Miracle-dz/UniFAR-FTUs huggingface dataset:
HIGHLY EFFICIENT PRODUCTION OF L (+)-LACTIC ACID USING MEDIUM WITH POTATO, CORN STEEP LIQUOR AND CALCIUM CARBONATE BY Lactobacillus rhamnosus ATCC 9595

The objective of this study was to optimize the composition of the fermentation medium through a Central Composite Rotational Design (CCRD) for the production of L (+)latic acid (LA) using Lactobacillus rhamnosus ATCC 9595 and potato, corn steep liquor (CSL) and calcium carbonate as raw materials. A CCRD with three independent variables (potato flour (PF), CSL and CaCO3 concentrations) was performed for a total of 17 treatments. Kinetic studies were conducted, and samples were taken at time intervals of 0 to 72 h. Cell count, pH, reducing sugars and LA concentrations were determined. LA productivity and yield, sugar consumption and cell growth factor were calculated. The highest concentration of LA was 183.8 g/L, with 2.553 g/L.h productivity, 94.2% yield and 96.1 % sugar consumption.
Convolutional Neural Networks Towards Arduino Navigation of Indoor Environments

In this paper we propose a number of tested ways in which a low-budget demo car could be made to navigate an indoor environment. Canny Edge Detection, Supervised Floor Detection and Imitation Learning were used separately and are contrasted in their effectiveness. The equipment used in this paper approximated an autonomous robot configured to work with a mobile device for image processing. This paper does not provide definitive solutions and simply illustrates the approaches taken to successfully achieve autonomous navigation of indoor environments. The successes and failures of all approaches were recorded and elaborated on to give the reader an insight into the construction of such an autonomous robot.
Machine Learning in Finance : The Case of Deep Learning for Option Pricing

Modern advancements in mathematical analysis, computational hardware and software, and availability of big data have made possible commoditized machines that can learn to operate as investment managers, financial analysts, and traders. We briefly survey how and why AI and deep learning can influence the field of Finance in a very general way. Revisiting original work from the 1990s, we summarize a framework within which machine learning may be used for finance, with specific application to option pricing. We train a fully-connected feed-forward deep learning neural network to reproduce the Black and Scholes (1973) option pricing formula to a high degree of accuracy. We also offer a brief introduction to neural networks and some detail on the various choices of hyper-parameters that make the model as accurate as possible. This exercise suggests that deep learning nets may be used to learn option pricing models from the markets, and could be trained to mimic option pricing traders who specialize in a single stock or index. 1 Artificial Intelligence: A Reincarnation Artificial intelligence (AI) is in its second new age. While the notion that machines are capable of exhibiting human levels of intelligence saw its beginning implementations in the 1950s, success in creating AI machines was thin, and mostly, AI was deemed to be a failed enterprise. In the past few years, there has been a resurgence of AI, ∗Robbie Culkin is an undergraduate Computer Science and Engineering student, and Sanjiv Das is Professor of Finance and Data Science. They may be reached at rculkin@scu.edu, srdas@scu.edu.
Self-adaptive single frequency laser assisted by distributed feedbacks.

High-coherence light sources with an ultra-narrow linewidth are of considerable research interests in numerous fields, such as laser interferometer gravitational-wave observatory, laser radio, etc. Herein, commencing from the in-depth analysis for consumption and replenishment law of the carriers in the gain-band under excitation of the feedback signal, a compression idea to compress extremely laser linewidth assisted by distributed feedback has been proposed. Consequentially, a novel laser configuration is demonstrated. A cavity mode signal matching with the output wavelength is fed back into the main cavity by the distributed feedback structure to provide an excitation signal required for laser gain. Moreover, the corresponding experimental investigation is conducted based on an on-chip laser system with distributed feedback. Eventually, an ultra-narrow linewidth laser with a side mode suppuration ratio of 80 dB, a linewidth of 10 Hz, a Lorentzian linewidth of sub Hz, and a relative intensity noise (RIN) of -150 dB/Hz is obtained under normal conditions. The proposed idea is valid in any other gain-types lasers with diverse wavelengths, which also provides a new perspective for other laser parameters for extreme modulation.
Detectability of primordial black hole binaries at high redshift

