Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluorometric Determination of DNA in Aquatic Microorganisms by Use of Hoechst 33258

A method for the determination of microbial DNA in aquatic environments by the use of Hoechst 33258 has been developed. With unsophisticated instrumentation and simple extraction procedures, it is possible to detect from 0.05 to 10 μg of DNA in bacterial cultures or natural water samples. The method is specific for DNA; DNase I treatment of extracts of natural microbial populations removed 95 to 100% of the observed fluorescence. DNA content ranged from 165 ng ml−1 for relatively eutrophic Potomac River water to 27 ng ml−1 for coastal Atlantic Ocean water and was correlated to an acridine orange direct count (r = 0.90).
Electronically Tunable Microwave Bandpass Filters

Combline filters with novel input and output coupling networks which enable broad-band tuning to be achieved with minimum degradation in passband performance are discussed. Explicit design formulas for these filters are presented. Computer analysis of varactor tuned combine bandpass filters including the small signal varactor equivalent circuit is presented enabling filter performance to be easily evaluated. The design and experimental performance of a varactor tuned combline filter, realized in suspended substrate stripline is described. This filter toned from 3.2 GHz to 4.9 GHz exhibited low passband insertion loss and its performance was is close agreement with theoretical expectations.
Rotational Spectra of Vibrationally Excited AlO and TiO in Oxygen-rich Stars

Rotational transitions in vibrationally excited AlO and TiO—two possible precursors of dust—were observed in the 300 GHz range (1 mm wavelength) toward the oxygen-rich asymptotic giant branch (AGB) stars R Dor and IK Tau with ALMA, and vibrationally excited AlO was observed toward the red supergiant VY CMa with the SMA. The J = 11 → 10 transition of TiO in the v = 1 and 2 levels and the N = 9 → 8 transition in the v = 2 level of AlO were identified toward R Dor, the J = 11 → 10 line of TiO was identified in the v = 1 level toward IK Tau, and two transitions in the v = 1 and 2 levels of AlO were identified toward VY CMa. The newly derived high vibrational temperature of TiO and AlO in R Dor of 1800 ± 200 K and prior measurements of the angular extent confirm that the majority of the emission is from a region within ≲2R⋆ of the central star. A full radiative transfer analysis of AlO in R Dor yielded a fractional abundance of ∼3% of the solar abundance of Al. From a similar analysis of TiO a fractional abundance of ∼78% of the solar abundance of Ti was found. The observations provide indirect evidence that TiO is present in a rotating disk close to the star. Further observations in the ground and excited vibrational levels are needed to determine whether AlO, TiO, and TiO2 are seeds of the Al2O3 dust in R Dor, and perhaps in the gravitationally bound dust shells in other AGB stars with low mass-loss rates.
Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment

Deep Reservoir Computing has emerged as a new paradigm for deep learning, which is based around the reservoir computing principle of maintaining random pools of neurons combined with hierarchical deep learning. The reservoir paradigm reflects and respects the high degree of recurrence in biological brains, and the role that neuronal dynamics play in learning. However, one issue hampering deep reservoir network development is that one cannot backpropagate through the reservoir layers. Recent deep reservoir architectures do not learn hidden or hierarchical representations in the same manner as deep artificial neural networks, but rather concatenate all hidden reservoirs together to perform traditional regression. Here we present a novel Deep Reservoir Network for time series prediction and classification that learns through the non-differentiable hidden reservoir layers using a biologically-inspired backpropagation alternative called Direct Feedback Alignment, which resembles global dopamine signal broadcasting in the brain. We demonstrate its efficacy on two real world multidimensional time series datasets.
Improving Text Generation with Student-Forcing Optimal Transport

Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, however, the model is instead conditioned on previously generated tokens, resulting in what is termed exposure bias. To reduce this gap between training and testing, we propose using optimal transport (OT) to match the sequences generated in these two modes. An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences. The effectiveness of the proposed method is validated on machine translation, text summarization, and text generation tasks.
Learning Tensor Train Representation with Automatic Rank Determination from Incomplete Noisy Data

