Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive trading strategies across liquidity pools

In this article, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.
The Southern Stellar Stream Spectroscopic Survey (S5): Chemical Abundances of Seven Stellar Streams

We present high-resolution Magellan/MIKE spectroscopy of 42 red giant stars in seven stellar streams confirmed by the Southern Stellar Stream Spectroscopic Survey (S5): ATLAS, Aliqa Uma, Chenab, Elqui, Indus, Jhelum, and Phoenix. Abundances of 30 elements have been derived from over 10,000 individual line measurements or upper limits using photometric stellar parameters and a standard LTE analysis. This is currently the most extensive set of element abundances for stars in stellar streams. Three streams (ATLAS, Aliqa Uma, and Phoenix) are disrupted metal-poor globular clusters, although only weak evidence is seen for the light-element anticorrelations commonly observed in globular clusters. Four streams (Chenab, Elqui, Indus, and Jhelum) are disrupted dwarf galaxies, and their stars display abundance signatures that suggest progenitors with stellar masses ranging from 106 to 107 M⊙. Extensive description is provided for the analysis methods, including the derivation of a new method for including the effect of stellar parameter correlations on each star’s abundance and uncertainty. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.
Efficient Private Machine Learning by Differentiable Random Transformations

With the increasing demands for privacy protection, many privacy-preserving machine learning systems were proposed in recent years. However, most of them cannot be put into production due to their slow training and inference speed caused by the heavy cost of homomorphic encryption and secure multiparty computation(MPC) methods. To circumvent this, I proposed a privacy definition which is suitable for large amount of data in machine learning tasks. Based on that, I showed that random transformations like linear transformation and random permutation can well protect privacy. Merging random transformations and arithmetic sharing together, I designed a framework for private machine learning with high efficiency and low computation cost.
Deep Search Query Intent Understanding

Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user's need by rendering results from more relevant categories. This paper aims to provide a comprehensive learning framework for modeling query intent under different stages of a search. We focus on the design for 1) predicting users' intents as they type in queries on-the-fly in typeahead search using character-level models; and 2) accurate word-level intent prediction models for complete queries. Various deep learning components for query text understanding are experimented. Offline evaluation and online A/B test experiments show that the proposed methods are effective in understanding query intent and efficient to scale for online search systems.
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting

Multivariate time series forecasting is widely used in various fields. Reasonable prediction results can assist people in planning and decision-making, generate benefits and avoid risks. Normally, there are two characteristics of time series, that is, long-term trend and short-term fluctuation. For example, stock prices will have a long-term upward trend with the market, but there may be a small decline in the short term. These two characteristics are often relatively independent of each other. However, the existing prediction methods often do not distinguish between them, which reduces the accuracy of the prediction model. In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed. This method uses the original time series and its first difference to characterize long-term trends and short-term fluctuations. Three prediction sub-networks are constructed to predict long-term trends, short-term fluctuations and the final value to be predicted. In the overall optimization goal, the idea of multi-task learning is used for reference, which is to make the prediction results of long-term trends and short-term fluctuations as close to the real values as possible while requiring to approximate the values to be predicted. In this way, the proposed method uses more supervision information and can more accurately capture the changing trend of the time series, thereby improving the forecasting performance.
Quantifying the information impact of future searches for exoplanetary biosignatures

Significance The search for life on extrasolar worlds by way of spectroscopic biosignature detection is among the most compelling scientific endeavors of the next decades. This article explores the implications of either discovering or ruling out the presence of detectable biosignatures on planets within a few tens of light years from Earth, a distance within reach of future searches. Using a Bayesian methodology, we show that not detecting biosignatures in such sample volume would bring no added information about the galactic population of life-hosting exoplanets. Conversely, if life arose independently on other planets, even a single detection would imply exobiospheres to be more abundant than pulsars. Putative interstellar transfer of life through the panspermia mechanism may, however, significantly lower this estimate. One of the major goals for astronomy in the next decades is the remote search for biosignatures (i.e., the spectroscopic evidence of biological activity) in exoplanets. Here we adopt a Bayesian statistical framework to discuss the implications of such future searches, both in the case when life is detected and when no definite evidence is found. We show that even a single detection of biosignatures in the vicinity of our stellar system, in a survey of similar size to what will be obtainable in the next 2 decades, would affect significantly our prior belief on the frequency of life in the universe, even starting from a neutral or pessimistic stance. In particular, after such discovery, an initially agnostic observer would be led to conclude that there are more than 105 inhabited planets in the galaxy with a probability exceeding 95%. However, this conclusion would be somewhat weakened by the viability of transfer of biological material over interstellar distances, as in panspermia scenarios. Conversely, the lack of significant evidence of biosignatures would have little effect, leaving the assessment of the abundance of life in the galaxy still largely undetermined.
Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects

