Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast End-to-End Speech Recognition via a Non-Autoregressive Model and Cross-Modal Knowledge Transferring from BERT

—Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because the decoder predicts text tokens (such as characters or words) in an autoregressive manner, it is difﬁcult for an AED model to predict all tokens in parallel. This makes the inference speed relatively slow. We believe that because the encoder already captures the whole speech utterance, which has the token-level relationship implicitly, we can predict a token without explicitly autoregressive language modeling. When the prediction of a token does not rely on other tokens, the parallel prediction of all tokens in the sequence is realizable. Based on this idea, we propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once). The model consists of an encoder, a decoder, and a position dependent summarizer (PDS). The three modules are based on basic attention blocks. The encoder extracts high-level representations from the speech. The PDS uses positional encodings corresponding to tokens to convert the acoustic representations into token-level representations. The decoder further captures token-level relationships with the self-attention mechanism. At last, the probability distribution on the vocabulary is computed for each token position. Therefore, speech recognition is re-formulated as a position-wise classiﬁcation problem. Further, we propose a cross-modal transfer learning method to reﬁne semantics from a large-scale pre-trained language model BERT for improving the performance. We conduct experiments on two scales of public speech datasets AISHELL-1 and AISHELL-2. Experimental results show that our proposed model achieves a speedup of about 50 × and competitive performance, compared with the autoregressive transformer models. To better understand the behaviors of LASO, we analyze the model by visualizing the attention patterns. The results show that PDS can attend speciﬁc encoded acoustic representations based on positions and the decoder can capture token relationships.
A Generic Descent Aggregation Framework for Gradient-based Bi-level Optimization

—In recent years, gradient-based methods for solving bi-level optimization tasks have drawn a great deal of interest from the machine learning community. However, to calculate the gradient of the best response, existing research always relies on the singleton of the lower-level solution set (a.k.a., Lower-Level Singleton, LLS). In this work, by formulating bi-level models from an optimistic bi-level viewpoint, we ﬁrst establish a novel Bi-level Descent Aggregation (BDA) framework, which aggregates hierarchical objectives of both upper level and lower level. The ﬂexibility of our framework beneﬁts from the embedded replaceable task-tailored iteration dynamics modules, thereby capturing a wide range of bi-level learning tasks. Theoretically, we derive a new methodology to prove the convergence of BDA framework without the LLS restriction. Besides, the new proof recipe we propose is also engaged to improve the convergence results of conventional gradient-based bi-level methods under the LLS simpliﬁcation. Furthermore, we employ a one-stage technique to accelerate the back-propagation calculation in a numerical manner. Extensive experiments justify our theoretical results and demonstrate the superiority of the proposed algorithm for hyper-parameter optimization and meta-learning tasks.
The Oligocene–Miocene Transition on Coral Reefs in the Falcón Basin (NW Venezuela)

Abstract The Oligocene–Miocene Transition (OMT) was an interval of regional environmental and biotic change on Caribbean reefs. During the late Oligocene, a diverse Tethyan biota contributed to extensive reef building across the region, but by the early Miocene, reef building had declined, and a regional extinction had removed up to 50% of the late Oligocene diversity. The general decline in reef building in the Caribbean has been attributed to changes in regional water quality. New collections of scleractinian reef corals from four different units in the northwestern Falcón Basin of Venezuela include distinct late Oligocene and early Miocene assemblages. The distribution of fossil coral species and reef limestones suggests that the thick carbonates of the San Luis Formation were deposited during the late Oligocene and that changing water quality during the OMT resulted in the demise of San Luis coral-reef ecosystems. Previous studies have noted an increase in surface-water productivity after the OMT in the region and have suggested oceanographic reorganization as the primary cause; this interval, however, also coincides with a switch in the outlet position of an ancestral Orinoco River. A change in the terrestrial geography of South America might have caused the regional decline in reef building by altering surface-water characteristics, just as modern Orinoco and Amazon outflows exert strong control on shallow-water habitats off the coast of northeastern South America.
Status Quo Effects in Fairness Games: Acts of Commission vs. Acts of Omission

Intent to help or harm is revealed more clearly by acts of commission that overturn the status quo than by acts of omission that uphold it. Both the law and culture make a central distinction between the two types of acts. Acts of commission elicit stronger reciprocal responses than do acts of omission. In this paper we compare reciprocal responses to both types of acts and ask whether behavior of subjects in two experiments is consistent with existing theory. The design of the experiments focuses on the axioms of revealed altruism theory (Cox, Friedman, and Sadiraj, 2008) that make it observationally distinct from other theories, Axiom R (for reciprocity) and Axiom S (for status quo). We find support for this theory in both experiments.
Methane efflux from bubbles suspended in ice-covered lakes in Syowa Oasis, East Antarctica

