 8.0 °Brix, while that of pitaya wine was 7.0 °Brix. Moreover, pitaya wine exhibited a higher TPC (22.7 mg GAE/100 g D.W.), and better FRAP (3578 μmole/L) and DPPH scavenging ability (80.2%) compared to the mixed wine with a TPC of 21.4 mg GAE/100 g D.W. , FRAP of 2528 μmole/L, and DPPH of 75.6%. , while the addition of watermelon and mint did not change the alcohol percentage contents of wine.
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks

Transducer and Attention based Encoder-Decoder (AED) are two widely used frameworks for speech-to-text tasks. They are designed for different purposes and each has its own benefits and drawbacks for speech-to-text tasks. In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks. The new method leverages AED’s strength in non-monotonic sequence to sequence learning while retaining Transducer’s streaming property. In the proposed framework, Transducer and AED share the same speech encoder. The predictor in Transducer is replaced by the decoder in the AED model, and the outputs of the decoder are conditioned on the speech inputs instead of outputs from an unconditioned language model. The proposed solution ensures that the model is optimized by covering all possible read/write scenarios and creates a matched environment for streaming applications. We evaluate the proposed approach on the MuST-C dataset and the findings demonstrate that TAED performs significantly better than Transducer for offline automatic speech recognition (ASR) and speech-to-text translation (ST) tasks. In the streaming case, TAED outperforms Transducer in the ASR task and one ST direction while comparable results are achieved in another translation direction.
Retraining A Graph-based Recommender with Interests Disentanglement

In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender needs to be periodically retrained or updated to capture the new trends, and yet not to forget the long-term preferences. In this paper, we propose a novel and generic retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommenders. We assume that long-term preferences are well captured in the existing model, in the form of model parameters learned from past interactions. New preferences can be learned from the user-item bipartite graph constructed using the newly observed interactions. In DIL, we design an Information Extraction Module to extract historical preferences from the existing model. Then we blend the historical and new preferences in the form of node embeddings in the new graph, through a Disentanglement Module. The essence of the disentanglement module is to decorrelate the historical and new preferences so that both can be well captured, via carefully designed losses. Through experiments on three benchmark datasets, we show the effectiveness of DIL in capturing dynamics of useritem interactions. We also demonstrate the robustness of DIL by attaching it to two base models - LightGCN and NGCF.
Replica-mean-field limit of continuous-time fragmentation-interaction-aggregation processes

Many phenomena can be modeled as network dynamics with punctuate interactions. However, most relevant dynamics do not allow for computational tractability. To circumvent this difficulty, the Poisson Hypothesis regime replaces interaction times between nodes by independent Poisson processes, allowing for tractability in several cases, such as intensity-based models from computational neuroscience. This hypothesis is usually only conjectured, or numerically validated. In this work, we introduce a class of processes in continuous time called continuous fragmentation-interaction-aggregation processes, by analogy with previously introduced processes in discrete time. The state of each node, described by the stochastic intensity of an associated point process, aggregates arrivals from its neighbors and is fragmented upon departure. We consider the replica-mean-field version of such a process, which is a physical system consisting of randomly interacting copies of the network of interest. Generalizing results proved in discrete time and in the particular case of excitatory intensity-based neural dynamics, we prove that the Poisson Hypothesis holds at the limit of an infinite number of replicas.
Semantic Segmentation using Vision Transformers: A survey

Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. Convolutional neural networks (CNN) and Vision Transformers (ViTs) provide the architecture models for semantic segmentation. Even though ViTs have proven success in image classification, they cannot be directly applied to dense prediction tasks such as image segmentation and object detection since ViT is not a general purpose backbone due to its patch partitioning scheme. In this survey, we discuss some of the different ViT architectures that can be used for semantic segmentation and how their evolution managed the above-stated challenge. The rise of ViT and its performance with a high success rate motivated the community to slowly replace the traditional convolutional neural networks in various computer vision tasks. This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets. This will be worthwhile for the community to yield knowledge regarding the implementations carried out in semantic segmentation and to discover more efficient methodologies using ViTs.
Confidence-Based Skill Reproduction Through Perturbation Analysis

Several methods exist for teaching robots, with one of the most prominent being Learning from Demonstration (LfD). Many LfD representations can be formulated as constrained optimization problems. We propose a novel convex formulation of the LfD problem represented as elastic maps, which models reproductions as a series of connected springs. Relying on the properties of strong duality and perturbation analysis of the constrained optimization problem, we create a confidence metric. Our method allows the demonstrated skill to be reproduced with varying confidence level yielding different levels of smoothness and flexibility. Our confidence-based method provides reproductions of the skill that perform better for a given set of constraints. By analyzing the constraints, our method can also remove unnecessary constraints. We validate our approach using several simulated and real-world experiments using a Jaco2 7DOF manipulator arm.
VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation

We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an intermediate video-text pre-training stage to learn video-specific skills such as spatio-temporal reasoning. As a result, our VideoOFA model achieves new state-of-the-art performance on four Video Captioning benchmarks, beating prior art by an average of 9.7 points in CIDEr score. It also outperforms existing models on two open-ended Video Question Answering datasets, showcasing its generalization capability as a universal video-to-text model.
On the analysis of Rayleigh-B\'enard convection using Latent Dirichlet Allocation

We apply a probabilistic clustering method, Latent Dirichlet Allocation (LDA), to characterize the largescale dynamics of Rayleigh-B\'enard convection. The method, introduced in Frihat et al. 2021, is applied to a collection of snapshots in the vertical mid-planes of a cubic cell for Rayleigh numbers in the range [106, 108]. For the convective heat flux, temperature and kinetic energy, the decomposition identifies latent factors, called motifs, which consist of connex regions of fluid. Each snapshot is modelled with a sparse combination of motifs, the coefficients of which are called the weights. The spatial extent of the motifs varies across the cell and with the Rayleigh number. We show that the method is able to provide a compact representation of the heat flux and displays good generative properties. At all Rayleigh numbers the dominant heat flux motifs consist of elongated structures located mostly within the vertical boundary layer, at a quarter of the cavity height. Their weights depend on the orientation of the large-scale circulation (LSC). A simple model relating the conditionally averaged weight of the motifs to the relative strength of the corner rolls and of the large-scale circulation, is found to predict well the average LSC reorientation rate. Application of LDA to the temperature fluctuations shows that temperature motifs are well correlated with heat flux motifs in space as well as in time, and to some lesser extent with kinetic energy motifs. The abrupt decrease of the reorientation rate observed at 108 is associated with a strong concentration of plumes impinging layers onto the corners of the cell, which decrease the temperature difference within the corner structures. It is also associated with a reinforcement of the longitudinal wind through formation and entrainment of new plumes.
Stylized Data-to-text Generation: A Case Study in the E-Commerce Domain

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute–value pairs, but overlook that different application scenarios may require texts of different styles. Inspired by this, we define a new task, namely stylized data-to-text generation, whose aim is to generate coherent text for the given non-linguistic data according to a specific style. This task is non-trivial, due to three challenges: the logic of the generated text, unstructured style reference and biased training samples. To address these challenges, we propose a novel stylized data-to-text generation model, named StyleD2T, comprising three components: logic planning-enhanced data embedding, mask-based style embedding, and unbiased stylized text generation. In the first component, we introduce a graph-guided logic planner for attribute organization to ensure the logic of generated text. In the second component, we devise feature-level mask-based style embedding to extract the essential style signal from the given unstructured style reference. In the last one, pseudo triplet augmentation is utilized to achieve unbiased text generation, and a multi-condition based confidence assignment function is designed to ensure the quality of pseudo samples. Extensive experiments on a newly collected dataset from Taobao1 have been conducted, and the results show the superiority of our model over existing methods.
Understanding the Salt Effects on the Liquid-Liquid Phase Separation of Proteins

Protein aggregation via liquid-liquid phase separation (LLPS) is ubiquitous in nature and intimately connects to many human diseases. Although it is widely known that the addition of salt has crucial impacts on the LLPS of protein, full understanding of the salt effect remains an outstanding challenge. Here, we develop a molecular theory which systematically incorporates the self-consistent field theory for charged macromolecules into the solution thermodynamics. The electrostatic interaction, hydrophobicity, ion solvation and translational entropy are included in a unified framework. Our theory fully captures the long-standing puzzles of the non-monotonic salt concentration dependence and the specific ion effect. We find that proteins show salting-out at low salt concentrations due to ionic screening. The solubility follows the inverse Hofmeister series. In the high salt concentration regime, protein remains salting-out for small ions but turns to salting-in for larger ions, accompanied by the reversal of the Hofmeister series. We reveal that the solubility at high salt concentrations is determined by the competition between the solvation energy and translational entropy of ion. Furthermore, we derive an analytical criterion for determining the boundary between the salting-in and salting-out regimes. The theoretical prediction is in quantitative agreement with experimental results for various proteins and salt ions without any fitting parameters.
Learning Decision Trees with Gradient Descent

Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difﬁcult optimization problem, as it is non-convex and non-differentiable. Therefore, common approaches learn DTs using a greedy growth algorithm that minimizes the impurity locally at each internal node. Unfortunately, this greedy procedure can lead to suboptimal trees. In this paper, we present a novel approach for learning hard, axis-aligned DTs with gradient descent. The proposed method uses backpropagation with a straight-through operator on a dense DT representation to jointly optimize all tree parameters. Our approach outperforms existing methods on binary classiﬁcation benchmarks and achieves competitive results for multi-class tasks.
Flock: Accurate Network Fault Localization at Scale