Tensor train (TT) decomposition, a powerful tool for analysing multidimensional data, exhibits superior performance in many signal processing and machine learning tasks. However, existing methods for TT decomposition either require the knowledge of the true TT ranks, or extensive fine-tuning the balance between model complexity and representation accuracy. In this paper, a fully Bayesian treatment of TT decomposition is employed to enable automatic rank determination. In particular, theoretical evidence is established for adopting a Gaussian-product-Gamma prior to induce sparsity on the slices of the TT cores, so that the model complexity is automatically determined even under incomplete and noisy observed data. Based on the proposed probabilistic model, an efficient learning algorithm is derived under the variational inference framework. Simulation results on synthetic data show the success of the proposed model and algorithm on recovering the ground-truth TT structure from incomplete noisy data. Further experiments on real world data demonstrate the proposed algorithm performs better in image completion and image classification, compared to other existing TT decomposition algorithms.
Controllable Pareto Multi-Task Learning

A multi-task learning (MTL) system aims at solving multiple related tasks at the same time. With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together. Multiple models with different preferences over tasks have to be trained and stored for many real-world applications where the trade-off has to be made online. This work proposes a novel controllable Pareto multi-task learning framework, to enable the system to make real-time trade-off switch among different tasks with a single model. To be specific, we formulate the MTL as a preference-conditioned multiobjective optimization problem, for which there is a parametric mapping from the preferences to the optimal Pareto solutions. A single hypernetwork-based multi-task neural network is built to learn all tasks with different trade-off preferences among them, where the hypernetwork generates the model parameters conditioned on the preference. At the inference time, MTL practitioners can easily control the model performance based on different trade-off preferences in real-time. Experiments on different applications demonstrate that the proposed model is efficient for solving various multi-task learning problems.
Generic aspects of the resource theory of coherence

The class of incoherent operations induces a pre-order on the set of quantum pure states, defined by the possibility of converting one state into the other by transformations within the class. We prove that if two $n$-dimensional pure states are chosen independently according to the natural uniform distribution, then the probability that they are comparable vanishes as $n$ increases. We also study the maximal success probability of incoherent conversions and find an explicit formula for its large-$n$ asymptotic distribution. Our analysis is based on the observation that the extreme values (largest and smallest components) of a random point uniformly sampled from the unit simplex are distributed asymptotically as certain explicit homogeneous Markov chains.
Massive MIMO for Cellular-Connected UAV: Challenges and Promising Solutions

Massive multiple-input multiple-output (MIMO) is a promising technology for enabling cellu-lar-connected unmanned aerial vehicle (UAV) communications in the future. Equipped with full-dimensional large arrays, ground base stations (GBSs) can apply adaptive fine-grained 3D beamforming to mitigate the strong interference between high-altitude UAVs and low-altitude terrestrial users, thus significantly enhancing the network spectral efficiency. However, the performance gain of massive MIMO critically depends on accurate channel state information of both UAVs and terrestrial users at the GBSs, which is practically difficult to achieve due to UAV-in-duced pilot contamination and UAVs' high mobility in 3D. Moreover, the increasingly popular applications relying on a large group of coordinated UAVs or UAV swarm as well as the practical hybrid GBS beamforming architecture for massive MIMO further complicate the pilot contamination and channel/beam tracking problems. In this article, we provide an overview of the above challenging issues, propose new solutions to cope with them, and discuss promising directions for future research. Preliminary simulation results are also provided to validate the effectiveness of the proposed solutions.
Zero-shot Entity Linking with Efficient Long Range Sequence Modeling

This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we find a simple yet effective way is to expand the long-range sequence modeling. Unlike many previous methods, our method does not require expensive pre-training of BERT with long position embeddings. Instead, we propose an efficient position embeddings initialization method called Embedding-repeat, which initializes larger position embeddings based on BERT-Base. On the zero-shot entity linking dataset, our method improves the STOA from 76.06% to 79.08%, and for its long data, the corresponding improvement is from 74.57% to 82.14%. Our experiments suggest the effectiveness of long-range sequence modeling without retraining the BERT model.
Asset Price Forecasting using Recurrent Neural Networks

This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices of two other stocks that lie in rather close correlation with GS. Other models such as ARIMA were used as benchmark. Empirical results manifest the practical challenges when using LSTM for forecasting stocks. One of the main upheavals was a recurring lag which we called "forecasting lag". The second purpose is to develop a more general and objective perspective on the task of time series forecasting so that it could be applied to assist in an arbitrary that of forecasting by ANNs. Thus, attempts are made for distinguishing previous works by certain criteria so as to summarise those including effective information. The summarised information is then unified and expressed through a common terminology that can be applied to different steps of a time series forecasting task. The last but not least purpose of this thesis is to elaborate on a mathematical framework on which ANNs are based. We are going to use the framework introduced in the book "Neural Networks in Mathematical Framework" by Anthony L. Caterini in which the structure of a generic neural network is introduced and the gradient descent algorithm (which incorporates backpropagation) is introduced in terms of their described framework. In the end, we use this framework for a specific architecture, which is recurrent neural networks on which we concentrated and our implementations are based. The book proves its theorems mostly for classification case. Instead, we proved theorems for regression case, which is the case of our problem.
Laminations and 2-filling rays on infinite type surfaces.