[1] This is the first estimation of methane efflux from bubbles in lake ice in Antarctica. Bubbles suspended in shallow ice in 20 lakes were observed as part of the operations of the 45th Japanese Antarctic Research Expedition in ice-free rocky areas along the eastern coast of Lutzow-Holm Bay (Syowa Oasis) in East Antarctica in 2004. Anomalous methane concentrations in bubbles suspended in lake ice and anomalous dissolved methane concentrations in lake water were frequently found. Methane concentrations in bubbles varied of 5 orders of magnitude, from 1.9 ppmv to 18% vol/vol. A procedure that makes estimations of methane flux from ice bubbles possible has been developed, on the basis of a relationship between bubble-density distribution, brightness observed by digital photographs, and methane concentration in bubbles. Such a procedure applied to Lake Oyako Ike in the Skarvsnes area, where the maximum methane concentration was observed, suggests that total bubble volume is about 0.6% of ice volume and the mass of methane in bubbles in the lake is about 74 kg. Almost all gases in bubbles are released to the atmosphere in the early ice melt season (a period of a few weeks in December and January). By applying area fraction frequency distributions to methane concentration ranges for 20 lakes surveyed, extrapolation to the whole Syowa Oasis, including 110 lakes, would give a provisional estimate of total emission of about 2 tons-CH4/yr.
Joint cortical registration of geometry and function using semi-supervised learning

Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.
CHERI Performance Enhancement for the MicroPython Interpreter

During our port of the MicroPython bytecode interpreter to the CHERI-based Arm Morello platform, we encountered a number of serious performance degradations. This paper explores several of these of these performance issues in detail, in each case characterizing the cause of the problem, the fix, and the corresponding performance improvement over a set of standard Python benchmarks. While we recognize that Morello is a prototypical physical instantiation of the CHERI concept, we show that it is possible to eliminate certain kinds of software-induced runtime overhead that occur due to the larger size of architectural capabilities relative to native pointers. In our case, we reduce a geometric mean benchmark slowdown from 5x (before optimization) to 2x (after optimization) relative to AArch64 execution. The worst-case slowdowns are greatly improved, from 100x (before optimization) to 2x (after optimization). The key insight is that pointer size assumptions pervade systems code; whereas previous CHERI porting projects high-lighted compile-time and execution-time errors exposed by pointer size assumptions, we instead focus on the performance implications of such assumptions.
Go Figure! A Meta Evaluation of Factuality in Summarization

Text generation models can generate factually inconsistent text containing distorted or fabricated facts about the source text. Recent work has focused on building evaluation models to verify the factual correctness of semantically constrained text generation tasks such as document summarization. While the field of factuality evaluation is growing fast, we don't have well-defined criteria for measuring the effectiveness, generalizability, reliability, or sensitivity of the factuality metrics. Focusing on these aspects, in this paper, we introduce a meta-evaluation framework for evaluating factual consistency metrics. We introduce five necessary, common-sense conditions for effective factuality metrics and experiment with nine recent factuality metrics using synthetic and human-labeled factuality data from short news, long news and dialogue summarization domains. Our framework enables assessing the efficiency of any new factual consistency metric on a variety of dimensions over multiple summarization domains and can be easily extended with new meta-evaluation criteria. We also present our conclusions towards standardizing the factuality evaluation metrics.
A Framework for IP and MPLS Fast Reroute Using Not-Via Addresses

This document presents an illustrative framework for providing fast reroute in an IP or MPLS network through encapsulation and forwarding to "not-via" addresses. The general approach described here uses a single level of encapsulation and could be used to protect unicast, multicast, and LDP traffic against link, router, and shared risk group failure, regardless of network topology and metrics. The mechanisms presented in this document are purely illustrative of the general approach and do not constitute a protocol specification. The document represents a snapshot of the work of the Routing Area Working Group at the time of publication and is published as a document of record. Further work is needed before implementation or deployment.
A 2 2 3 {approximation Algorithm for the Shortest Superstring Problem

