Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALISIS PENGEMBANGAN FASILITAS PELABUHAN PERIKANAN NUSANTARA PRIGI KABUPATEN TRENGGALEK JAWA TIMUR DITINJAU DARI ASPEK PRODUKSI Analysis of Development Facility of Prigi Archipelagic Fishing Port Trenggalek Regency East Java Seen from the Aspect of Production

Prigi Archipelagic Fishing Port (PPN) in Trenggalek have basic, functional and support fasilities. Facilities of Prigi PPN but need development to to develop for anticipate future demand. This purpose of this research were to know the physical condition of the port facilities and existing problems in the Prigi archipelagic Fishing Port, to analys the level of utilization of port facilities in Prigi Archipelagic Fishing Port, problem mapping and solution development for development Prigi Archipelagic Fishing Port. This research was conducted in February 2015 at Prigi Archipelagic Fishing Port. The resech used descriptive method and data analysis used rate the level of utilization of the facilities Analysis, estimation analysis and Important Values And present Conditions (NPKS). The result showed that facilities condition of Prigi Archipelagic Fishing Port was good enough because the facilities in well-maintained and good condition. The level of utilization Prigi Archipelagic Fishing Port found that, land 100%, shipping lanes 92%, harbor pool at west side 97.59%, harbor pool at east side 72.6%, pier at west side with the ship's position parallel 179.3%, pier at west side with the ship's position prependicular 59.7%, pier at west side with the ship's position parallel 141%, pier at west side with the ship's position prependicular 57.8 %, and both of west and east side fish landing place 34% and 51%. The selected strategy was strategy in quadrant I, namely Priority development. Facilities in quadrants I and be priority development, namely management of Waste water treatment plant development, ships dock and drainage systems.
Efficient Language Modeling with Sparse all-MLP

All-MLP architectures have attracted increasing interest as an alternative to attention-based models. In NLP, recent work like gMLP shows that all-MLPs can match Transformers in language modeling, but still lag behind in downstream tasks. In this work, we analyze the limitations of MLPs in expressiveness, and propose sparsely activated MLPs with mixture-of-experts (MoEs) in both feature and input (token) dimensions. Such sparse all-MLPs significantly increase model capacity and expressiveness while keeping the compute constant. We address critical challenges in incorporating conditional computation with two routing strategies. The proposed sparse all-MLP improves language modeling perplexity and obtains up to 2$\times$ improvement in training efficiency compared to both Transformer-based MoEs (GShard, Switch Transformer, Base Layers and HASH Layers) as well as dense Transformers and all-MLPs. Finally, we evaluate its zero-shot in-context learning performance on six downstream tasks, and find that it surpasses Transformer-based MoEs and dense Transformers.
Determinants of Small Milk Producers’ Participation in Organized Dairy Value Chains: Evidence from India

Indian dairy industry which was highly regulated till late-1990s, has been completely liberalized and exposed to world competition. The past decade has seen rapid changes in the Indian dairy sector and there have been growing concerns on the likely impacts of these changes on smallholder producers. The present paper has analysed determinants of market channel choices of small milk producers based on farm household survey and has investigated what impacts these market channel choices may have on farmers’ income and technology adoption. The study has found that though there have been emerging milk marketing channels, the traditional sector still dominates. The analysis has indicated that small dairy farmers are not excluded from the cooperatives but are excluded from the modern private sector.
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records

Question Answering on Electronic Health Records (EHR-QA) has a significant impact on the healthcare domain, and it is being actively studied. Previous research on structured EHR-QA focuses on converting natural language queries into query language such as SQL or SPARQL (NLQ2Query), so the problem scope is limited to pre-defined data types by the specific query language. In order to expand the EHR-QA task beyond this limitation to handle multi-modal medical data and solve complex inference in the future, more primitive systemic language is needed. In this paper, we design the program-based model (NLQ2Program) for EHR-QA as the first step towards the future direction. We tackle MIMICSPARQL*, the graph-based EHR-QA dataset, via a program-based approach in a semi-supervised manner in order to overcome the absence of gold programs. Without the gold program, our proposed model shows comparable performance to the previous state-of-the-art model, which is an NLQ2Query model (0.9% gain). In addition, for a reliable EHR-QA model, we apply the uncertainty decomposition method to measure the ambiguity in the input question. We empirically confirmed data uncertainty is most indicative of the ambiguity in the input question.
Staged Training for Transformer Language Models

