Miracle-dz/UniFAR-FTUs huggingface dataset:
The Nearly Forgotten Oyster: Ostrea lurida Carpenter 1864 (Olympia Oyster) History and Management in Washington State

ABSTRACT Overexploited fisheries are a worldwide problem. Restoration efforts aimed at these fisheries often involve a combination of reduced catch, hatcheries, and habitat improvement. The native oyster of western North America, Ostrea lurida,† was commercially extinct in most locations more than a century ago. In this paper, we track the history of its management for insight into its demise and failed recovery in Washington State. We document six phases of management: open access, aquaculture, control of water pollution sources, substitution by nonindigenous oysters, harvest regulations and marine reserves, and restoration. Three general lessons emerge from this historical analysis, which may apply generally to exploited fisheries that fail to recover. First, the introduction of substitute species led to neglect of the native species for many decades. Second, reserves were not fully protected and instead were designed for commercial removal of newly settled oysters, thus they largely failed. Finally, current restoration efforts are hampered by several biological problems, including water pollution, invasive competitors and predators, and habitat loss.
Event-Based Stereo Visual Odometry

Event-based cameras are bioinspired vision sensors whose pixels work independently from each other and respond asynchronously to brightness changes, with microsecond resolution. Their advantages make it possible to tackle challenging scenarios in robotics, such as high-speed and high dynamic range scenes. We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig. Our system follows a parallel tracking-and-mapping approach, where novel solutions to each subproblem (three-dimensional (3-D) reconstruction and camera pose estimation) are developed with two objectives in mind: being principled and efficient, for real-time operation with commodity hardware. To this end, we seek to maximize the spatio-temporal consistency of stereo event-based data while using a simple and efficient representation. Specifically, the mapping module builds a semidense 3-D map of the scene by fusing depth estimates from multiple viewpoints (obtained by spatio-temporal consistency) in a probabilistic fashion. The tracking module recovers the pose of the stereo rig by solving a registration problem that naturally arises due to the chosen map and event data representation. Experiments on publicly available datasets and on our own recordings demonstrate the versatility of the proposed method in natural scenes with general 6-DoF motion. The system successfully leverages the advantages of event-based cameras to perform visual odometry in challenging illumination conditions, such as low-light and high dynamic range, while running in real-time on a standard CPU. We release the software and dataset under an open source license to foster research in the emerging topic of event-based simultaneous localization and mapping.
Pattern and Seral Composition in an Old‐growth Beech‐Maple Forest

A abstract. A nested quadrat pattern analysis suggests that the adult members of early seral tree species exhibit maximal clumping at a quadrat size corresponding to the sizes of natural lightgaps created by treefalls. However, the dominant species show a pattern of randomness at nearly all quadrat sizes. Some seral characteristics of forest trees support the idea that the beech-maple forest is a mosaic of species of different seral stages.
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction

In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameters bounds reduction) allowing a robot to use meta-learning mechanism increasing the efficiency of such continuous and constrained parameters optimizations. The new optimization, viewed as a learning for the robot, can take advantage of past experiences (stored in the episodic and procedural memories) to shrink the search space by using reduced parameters bounds computed from the best optimizations realized by the robot with similar tasks of the new one (e.g. bin-picking from an homogenous heap of a similar object, based on visual similarity of objects stored in the semantic memory). As example, we have confronted the system to the constrained optimizations of 9 continuous hyperparameters for a professional software (Kamido) in industrial robotic arm bin-picking tasks, a step that is needed each time to handle correctly new object. We used a simulator to create bin-picking tasks for 8 different objects (7 in simulation and one with real setup, without and with meta-learning with experiences coming from other similar objects) achieving goods results despite a very small optimization budget, with a better performance reached when meta-learning is used (84.3 % vs 78.9 % of success overall, with a small budget of 30 iterations for each optimization) for every object tested (p-value=0.036).
TeleHammer: A Formal Model of Implicit Rowhammer