The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads. To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem. Computer vision 3D reconstruction approaches project objects and the human body from the 2D image domain to 3D and perform 3D space intersection directly. However, this solution would not meet the accuracy requirement in applications due to projection error. Another standard approach is to train a neural network to infer touch actions from the collected visual data. This strategy would require significant amounts of training data to generalize over scale and viewpoint variations. A different approach to this problem is to identify whether a person has touched a defined object. In this work, we show that the solution to this problem can be straightforward. Specifically, we show that the contact between an object and a static surface can be identified by projecting the object onto the static surface through two different viewpoints and analyzing their 2D intersection. The object contacts the surface when the projected points are close to each other; we call this cross view projection consistency. Instead of doing 3D scene reconstruction or transfer learning from deep networks, a mapping from the surface in the two camera views to the surface space is the only requirement. For planar space, this mapping is the Homography transformation. This simple method can be easily adapted to real-life applications. In this paper, we apply our method to do office occupancy detection for studying the COVID-19 transmission pattern from an office desk in a meeting room using the contact information.
PopMAG: Pop Music Accompaniment Generation

In pop music, accompaniments are usually played by multiple instruments (tracks) such as drum, bass, string and guitar, and can make a song more expressive and contagious by arranging together with its melody. Previous works usually generate multiple tracks separately and the music notes from different tracks not explicitly depend on each other, which hurts the harmony modeling. To improve harmony, in this paper, we propose a novel MUlti-track MIDI representation (MuMIDI), which enables simultaneous multi-track generation in a single sequence and explicitly models the dependency of the notes from different tracks. While this greatly improves harmony, unfortunately, it enlarges the sequence length and brings the new challenge of long-term music modeling. We further introduce two new techniques to address this challenge: 1) We model multiple note attributes (e.g., pitch, duration, velocity) of a musical note in one step instead of multiple steps, which can shorten the length of a MuMIDI sequence. 2) We introduce extra long-context as memory to capture long-term dependency in music. We call our system for pop music accompaniment generation as PopMAG. We evaluate PopMAG on multiple datasets (LMD, FreeMidi and CPMD, a private dataset of Chinese pop songs) with both subjective and objective metrics. The results demonstrate the effectiveness of PopMAG for multi-track harmony modeling and long-term context modeling. Specifically, PopMAG wins 42%/38%/40% votes when comparing with ground truth musical pieces on LMD, FreeMidi and CPMD datasets respectively and largely outperforms other state-of-the-art music accompaniment generation models and multi-track MIDI representations in terms of subjective and objective metrics.
Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation

We present SLASH (Sketched LocAlity Sensitive Hashing), an MPI (Message Passing Interface) based distributed system for approximate similarity search over terabyte scale datasets. SLASH provides a multi-node implementation of the popular LSH (locality sensitive hashing) algorithm, which is generally implemented on a single machine. We show how we can append the LSH algorithm with heavy hitters sketches to provably solve the (high) similarity search problem without a single distance computation. Overall, we mathematically show that, under realistic data assumptions, we can identify the near-neighbor of a given query $q$ in sub-linear ($ \ll O(n)$) number of simple sketch aggregation operations only. To make such a system practical, we offer a novel design and sketching solution to reduce the inter-machine communication overheads exponentially. In a direct comparison on comparable hardware, SLASH is more than 10000x faster than the popular LSH package in PySpark. PySpark is a widely-adopted distributed implementation of the LSH algorithm for large datasets and is deployed in commercial platforms. In the end, we show how our system scale to Tera-scale Criteo dataset with more than 4 billion samples. SLASH can index this 2.3 terabyte data over 20 nodes in under an hour, with query times in a fraction of milliseconds. To the best of our knowledge, there is no open-source system that can index and perform a similarity search on Criteo with a commodity cluster.
Exploring Restart Distributions.