Given a collection of strings S = fs 1 ; : : :; s n g over an alphabet , a superstring of S is a string containing each s i as a substring; that is, for each i, 1 i n, contains a block of js i j consecutive characters that match s i exactly. The shortest superstring problem is the problem of nding a superstring of minimum length. The shortest superstring problem has applications in both data compression and computational biology. In data compression, the problem is a part of a general model of string compression proposed by Gallant, Maier and Storer (JCSS '80). Much of the recent interest in the problem is due to its application to DNA sequence assembly. The problem has been shown to be NP-hard; in fact, it was shown by Blum et al.(JACM '94) to be MAX SNP-hard. The rst O(1)-approximation was also due to Blum et al., who gave an algorithm that always returns a superstring no more than 3 times the length of an optimal solution. Several researchers have published results that improve on the approximation ratio; of these, the best previous result is our algorithm ShortString, which achieves a 2 3 4 {approximation (WADS '95). We present our new algorithm, G-ShortString, which achieves a ratio of 2 2 3. It generalizes the ShortString algorithm, but the analysis diers substantially from that of ShortString. Our previous work identied classes of strings that have a nested periodic structure, and which must be present in the worst case for our algorithms. We introduced machinery to descibe these strings and proved strong structural properties about them. In this paper we extend this study to strings that exhibit a more relaxed form of the same structure, and we use this understanding to obtain our improved result.
Mind garden

V IS U A L P R O C E E D IN G S 92 Mind Garden is a three-dimensional audio, visual, and neurolinguistic journey through a fractal garden environment. Using MindSet, a state-ofthe-art interactive brainwave software, human brainwaves activate fractal forms, sounds, and words as objects in this VRML world. Growth in the Mind Garden is viewable on the World Wide Web and can be monitored at the Mind Garden Web site:
Fracture detection using P-wave and S-wave vertical seismic profiling at The Geysers

In a pilot vertical seismic profiling study, P-wave and cross‐polarized S-wave vibrators were used to investigate the potential utility of shear‐wave anisotropy measurements in characterizing a fractured rock mass. The caprock at The Geysers geothermal field was found to exhibit about an 11 percent velocity variation between SH-waves and SV-waves generated by rotating the S-wave vibrator orientation to two orthogonal polarizations for each survey level in the well. The effect is generally consistent with the equivalent anisotropy expected from the known fracture geometry.
On representation of natural image patches

Starting from the ﬁrst principle I derive an unsupervised learning method named even code to model local statistics of natural images. The ﬁrst version uses orthogonal bases with independent states to model simple probability distribution of a few pixels. The second version uses a microscopic loss function to learn a nonlinear sparse binary representation of image patches. The distance in the binary representation space reﬂects image patch similarity. The learned model also has local edge detecting and orientation selective units like early visual systems.
Towards Visual Affordance Learning: A Benchmark for Affordance Segmentation and Recognition

The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision. A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To intelligently interact with objects, robots and intelligent machines need the ability to infer beyond the traditional physical/textural attributes, and understand/learn visual cues, called visual affordances, for affordance recognition, detection and segmentation. To date there is no publicly available large dataset for visual affor-dance understanding and learning. In this paper, we introduce a large scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories. To the best of our knowledge, this is the ﬁrst ever and the largest multi-view RGBD visual affordance learning dataset. We benchmark the proposed dataset for affordance segmentation and recognition tasks using popular Vision Transformer and Convolutional Neural Networks. Several state-of-the-art deep learning networks are evaluated each for affordance recognition and segmentation tasks. Our experimental re-sults showcase the challenging nature of the dataset and present deﬁnite prospects for new and robust affordance learning algorithms. The dataset is publicly available at https://sites.google. com/view/afaqshah/dataset.
Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest

Unmanned Aerial Vehicle (UAV) technology provides potential for very high spatial resolution (<25 cm) mapping of relatively large areas at a user-defined re-survey frequency. In a riparian context, UAV technology provides a mechanism for riparian managers to (a) quantify riparian terrain and vegetation units and (b) identify standing dead wood and canopy mortality. In this study a paraglider UAV was used to survey 174 ha at 6.8–21.8 cm ground resolution. Pixel-based and object-oriented classification approaches were used at the scale of a single image and a channel mosaic. Significant potential was demonstrated: vegetation units were classified with an accuracy of kappa = 0.79 and standing dead wood units were identified with an average accuracy with respect to omission and commission errors of 80% and 65%, respectively. Work across multiple images identified that major constraints currently result from factors such as illumination conditions and sensor movement during flight, which create variations in spatial resolution and radiometry. It is expected that with further methodological refinement and more complex methods of automated radiometric correction UAV technology can provide the flexibility to rapidly produce very high resolution map products to aid riparian management.
Kernel Metric Learning for Clustering Mixed-type Data