The rowhammer bug allows an attacker to gain privilege escalation or steal private data. A key requirement of all existing rowhammer attacks is that an attacker must have access to at least part of an exploitable hammer row. We refer to such rowhammer attacks as PeriHammer. The state-of-the-art software-only defenses against PeriHammer attacks is to make the exploitable hammer rows beyond the attacker's access permission. In this paper, we question the necessity of the above requirement and propose a new class of rowhammer attacks, termed as TeleHammer. It is a paradigm shift in rowhammer attacks since it crosses privilege boundary to stealthily rowhammer an inaccessible row by implicit DRAM accesses. Such accesses are achieved by abusing inherent features of modern hardware and or software. We propose a generic model to rigorously formalize the necessary conditions to initiate TeleHammer and PeriHammer, respectively. Compared to PeriHammer, TeleHammer can defeat the advanced software-only defenses, stealthy in hiding itself and hard to be mitigated. To demonstrate the practicality of TeleHammer and its advantages, we have created a TeleHammer's instance, called PThammer, which leverages the address-translation feature of modern processors. We observe that a memory access from user space can induce a load of a Level-1 page-table entry (L1PTE) from memory and thus hammer the L1PTE once, although L1PTE is not accessible to us. To achieve a high enough hammering frequency, we flush relevant TLB and cache effectively and efficiently. To this end, we demonstrate PThammer on three different test machines and show that it can cross user-kernel boundary and induce the first bit flips in L1PTEs within 15 minutes of double-sided PThammering. We have exploited PThammer to defeat advanced software-only rowhammer defenses in default system setting.
Postfire vegetation recovery and tree establishment at the Arctic treeline: climate-change-vegetation-response hypotheses

1) A fire of unusually great severity (deep burning) burned across the forest-tundra ecotone near Inuvik, Northwest Territories from August 8 to 18, 1968. 2) Burned-unburned paired study sites around the fire perimeter, which had been established in both tundra and forest-tundra in 1973 were relocated in 1990. These showed that total vascular plant cover had reached prefire levels after 22 years, that tall shrubs had become dominant in the tundra and that biomass was now sufficient to support another fire. Cryptogams showed minimum recovery between the two studies. 3) In previously treed areas postfire densities of Picea mariana and Picea glauca were much lower than before
Zooplankton community structure, but not biomass, influences the phosphorus-chlorophyll a relationship

Simultaneous observations were made of total phosphorus (TP), chlorophyll a (Chl a), and zooplankton biomass and community structure at 12 sites in the Eastern Townships (ET) region of Quebec to determine if zooplankton parameters reduced residual variation in the TP–Chl a relationship. Similar data from three stations in Lake Memphremagog and the literature were also analyzed. Zooplankton biomass was not significant in explaining residual variation in the TP–Chl a relationship of any data set studied. The mean body size of cladocerans was also not a significant additional variable in a TP–Chl a model based on the seasonal mean values in the ET lakes, but cladoceran body size was a significant factor in 13 lakes of the Indian River district of New York. The difference between these results is attributed to the rarity of large daphnids in the ET lakes. An index of zooplankton community structure derived from the slope of log abundance–log weight regressions was significant in explaining residual variation .
The Return of Lexical Dependencies: Neural Lexicalized PCFGs

Abstract In this paper we demonstrate that context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies. This contrasts to the most popular current methods for grammar induction, which focus on discovering either constituents or dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g., lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs that allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone.1
dMelodies: A Music Dataset for Disentanglement Learning

Representation learning focused on disentangling the underlying factors of variation in given data has become an important area of research in machine learning. However, most of the studies in this area have relied on datasets from the computer vision domain and thus, have not been readily extended to music. In this paper, we present a new symbolic music dataset that will help researchers working on disentanglement problems demonstrate the efficacy of their algorithms on diverse domains. This will also provide a means for evaluating algorithms specifically designed for music. To this end, we create a dataset comprising of 2-bar monophonic melodies where each melody is the result of a unique combination of nine latent factors that span ordinal, categorical, and binary types. The dataset is large enough (approx. 1.3 million data points) to train and test deep networks for disentanglement learning. In addition, we present benchmarking experiments using popular unsupervised disentanglement algorithms on this dataset and compare the results with those obtained on an image-based dataset.
Rethinking Recurrent Neural Networks and other Improvements for Image Classification