We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those corresponding to the agent's past observations, we help exploration by promoting faster state-space coverage via restarting the agent from a more diverse set of initial states, as well as allowing it to restart in states associated with significant past experiences. This approach is compatible with both on-policy and off-policy methods. However, a caveat is that altering the distribution of initial states could change the optimal policies when searching within a restricted class of policies. To reduce this unsought learning bias, we evaluate our approach in deep reinforcement learning which benefits from the high representational capacity of deep neural networks. We instantiate three variants of our approach, each inspired by an idea in the context of experience replay. Using these variants, we show that performance gains can be achieved, especially in hard exploration problems.
Multi-Modal Trajectory Prediction of NBA Players

National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game. As a step towards understanding how players make their decisions, we focus on their movement trajectories during games. We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function that updates the best trajectories. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art. In addition, the results indicate that the approach generates more realistic trajectories and that it can learn individual playing styles of specific players.
Mastering Large Scale Multi-label Image Recognition with high efficiency overCamera trap images

Camera traps are crucial in biodiversity motivated studies, however dealing with large number of images while annotating these data sets is a tedious and time consuming task. To speed up this process, Machine Learning approaches are a reasonable asset. In this article we are proposing an easy, accessible, light-weight, fast and efficient approach based on our winning submission to the "Hakuna Ma-data - Serengeti Wildlife Identification challenge". Our system achieved an Accuracy of 97% and outperformed the human level performance. We show that, given relatively large data sets, it is effective to look at each image only once with little or no augmentation. By utilizing such a simple, yet effective baseline we were able to avoid over-fitting without extensive regularization techniques and to train a top scoring system on a very limited hardware featuring single GPU (1080Ti) despite the large training set (6.7M images and 6TB).
An Annotated Corpus of Webtables for Information Extraction Tasks

Information Extraction is a well-researched area of Natural Language Processing with applications in web search and question answering concerned with identifying entities and relationships between them as expressed in a given context, usually a sentence of a paragraph of running text. Given the importance of the task, several datasets and benchmarks have been curated over the years. However, focusing on running text alone leaves out tables which are common in many structured documents and in which pairs of entities also co-occur in context (e.g., the same row of the table). While there are recent papers on relation extraction from tables in the literature, their experimental evaluations have been on ad-hoc datasets for the lack of a standard benchmark. This paper helps close that gap. We introduce an annotation framework and a dataset of 217,834 tables from Wikipedia which are annotated with 28 relations, using both classifiers and carefully designed queries over a reference knowledge graph. Binary classifiers are then applied to the resulting dataset to remove false positives, resulting in an average annotation accuracy of 94%. The resulting dataset is the first of its kind to be made publicly available.
RTFN: Robust Temporal Feature Network

Time series analysis plays a vital role in various applications, for instance, healthcare, weather prediction, disaster forecast, etc. However, to obtain sufficient shapelets by a feature network is still challenging. To this end, we propose a novel robust temporal feature network (RTFN) that contains temporal feature networks and attentional LSTM networks. The temporal feature networks are built to extract basic features from input data while the attentional LSTM networks are devised to capture complicated shapelets and relationships to enrich features. In experiments, we embed RTFN into supervised structure as a feature extraction network and into unsupervised clustering as an encoder, respectively. The results show that the RTFN-based supervised structure is a winner of 40 out of 85 datasets and the RTFN-based unsupervised clustering performs the best on 4 out of 11 datasets in the UCR2018 archive.
The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation

We provide a necessary and sufficient condition for the uniqueness of penalized least-squares estimators whose penalty term is given by a norm with a polytope unit ball, covering a wide range of methods including SLOPE and LASSO, as well as the related method of basis pursuit. We consider a strong type of uniqueness that is relevant for statistical problems. The uniqueness condition is geometric and involves how the row span of the design matrix intersects the faces of the dual norm unit ball, which for SLOPE is given by the sign permutahedron. Further considerations based this condition also allow to derive results on sparsity and clustering features. In particular, we define the notion of a SLOPE model to describe both sparsity and clustering properties of this method and also provide a geometric characterization of accessible SLOPE models.
A dusty origin for the correlation between protoplanetary disc accretion rates and dust masses