The current standard approach to scaling transformer language models trains each model size from a different random initialization. As an alternative, we consider a staged training setup that begins with a small model and incrementally increases the amount of compute used for training by applying a"growth operator"to increase the model depth and width. By initializing each stage with the output of the previous one, the training process effectively re-uses the compute from prior stages and becomes more efficient. Our growth operators each take as input the entire training state (including model parameters, optimizer state, learning rate schedule, etc.) and output a new training state from which training continues. We identify two important properties of these growth operators, namely that they preserve both the loss and the"training dynamics"after applying the operator. While the loss-preserving property has been discussed previously, to the best of our knowledge this work is the first to identify the importance of preserving the training dynamics (the rate of decrease of the loss during training). To find the optimal schedule for stages, we use the scaling laws from (Kaplan et al., 2020) to find a precise schedule that gives the most compute saving by starting a new stage when training efficiency starts decreasing. We empirically validate our growth operators and staged training for autoregressive language models, showing up to 22% compute savings compared to a strong baseline trained from scratch. Our code is available at https://github.com/allenai/staged-training.
Ensemble plasticity and network adaptability in SNNs

Artificial Spiking Neural Networks (ASNNs) promise greater information processing efficiency because of discrete event-based (i.e., spike) computation. Several Machine Learning (ML) applications use biologically inspired plasticity mechanisms as unsupervised learning techniques to increase the robustness of ASNNs while preserving efficiency. Spike Time Dependent Plasticity (STDP) and Intrinsic Plasticity (IP) (i.e., dynamic spiking threshold adaptation) are two such mechanisms that have been combined to form an ensemble learning method. However, it is not clear how this ensemble learning should be regulated based on spiking activity. Moreover, previous studies have attempted threshold based synaptic pruning following STDP, to increase inference efficiency at the cost of performance in ASNNs. However, this type of structural adaptation, that employs individual weight mechanisms, does not consider spiking activity for pruning which is a better representation of input stimuli. We envisaged that plasticity-based spike-regulation and spike-based pruning will result in ASSNs that perform better in low resource situations. In this paper, a novel ensemble learning method based on entropy and network activation is introduced, which is amalgamated with a spike-rate neuron pruning technique, operated exclusively using spiking activity. Two electroencephalography (EEG) datasets are used as the input for classification experiments with a three-layer feed forward ASNN trained using one-pass learning. During the learning process, we observed neurons assembling into a hierarchy of clusters based on spiking rate. It was discovered that pruning lower spike-rate neuron clusters resulted in increased generalization or a predictable decline in performance.
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Affective computing plays a key role in human-computer interactions, entertainment, teaching, safe driving, and multimedia integration. Major breakthroughs have been made recently in the areas of affective computing (i.e., emotion recognition and sentiment analysis). Affective computing is realized based on unimodal or multimodal data, primarily consisting of physical information (e.g., textual, audio, and visual data) and physiological signals (e.g., EEG and ECG signals). Physical-based affect recognition caters to more researchers due to multiple public databases. However, it is hard to reveal one's inner emotion hidden purposely from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring physiological signals also hinders their practical application. Thus, the fusion of physical information and physiological signals can provide useful features of emotional states and lead to higher accuracy. Instead of focusing on one specific field of affective analysis, we systematically review recent advances in the affective computing, and taxonomize unimodal affect recognition as well as multimodal affective analysis. Firstly, we introduce two typical emotion models followed by commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some important aspects on affective computing and their applications and conclude this review with an indication of the most promising future directions, such as the establishment of baseline dataset, fusion strategies for multimodal affective analysis, and unsupervised learning models.
Parameterization of hydrodynamic friction in a model for sheared suspensions of rough particles