For a long history of Machine Learning which dates back to several decades, Recurrent Neural Networks (RNNs) have been mainly used for sequential data and time series or generally 1D information. Even in some rare researches on 2D images, the networks merely learn and generate data sequentially rather than for recognition of images. In this research, we propose to integrate RNN as an additional layer in designing image recognition's models. Moreover, we develop End-to-End Ensemble Multi-models that are able to learn experts' predictions from several models. Besides, we extend training strategy and softmax pruning which overall leads our designs to perform comparably to top models on several datasets. The source code of the methods provided in this article is available in this https URL and this http URL.
Plasma dark matter and electronic recoil events in XENON1T

Dark matter might be in the form of a dark plasma in the Milky Way halo. Specifically, we consider here a hidden sector consisting of a light `dark electron' and a much heavier `dark proton', each charged under an unbroken U(1)′ gauge symmetry. These self-interacting dark sector particles can also interact with ordinary matter via the kinetic mixing interaction, and lead to a signal in dark matter direct detection experiments. Indeed, keV electron recoils can arise quite naturally in such models from dark electron scattering off loosely bound atomic electrons. Here we examine the recently reported XENON1T excess in the context of such a plasma dark matter model. We find that the observed excess can be explained if kinetic mixing is in the approximate range: 10−12 ≲ ϵ ≲ 10−10. The allowed parameter space is consistent with astrophysical and cosmological constraints and consistent also with other direct detection experiments.
Instance Selection for GANs

Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery. While capable of generating photo-realistic images, these models often produce unrealistic samples which fall outside of the data manifold. Several recently proposed techniques attempt to avoid spurious samples, either by rejecting them after generation, or by truncating the model's latent space. While effective, these methods are inefficient, as large portions of model capacity are dedicated towards representing samples that will ultimately go unused. In this work we propose a novel approach to improve sample quality: altering the training dataset via instance selection before model training has taken place. To this end, we embed data points into a perceptual feature space and use a simple density model to remove low density regions from the data manifold. By refining the empirical data distribution before training we redirect model capacity towards high-density regions, which ultimately improves sample fidelity. We evaluate our method by training a Self-Attention GAN on ImageNet at 64x64 resolution, where we outperform the current state-of-the-art models on this task while using 1/2 of the parameters. We also highlight training time savings by training a BigGAN on ImageNet at 128x128 resolution, achieving a 66% increase in Inception Score and a 16% improvement in FID over the baseline model with less than 1/4 the training time.
Stable Learning via Causality-based Feature Rectification

How to learn a stable model under agnostic distribution shift between training and testing datasets is an essential problem in machine learning tasks. The agnostic distribution shift caused by data generation bias can lead to model misspecification and unstable performance across different test datasets. Most of the recently proposed methods are causality-based sample reweighting methods, whose performance is affected by sample size. Moreover, these works are restricted to linear models, not to deep-learning based nonlinear models. In this work, we propose a novel Causality-based Feature Rectification (CFR) method to address the model misspecification problem under agnostic distribution shift by using a weight matrix to rectify features. Our proposal based on the fact that the causality between stable features and the ground truth is consistent under agnostic distribution shift, but is partly omitted and statistically correlated with other features. We propose the feature rectification weight matrix to reconstruct the omitted causality by using other features as proxy variables. We further propose an algorithm that jointly optimizes the weight matrix and the regressor (or classifier). Our proposal can not only improve the stability of linear models, but also deep-learning based models. Extensive experiments on both synthetic and real-world datasets demonstrate that our proposal outperforms previous state-of-the-art stable learning methods. The code will be released later on.
Approximation of Smoothness Classes by Deep ReLU Networks