Recent observations have uncovered a correlation between the accretion rates (measured from the UV continuum excess) of protoplanetary discs and their masses inferred from observations of the sub-mm continuum. While viscous evolution models predict such a correlation, the predicted values are in tension with data obtained from the Lupus and Upper Scorpius star forming regions; for example, they underpredict the scatter in accretion rates, particularly in older regions. Here we argue that since the sub-mm observations trace the discs' dust, by explicitly modelling the dust grain growth, evolution, and emission, we can better understand the correlation. We show that for turbulent viscosities with $\alpha \lesssim 10^{-3}$, the depletion of dust from the disc due to radial drift means we can reproduce the range of masses and accretion rates seen in the Lupus and Upper Sco datasets. One consequence of this model is that the upper locus of accretion rates at a given dust mass does not evolve with the age of the region. Moreover, we find that internal photoevaporation is necessary to produce the lowest accretion rates observed. In order to replicate the correct dust masses at the time of disc dispersal, we favour relatively low photoevaporation rates $\lesssim 10^{-9}~M_{\odot}~\mathrm{yr^{-1}}$ for most sources but cannot discriminate between EUV or X-ray driven winds. A limited number of sources, particularly in Lupus, are shown to have higher masses than predicted by our models which may be evidence for variations in the properties of the dust or dust trapping induced in substructures.
Learning Theory: An Approximation Theory Viewpoint: Index

Preface Foreword 1. The framework of learning 2. Basic hypothesis spaces 3. Estimating the sample error 4. Polynomial decay approximation error 5. Estimating covering numbers 6. Logarithmic decay approximation error 7. On the bias-variance problem 8. Regularization 9. Support vector machines for classification 10. General regularized classifiers Bibliography Index.
Bayesian Methods for Ecology: Frontmatter

1. Introduction 2. Critiques of statistical methods 3. Analysing averages and frequencies 4. How good are the models? 5. Regression and correlation 6. Analysis of variance Case studies 7. Mark-recapture analysis 8. Effects of marking frogs 9. Population dynamics 10. Subjective priors 11. Conclusion Appendix A. A tutorial for running WinBUGS Appendix B. Probability distributions Appendix C. MCMC algorithms.
Papillary Squamo-Transitional Cell Carcinoma

Malignancy of cervix continues to be major health burden worldwide and especially in countries with minimal access to screening tests. The average age at diagnosis is 45years. Squamous cell carcinoma is the common histopathologic type, accounting to nearly 90% of all carcinoma cervix cases. The most common variants of SCC are large cell keratinizing SCC, large cell nonkeratinizing SCC. Various less common variants of SCC have been described and include small cell SCC, papillary squamotransitional cell carcinoma (PSTCC), verrucous carcinoma, sarcomatoid squamous carcinoma. Papillary squamo-transitional cell carcinoma can be either well differentiated Papillary variant or poorly differentiated transitional cell variant (181 in devita). Koenig et al. [1] divided papillary squamo-transitional cell carcinoma into 3 groups – predominantly squamous (28.1%), mixed squamous and transitional (50%), and transitional (21.9%). PSTCC are known to be aggressive with increased risk of locoregional recurrences and distant metastasis. They are usually treated like carcinoma cervix. Here we present clinical, pathological and radiological presentation, and treatment of 38 years old premenopausal woman with relevant literature.
Promoting Role of Bacillus Subtilis BS87 on the Growth and Content of Some Natural Products in the Medicinal Plants Anoectochilus Roxburghii and A. Formosanus

Anoectochilus genus is an epiphytic Orchid used as a traditional Chinese folk medicine for the treatment of many diseases. Plant growth-promoting rhizobacteria (PGPR) such as Bacillus subtilis play an important role in promoting medicinal orchids growth. This study aimed to investigate the promoting role of B. subtilis BS87 on the biomass and contents of some natural products in A. roxburghii and A. formosanus such as flavonoids, steroids and essential oils, in A.roxburghii and A. formosanus treated for 90 days with B. subtilis BS87, the flavonoid contents reached 1.208% and 1.424%, respectively, whereas the steroid content in both species was closely values approximately. The essential oil content was 0.11% was 0.21%, respectively, which was significantly different to levels in control plants in pot culture. After 90 days of symbiotic cultivation, increased shoot height, fresh weight and leaf number were observed in treated A. roxburghii and A. formosanus plants compared with control plants. In addition, qRT-PCR showed that the expression levels of seven plant growth-related genes, namely AR017, AR019, AR020, AR023, AR037, AR044, and AR047 were in-creased in both A. roxburghii and A formosanus plants treated with B.subtilisBS8790 days post-inculcation compared with control plants.