In this work, we propose a method to parameterize a coarse grained, discrete element model for the hydrodynamic friction between nearly touching rough spheres in suspension flows. The frictional resistance due to surface roughness primarily alters the sliding and rolling modes of motion of rough particles. Near contact Stokesian dynamics simulations incorporating a near-field pairwise resistance model accounting for these enhanced frictional modes was employed to compute particle trajectories in shear flow. In this model, the resistance to sliding and rolling modes of motion are augmented from a weakly diverging form proportional to log(1/h), characteristic of smooth spheres, to a strongly diverging form scaling as 1/h for rough spheres, where h is the mean surface to surface distance between nearly touching particles. The augmentation reflects the hydrodynamic resistance due to squeezing flows between surface asperities. We determine new bounds on the relative magnitude of the augmentations to the resistance to different modes of motion using inequality constraints reflecting the positive definiteness of the Stokes resistance tensor for a pair of the rough particles. Using the simulations of a particle pair in a shear flow, a simple model for angular rotation rate of the line of centers of the pair, ω, is computed as a function of its orientation in the linear shear flow and the two free parameters of the augmented hydrodynamic resistance model: the friction coupling strength, α, and friction coupling range, h0. Values of α and h0 for rough particles synthesized experimentally can then be inferred by matching the pair rotation rate in the model to experimental observations of the same rotation rate when a dilute suspension of these rough particles is subjected to a linear shear flow. The same model is used to calculate the hydrodynamic contribution to the high frequency viscosity of rough particle suspensions. For different α and h0 while assuming the particles otherwise behave as hard spheres, we observe that the viscosity diverges differently depending on the ratio of h0 to the hydrodynamic radius of the particles.
Schwarzschild and Ledoux are Equivalent on Evolutionary Timescales

Stellar evolution models calculate convective boundaries using either the Schwarzschild or Ledoux criterion, but confusion remains regarding which criterion to use. Here we present a 3D hydrodynamical simulation of a convection zone and adjacent radiative zone, including both thermal and compositional buoyancy forces. As expected, regions that are unstable according to the Ledoux criterion are convective. Initially, the radiative zone adjacent to the convection zone is Schwarzschild unstable but Ledoux stable due to a composition gradient. Over many convective overturn timescales, the convection zone grows via entrainment. The convection zone saturates at the size originally predicted by the Schwarzschild criterion, although in this final state the Schwarzschild and Ledoux criteria agree. Therefore, the Schwarzschild criterion should be used to determine the size of stellar convection zones, except possibly during short-lived evolutionary stages in which entrainment persists.
Nuclear effects in the deuteron and global QCD analyses

We report the results of a new global QCD analysis, which includes deep-inelastic 𝑒/𝜇 scattering data off proton and deuterium, as well as Drell-Yan lepton pair production in proton-proton and proton-deuterium collisions and 𝑊 ± /𝑍 boson production data from 𝑝𝑝 and 𝑝 ¯ 𝑝 collisions at the LHC and Tevatron. Nuclear effects in the deuteron are treated in terms of a nuclear convolution approach with bound off-shell nucleons within a weak binding approximation. The off-shell correction is controlled by a universal function of the Bjorken variable 𝑥 describing the modification of parton distributions in bound nucleons, which is determined in our analysis along with the parton distribution functions of the proton. A number of systematic studies are performed to estimate the uncertainties arising from the use of various deuterium datasets, from the modeling of higher twist contributions to the structure functions, from the treatment of target mass corrections, as well as from the nuclear corrections in the deuteron. We obtain predictions for the ratios 𝐹 𝑛 2 /𝐹 𝑝 2 , and 𝑑/𝑢 , focusing on the region of large 𝑥 . We also compare our results with the ones obtained by other QCD analyses, as well as with the recent data from the MARATHON experiment.
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution

The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low-frame-rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synthesize the missing frame embedding, which cannot fully explore the information flow of consecutive input LR frames. In addition, existing STVSR models hardly exploit the temporal contexts explicitly to assist high-resolution (HR) frame reconstruction. To address these issues, in this article, we propose a deformable attention network called STDAN for STVSR. First, we devise a long short-term feature interpolation (LSTFI) module that is capable of excavating abundant content from more neighboring input frames for the interpolation process through a bidirectional recurrent neural network (RNN) structure. Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction. Experimental results on several datasets demonstrate that our approach outperforms state-of-the-art STVSR methods. The code is available at https://github.com/littlewhitesea/STDAN.
Masked Autoencoders for Point Cloud Self-supervised Learning

As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point cloud's properties, including leakage of location information and uneven information density. Concretely, we divide the input point cloud into irregular point patches and randomly mask them at a high ratio. Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point patches. Extensive experiments show that our approach is efficient during pre-training and generalizes well on various downstream tasks. Specifically, our pre-trained models achieve 85.18% accuracy on ScanObjectNN and 94.04% accuracy on ModelNet40, outperforming all the other self-supervised learning methods. We show with our scheme, a simple architecture entirely based on standard Transformers can surpass dedicated Transformer models from supervised learning. Our approach also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot object classification. Furthermore, our work inspires the feasibility of applying unified architectures from languages and images to the point cloud.
Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound

In this work, we revisit the semi-supervised learning (SSL) problem from a new perspective of explicitly reducing empirical distribution mismatch between labeled and unlabeled samples. Benefited from this new perspective, we first propose a new deep semi-supervised learning framework called Semi-supervised Learning by Empirical Distribution Alignment (SLEDA), in which existing technologies from the domain adaptation community can be readily used to address the semi-supervised learning problem through reducing the empirical distribution distance between labeled and unlabeled data. Based on this framework, we also develop a new theoretical generalization bound for the research community to better understand the semi-supervised learning problem, in which we show the generalization error of semi-supervised learning can be effectively bounded by minimizing the training error on labeled data and the empirical distribution distance between labeled and unlabeled data. Building upon our new framework and the theoretical bound, we develop a simple and effective deep semi-supervised learning method called Augmented Distribution Alignment Network (ADA-Net) by simultaneously adopting the well-established adversarial training strategy from the domain adaptation community and a simple sample interpolation strategy for data augmentation. Additionally, we incorporate both strategies in our ADA-Net into two exiting SSL methods to further improve their generalization capability, which indicates that our new framework provides a complementary solution for solving the SSL problem. Our comprehensive experimental results on two benchmark datasets SVHN and CIFAR-10 for the semi-supervised image recognition task and another two benchmark datasets ModelNet40 and ShapeNet55 for the semi-supervised point cloud recognition task demonstrate the effectiveness of our proposed framework for SSL.
NeILF: Neural Incident Light Field for Physically-based Material Estimation

We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry. In the framework, we represent scene lightings as the Neural Incident Light Field (NeILF) and material properties as the surface BRDF modelled by multi-layer perceptrons. Compared with recent approaches that approximate scene lightings as the 2D environment map, NeILF is a fully 5D light field that is capable of modelling illuminations of any static scenes. In addition, occlusions and indirect lights can be handled naturally by the NeILF representation without requiring multiple bounces of ray tracing, making it possible to estimate material properties even for scenes with complex lightings and geometries. We also propose a smoothness regularization and a Lambertian assumption to reduce the material-lighting ambiguity during the optimization. Our method strictly follows the physically-based rendering equation, and jointly optimizes material and lighting through the differentiable rendering process. We have intensively evaluated the proposed method on our in-house synthetic dataset, the DTU MVS dataset, and real-world BlendedMVS scenes. Our method is able to outperform previous methods by a significant margin in terms of novel view rendering quality, setting a new state-of-the-art for image-based material and lighting estimation.
Mass Loss by Atmospheric Escape from Extremely Close-in Planets

We explore atmospheric escape from close-in exoplanets with the highest mass-loss rates. First, we locate the transition from stellar X-ray and UV-driven escape to rapid Roche lobe overflow, which occurs once the 10–100 nbar pressure level in the atmosphere reaches the Roche lobe. Planets enter this regime when the ratio of the substellar radius to the polar radius along the visible surface pressure level, which aligns with a surface of constant Roche potential, is X/Z ≳ 1.2 for Jovian planets (Mp ≳ 100 M ⊕) and X/Z ≳ 1.02 for sub-Jovian planets (M p ≈ 10–100 M ⊕). Around a Sun-like star, this regime applies to orbital periods of less than two days for planets with radii of about 3–14R⊕. Our results agree with the properties of known transiting planets and can explain parts of the sub-Jovian desert in the population of known exoplanets. Second, we present detailed numerical simulations of atmospheric escape from a planet like Uranus or Neptune orbiting close to a Sun-like star that support the results above and point to interesting qualitative differences between hot Jupiters and sub-Jovian planets. We find that hot Neptunes with solar-metallicity hydrogen and helium envelopes have relatively more extended upper atmospheres than typical hot Jupiters, with a lower ionization fraction and higher abundances of escaping molecules. This is consistent with existing ultraviolet transit observations of warm Neptunes, and it might provide a way to use future observations and models to distinguish solar-metallicity atmospheres from higher-metallicity atmospheres.