Distance-based clustering and classification are widely used in various fields to group mixed numeric and categorical data. A predefined distance measurement is used to cluster data points based on their dissimilarity. While there exist numerous distance-based measures for data with pure numerical attributes and several ordered and unordered categorical metrics, an optimal distance for mixed-type data is an open problem. Many metrics convert numerical attributes to categorical ones or vice versa. They handle the data points as a single attribute type or calculate a distance between each attribute separately and add them up. We propose a metric that uses mixed kernels to measure dissimilarity, with cross-validated optimal kernel bandwidths. Our approach improves clustering accuracy when utilized for existing distance-based clustering algorithms on simulated and real-world datasets containing pure continuous, categorical, and mixed-type data.
Facetron: Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations

In this paper, we propose an effective method to synthesize speaker-speciﬁc speech waveforms by conditioning on videos of an individual’s face. Using a generative adversarial network (GAN) with linguistic and speaker characteristic features as auxiliary conditions, our method directly converts face images into speech waveforms under an end-to-end training framework. The linguistic features are extracted from lip movements using a lip-reading model, and the speaker characteristic features are predicted from face images using cross-modal learning with a pre-trained acoustic model. Since these two features are un-correlated and controlled independently, we can ﬂexibly synthesize speech waveforms whose speaker characteristics vary depending on the input face images. Therefore, our method can be regarded as a multi-speaker face-to-speech waveform model. We show the superiority of our proposed model over conventional methods in terms of both objective and subjective evaluation results. Speciﬁcally, we evaluate the performances of the linguistic feature and the speaker characteristic generation modules by measuring the accuracy of automatic speech recognition and automatic speaker/gender recognition tasks, respectively. We also evaluate the naturalness of the synthesized speech waveforms using a mean opinion score (MOS) test.
On Reconfiguration Graph of Independent Sets under Token Sliding

An independent set of a graph G is a vertex subset I such that there is no edge joining any two vertices in I . Imagine that a token is placed on each vertex of an independent set of G . The TS - ( TS k -) reconﬁguration graph of G takes all non-empty independent sets (of size k ) as its nodes, where k is some given positive integer. Two nodes are adjacent if one can be obtained from the other by sliding a token on some vertex to one of its unoccupied neighbors. This paper focuses on the structure and realizability of these reconﬁguration graphs. More precisely, we study two main questions for a given graph G : (1) Whether the TS k -reconﬁguration graph of G belongs to some graph class G (including complete graphs, paths, cycles, complete bipartite graphs, and connected split graphs) and (2) If G satisﬁes some property P (including s -partitedness, planarity, Eulerianity, girth, and the clique’s size), whether the corresponding TS - ( TS k -) reconﬁguration graph of G also satisﬁes P , and vice versa.
Design and Deployment of a Personalized News Service

From 2008-2010 we built an experimental personalized news system where readers subscribe to organized channels of topical information that are curated by experts. AI technology was employed to efficiently present the right information to each reader and to radically reduce the workload of curators. The system went through three implementation cycles and processed over 20 million news stories from about 12,000 RSS feeds on over 8000 topics organized by 160 curators for over 600 registered readers. This paper describes the approach, engineering and AI technology of the system.
Value of a Platform to a Seller: Case of American Airlines and Online Travel Agencies

We approach the issue of the value of a platform to a seller in a two-sided market where both buyers and sellers multi-home. A seller that loses access to a major buyer platform can potentially incur substantial financial losses. We exploit a recent conflict between American Airlines and two leading online travel agencies (Expedia and Orbitz), which dropped American Airlines fare quotes during the first quarter of 2011. We present a simple model of airline ticket distribution. This model provides a framework to analyze the events that happened in the American Airlines – online travel agency conflict. We analyze price data for the first quarter of 2010 and 2011, employing a simple difference-in-differences identification strategy to evaluate changes in American Airlines’ fares. After controlling for across-market heterogeneity, carrier-specific time-invariant effects, and time-specific carrier-invariant effects, American Airlines’ fares during the conflict were 2.7-4.2 percent lower than similar fares charged by American’s main competitors (United, Continental, Delta, and US Airways). The fare effect is most pronounced in the sub-sample of one-stop itineraries, where competition is stronger, and customers are more likely to have to rely on travel agents – rather than carriers’ own web-sites – for flight bookings. In sum, our findings indicate that access to major buyer platforms is considerably valuable to a seller. We estimate that during the first quarter of 2011 the loss of access to the Expedia/Orbitz platforms resulted in over $50 million reduction in revenue for American Airlines.