The loop graph of an infinite type surface is an infinite diameter hyperbolic graph first studied in detail by Juliette Bavard. An important open problem in the study of infinite type surfaces is to describe the boundary of the loop graph as a space of geodesic laminations. We approach this problem by constructing the first examples of 2-filling rays on infinite type surfaces. Such rays accumulate onto geodesic laminations which are in some sense filling, but without strong enough properties to correspond to points in the boundary of the loop graph. We give multiple constructions using both a hands-on combinatorial approach and an approach using train tracks and automorphisms of flat surfaces. In addition, our approaches are sufficiently robust to describe all 2-filling rays with certain other basic properties as well as to produce uncountably many distinct mapping class group orbits.
RackSched: A Microsecond-Scale Scheduler for Rack-Scale Computers (Technical Report)

Low-latency online services have strict Service Level Objectives (SLOs) that require datacenter systems to support high throughput at microsecond-scale tail latency. Dataplane operating systems have been designed to scale up multi-core servers with minimal overhead for such SLOs. However, as application demands continue to increase, scaling up is not enough, and serving larger demands requires these systems to scale out to multiple servers in a rack. We present RackSched, the first rack-level microsecond-scale scheduler that provides the abstraction of a rack-scale computer (i.e., a huge server with hundreds to thousands of cores) to an external service with network-system co-design. The core of RackSched is a two-layer scheduling framework that integrates inter-server scheduling in the top-of-rack (ToR) switch with intra-server scheduling in each server. We use a combination of analytical results and simulations to show that it provides near-optimal performance as centralized scheduling policies, and is robust for both low-dispersion and high-dispersion workloads. We design a custom switch data plane for the inter-server scheduler, which realizes power-of-k-choices, ensures request affinity, and tracks server loads accurately and efficiently. We implement a RackSched prototype on a cluster of commodity servers connected by a Barefoot Tofino switch. End-to-end experiments on a twelve-server testbed show that RackSched improves the throughput by up to 1.44x, and scales out the throughput near linearly, while maintaining the same tail latency as one server until the system is saturated.
Heavy light inflaton and dark matter production

We study the minimal extension of the SM by a scalar with quartic interaction serving as an inflaton. For the model where scale symmetry is broken only in the inflaton sector, the mass of the inflaton is constrained to be relatively low. Here, we analysed the previously omitted situation of the inflaton masses $m_{\chi}\gtrsim 250\text{ GeV}$. Therefore, we provide a window of inflaton masses with viable inflationary properties that evade direct observational constraints, due to their small mixing with the Higgs sector. The addition of heavy neutral leptons with Majorana masses induced by the interaction with the inflaton allow for Cold Dark Matter in the model with masses $O(1-10)\text{ MeV}$.
Ultrasound-guided immature follicle puncture for clomiphene resistent polycystic ovarian syndrome

Objective To investigate the effect of ultrasound-guided immature follicle puncture technique(IMFP) on the endocrinosity,basic follicles number,and ovarian volumes in patients with clomiphene resistant polycystic ovarian syndrome(PCOS),and compare the rates of ovaian hyperstimulation syndrome(OHSS) and pregnancy after the therapy.Methods Sixty-seven patients with clomiphene resistant PCOS from Sep 2008 to Sep 2009 were divided into observe group(n = 32) and control group(n = 35). After applying little amount of HMG to the patients in observe group,IMFP was performed.The changes of the endocrinosity,basic follicles number,and ovarian volumes were observed after IMFP.Patients who reached the effective standard would receive gonadotropin,and then the rates of OHSS and pregnancy were observed.Results After 1-3 times of treatment,the levels of testosterone and LH/FSH ratio became normal in all the patients in observe group.The ovarian volumes were normal in 24 cases(75%),the basic follicle number decreased to less than 10 per ovary in 27 cases(84.4%). Patients in observe group were arranged for sexual intercourse or intra-uterine insemination after being administered with CC or HMG.There were 14 patients(43.8%) in observe group acquired pregnancy and 2 patients(6.3%) developed moderate OHSS 6 months after the last IMFP.There were significant differences in the rates of pregnancy and OHSS between the two groups.Conclusion After receiving IMFP,the ovarian volumes and basic follicle number were decreased,the testosterone level was improved,the rate of OHSS was declined,and the rate of pregnancy was raised in patients with clomiphene resistant PCOS.
MeetCues: Supporting Online Meetings Experience