We consider approximation rates of sparsely connected deep rectified linear unit (ReLU) and rectified power unit (RePU) neural networks for functions in Besov spaces $B^\alpha_{q}(L^p)$ in arbitrary dimension $d$, on bounded or unbounded domains. We show that RePU networks with a fixed activation function attain optimal approximation rates for functions in the Besov space $B^\alpha_{\tau}(L^\tau)$ on the critical embedding line $1/\tau=\alpha/d+1/p$ for arbitrary smoothness order $\alpha>0$. Moreover, we show that ReLU networks attain near to optimal rates for any Besov space strictly above the critical line. Using interpolation theory, this implies that the entire range of smoothness classes at or above the critical line is (near to) optimally approximated by deep ReLU/RePU networks.
Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at https://github.com/ojedaf/IC-TIR-Lol .
Collapsing index: a new method to identify star-forming cores based on ALMA images

Stars form through the gravitational collapse of molecular cloud cores. Before collapsing, the cores are supported by thermal pressure and turbulent motions. A question of critical importance for the understanding of star formation is how to observationally discern whether a core has already initiated gravitational collapse or is still in hydrostatic balance. The canonical method to identify gravitational collapse is based on the observed radial density profile, which would change from Bonnor-Ebert type toward power laws as the core collapses. In practice, due to the projection effect, the resolution limit and other caveats, it has been difficult to directly reveal the dynamical status of cores, particularly in massive star forming regions. We here propose a novel, straightforward diagnostic, namely, the collapsing index (CI), which can be modeled and calculated based on the radial profile of the line width of dense gas. A meaningful measurement of CI requires spatially and spectrally resolved images of optically thin and chemically stable dense gas tracers. ALMA observations are making such data sets increasingly available for massive star forming regions. Applying our method to one of the deepest dense-gas spectral images ever taken toward such a region, namely, the Orion molecular cloud, we detect the dynamical status of selected cores. We observationally distinguished a collapsing core in a massive star forming region from a hydrostatical one. Our approach would help significantly improve our understanding of the interaction between gravity and turbulence within molecular cloud cores in the process of star formation.
Reasoning about strategies on collapsible pushdown arenas with imperfect information

Strategy Logic with imperfect information (SLiR) is a very expressive logic designed to express complex properties of strategic abilities in distributed systems. Previous work on SLiR focused on finite systems, and showed that the model-checking problem is decidable when information on the control states of the system is hierarchical among the players or components of the system, meaning that the players or components can be totally ordered according to their respective knowledge of the state. We show that moving from finite to infinite systems generated by collapsible (higher-order) pushdown systems preserves decidability, under the natural restriction that the stack content is visible. The proof follows the same lines as in the case of finite systems, but requires to use (collapsible) alternating pushdown tree automata. Such automata are undecidable, but semi-alternating pushdown tree automata were introduced and proved decidable, to study a strategic problem on pushdown systems with two players. In order to tackle multiple players with hierarchical information, we refine further these automata: we define direction-guided (collapsible) pushdown tree automata, and show that they are stable under projection, nondeterminisation and narrowing. For the latter operation, used to deal with imperfect information, stability holds under some assumption that is satisfied when used for systems with visible stack. We then use these automata to prove our main result.
grid2vec: Learning Efficient Visual Representations via Flexible Grid-Graphs

We propose grid2vec , a novel approach for image representation learning based on Graph Convolutional Network (GCN). Existing visual representation methods suﬀer from several issues, such as requiring high-computation, losing in-depth structures, and being restricted to speciﬁc objects. grid2vec converts an image to a low-dimensional feature vector. A key component of grid2vec is Flexible Grid-Graphs, a spatially-adaptive method based on the image key-points, as a ﬂexible grid, to generate the graph representation. It represents each image with a graph of unique node locations and edge distances. Nodes, in Flexible Grid-Graphs, describe the most representative patches in the image. We develop a multi-channel Convolutional Neural Network architecture to learn local features of each patch. We implement a hybrid node-embedding method, i.e., having spectral and non-spectral components. It aggregates the products of neighbours’ features and node’s eigenvector centrality score. We compare the performance of grid2vec with a set of state-of-the-art representation learning and visual recognition models. grid2vec has only 512 features in comparison to a range from VGG16 with 25,090 to NAS-Net with 487,874. We show the models’ superior accuracy in both binary and multi-class image classiﬁcation. Although we utilise imbalanced, low-size dataset, grid2vec shows stable and superior results against the well-known base classiﬁers.