Inferring the root cause of failures among thousands of components in a data center network is challenging, especially for "gray" failures that are not reported directly by switches. Faults can be localized through end-to-end measurements, but past localization schemes are either too slow for large-scale networks or sacrifice accuracy. We describe Flock, a network fault localization algorithm and system that achieves both high accuracy and speed at datacenter scale. Flock uses a probabilistic graphical model (PGM) to achieve high accuracy, coupled with new techniques to dramatically accelerate inference in discrete-valued Bayesian PGMs. Large-scale simulations and experiments in a hardware testbed show Flock speeds up inference by >10000x compared to past PGM methods, and improves accuracy over the best previous datacenter fault localization approaches, reducing inference error by 1.19-11x on the same input telemetry, and by 1.2-55x after incorporating passive telemetry. We also prove Flock's inference is optimal in restricted settings.
Ground-Based FSSP and PVM Measurements of Liquid Water Content

Abstract Recently published ground-based measurements of liquid water content (LWC) measured in fogs by two microphysical instruments, the FSSP-100 and PVM-100, are evaluated. These publications had suggested that the PVM-100 underestimated LWC significantly in comparison to the FSSP-100 when the fog droplets were large. The present evaluation suggests just the opposite: The FSSP-100 overestimates LWC for large droplets because these droplets are unable to follow the curved streamlines of the flow generated by drawing air into the FSSP-100’s sensitive volume at 25 m s−1. This inertial effect causes droplets to accumulate near the active volume of the instrument’s laser beam and to produce large and spurious droplet concentration and LWC values for the largest droplets. Model calculations estimate the magnitude of this error for the FSSP-100.
Shear and Curvature vorticity and Potential-Vorticity Interchanges: Interpretation and Application to a Cutoff Cyclone Event

Abstract Equations are presented for the evolution of isobaric shear and curvature vorticity and for isentropic shear and curvature potential vorticity in natural (streamline-following) coordinates, in the case of adiabatic, frictionless flow. In isobaric coordinates, two terms of equal magnitude and opposite sign arise in the respective tendency equations for shear and curvature vorticity; these terms represent conversions between shear and curvature vorticity in the sense that their sum does not alter the total tendency of absolute vorticity. In isentropic coordinates, only the conversion terms remain in the tendency equations for shear and curvature potential vorticity, consistent with potential-vorticity conservation. The vorticity and potential-vorticity conversions arise from (i) along-stream variations in wind speed in the presence of Lagrangian changes in wind direction and (ii) flow-normal gradients of Lagrangian changes in wind speed. The assumption of horizontal nondivergence simplifies the int.
Filamentary superconductivity in systems with competing charge density waves

Increasing experimental evidence suggests the occurrence of ﬁlamentary superconductivity in diﬀerent (quasi) two-dimensional physical systems. In this piece of work, we discuss the proposal that, under certain circumstances, this occurrence may be related to the competition with a phase characterized by charge ordering, in the form of charge density waves
Blind Identification of Ambisonic Reduced Room Impulse Response

Recently proposed Generalized Time-domain Velocity Vector (GTVV) is a generalization of relative room impulse response in spherical harmonic (aka Ambisonic) domain, that allows for blind estimation of early-echo parameters: the directions and relative delays of individual reflections. However, the derived closed-form expression of GTVV mandates few assumptions to hold, most important being that the impulse response of the reference signal needs to be a minimum-phase filter. In practice, the reference is obtained by spatial filtering towards the Direction-of-Arrival of the source, and the aforementioned condition is bounded by the performance of the applied beamformer (and thus, by the Ambisonic array order). In the present work, we circumvent this problem by directly modeling the impulse responses constituting the GTVV time series, which permits not only to relax the initial assumptions, but also to extract the information therein in a more consistent and efficient manner, entering the realm of blind system identification. Experiments using simulated and recorded room impulse responses confirm the effectiveness of the proposed approach.
Bayesian Reinforcement Learning with Limited Cognitive Load

All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This body of work provides an account of capacity-limited Bayesian reinforcement learning, a unifying normative framework for modeling the effect of processing constraints on learning and action selection. Here, we provide an accessible review of recent algorithms and theoretical results in this setting, paying special attention to how these ideas can be applied to studying questions in the cognitive and behavioral sciences.
Differentially Private Topological Data Analysis

This paper is the first to attempt differentially private (DP) topological data analysis (TDA), producing near-optimal private persistence diagrams. We analyze the sensitivity of persistence diagrams in terms of the bottleneck distance, and we show that the commonly used \v{C}ech complex has sensitivity that does not decrease as the sample size $n$ increases. This makes it challenging for the persistence diagrams of \v{C}ech complexes to be privatized. As an alternative, we show that the persistence diagram obtained by the $L^1$-distance to measure (DTM) has sensitivity $O(1/n)$. Based on the sensitivity analysis, we propose using the exponential mechanism whose utility function is defined in terms of the bottleneck distance of the $L^1$-DTM persistence diagrams. We also derive upper and lower bounds of the accuracy of our privacy mechanism; the obtained bounds indicate that the privacy error of our mechanism is near-optimal. We demonstrate the performance of our privatized persistence diagrams through simulations as well as on a real dataset tracking human movement.