The remote work ecosystem is transforming patterns of communication between teams and individuals located at distance. Particularly, the absence of certain subtle cues in current communication tools may hinder an online’s meeting outcome by negatively impacting attendees’ overall experience and, often, make them feeling disconnected. The problem here might be due to the fact that current tools fall short in capturing it. To partly address this, we developed an online platform—MeetCues— with the aim of supporting online communication during meetings. MeetCues is a companion platform for a commercial communication tool with interactive and visual UI features that support back-channels of communications. It allows attendees to be more engaged during a meeting, and reflect in real-time or post-meeting. We evaluated our platform in a diverse set of five, real-world corporate meetings, and we found that, not only people were more engaged and aware during their meetings, but they also felt more connected. These findings suggest promise in the design of new communications tools, and reinforce the role of InfoVis in augmenting and enriching online meetings.
A model‐free approach for testing association

The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis and inflammation, predictive of recurrence after lung resection in node‐negative non‐small cell lung cancer patients with tumour stage T2a or less. We propose an omnibus approach for testing the association that is free of assumptions on functional forms and distributions and can be used as a general method. This proposed maximal permutation test is based on the idea of thresholding, is readily implementable and is computationally efficient. We demonstrate that the proposed omnibus tests maintain their levels and have strong power for detecting linear, nonlinear and quantile‐based associations, even with outlier‐prone and heavy‐tailed error distributions and under nonparametric setting. We additionally illustrate the use of this approach in model‐free feature screening and further examine the level and power of these tests for binary outcome. We compare the performance of the proposed omnibus tests with comparator methods in our motivating application to identify the preoperative serum biomarkers associated with non‐small cell lung cancer recurrence in early stage patients.
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization

Autoencoder-based hybrid recommender systems have become popular recently because of their ability to learn user and item representations by reconstructing various information sources, including users' feedback on items (e.g., ratings) and side information of users and items (e.g., users' occupation and items' title). However, existing systems still use representations learned by matrix factorization (MF) to predict the rating, while using representations learned by neural networks as the regularizer. In this paper, we define the neural representation for prediction (NRP) framework and apply it to the autoencoder-based recommendation systems. We theoretically analyze how our objective function is related to the previous MF and autoencoder-based methods and explain what it means to use neural representations as the regularizer. We also apply the NRP framework to a direct neural network structure which predicts the ratings without reconstructing the user and item information. We conduct extensive experiments which confirm that neural representations are better for prediction than regularization and show that the NRP framework outperforms the state-of-the-art methods in the prediction task, with less training time and memory.
Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data

Modern High-Performance Computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations. They employ models that ingest time-series monitoring sensor data and transform it into actionable knowledge for system tuning: a process known as Operational Data Analytics (ODA). However, monitoring data has a high dimensionality, is hardware-dependent and difficult to interpret. This, coupled with the strict requirements of ODA, makes most traditional data mining methods impractical and in turn renders this type of data cumbersome to process. Most current ODA solutions use ad-hoc processing methods that are not generic, are sensible to the sensors’ features and are not fit for visualization. In this paper we propose a novel method, called Correlation-wise Smoothing (CS), to extract descriptive signatures from time-series monitoring data in a generic and lightweight way. Our CS method exploits correlations between data dimensions to form groups and produces image-like signatures that can be easily manipulated, visualized and compared. We evaluate the CS method on HPC-ODA, a collection of datasets that we release with this work, and show that it leads to the same performance as most state-of-the-art methods while producing signatures that are up to ten times smaller and up to ten times faster, while gaining visualizability, portability across systems and clear scaling properties.
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM). In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information. Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly. The experiments performed on the CCGbank demonstrate that our approach outperforms all previous studies in terms of both supertagging and parsing. Further analyses illustrate the effectiveness of each component in our approach to discriminatively learn from word pairs to enhance CCG supertagging.