We show that the gravitational wave signals from primordial black hole (PBH) binaries at high redshift can be detected. The detectability of PBH binaries is enhanced by redshift bias and more PBH binaries at high redshift. The initial clustering of PBHs is also included and enhances the effectively detectable mass ranges of PBHs at high redshift. Future observations on the gravitational wave at high redshift by space-based detectors such as LISA and SKA can constrain the fraction of PBHs in dark matter and PBHs initial distribution.
Offline Learning from Demonstrations and Unlabeled Experience

Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. However, BC does not effectively leverage what we will refer to as unlabeled experience: data of mixed and unknown quality without reward annotations. This unlabeled data can be generated by a variety of sources such as human teleoperation, scripted policies and other agents on the same robot. Towards data-driven offline robot learning that can use this unlabeled experience, we introduce Offline Reinforced Imitation Learning (ORIL). ORIL first learns a reward function by contrasting observations from demonstrator and unlabeled trajectories, then annotates all data with the learned reward, and finally trains an agent via offline reinforcement learning. Across a diverse set of continuous control and simulated robotic manipulation tasks, we show that ORIL consistently outperforms comparable BC agents by effectively leveraging unlabeled experience.
Axion electrodynamics in topological materials

One of the intriguing properties characteristic to three-dimensional topological materials is the topological magnetoelectric phenomena arising from a topological term called the $\theta$ term. Such magnetoelectric phenomena are often termed the axion electrodynamics, since the $\theta$ term has exactly the same form as the action describing the coupling between a hypothetical elementary particle, axion, and a photon. The axion was proposed about forty years ago to solve the so-called strong CP problem in quantum chromodynamics, and is now considered as a candidate for dark matter. In this tutorial, we overview theoretical and experimental studies on the axion electrodynamics in three-dimensional topological materials. Starting from the topological magnetoelectric effect in three-dimensional time-reversal invariant topological insulators, we describe the basic properties of static and dynamical axion insulators whose realizations require magnetic orderings. We also discuss the electromagnetic responses of Weyl semimetals with a focus on the chiral anomaly. We extend the concept of the axion electrodynamics in condensed matter to topological superconductors, whose responses to external fields can be described by a gravitational topological term analogous to the $\theta$ term.
Group-level Fairness Maximization in Online Bipartite Matching

In typical online matching problems, the goal is to maximize the number of matches. This paper studies online bipartite matching from the perspective of group-level fairness, and the goal is to balance the number of matches made across different groups of online nodes. We assume that an online node's group belongings are revealed upon arrival, and measure performance based on the group with the smallest fraction of its nodes matched at the end. We distinguish between two different objectives: long-run fairness, where the algorithm is audited for its fairness over many realizations from the same instance; and short-run fairness, where the algorithm is audited for its fairness on a single realization. We focus on the known-IID model of online arrivals and analyze, under both objectives, the competitive ratio for two classes of algorithms: non-rejecting algorithms, which must match an online node as long as a neighbor is available; and general online algorithms, which are allowed to reject online nodes to improve fairness. For long-run fairness, we analyze two online algorithms (sampling and pooling) which establish asymptotic optimality across many different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand). By contrast, outside all of these regimes, we show that the competitive ratio for online algorithms is between 0.632 and 0.732. For short-run fairness, we focus on the case of a complete bipartite graph and show that the competitive ratio for online algorithms is between 0.863 and 0.942; we also derive a probabilistic rejection algorithm which is asymptotically optimal as total demand increases.
Braid: Weaving Symbolic and Statistical Knowledge into Coherent Logical Explanations

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching/uniﬁcation of logical terms, an inability to deal with uncertainty, and the need for a pre-compiled rule-base of knowledge (the “knowl-edge acquisition” problem). These issues are particularly severe for the Natural Language Understanding (NLU) task, where we often use implicit background knowledge to understand and reason about text, resort to imperfect/fuzzy alignment of concepts and relations during reasoning, and constantly deal with ambiguity in representations. To address these issues, we devise a novel FOL-based reasoner, called Braid, that supports probabilistic rules, and uses the notion of custom uniﬁcation functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid-BC (the backchaining component of Braid), and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query in a highly scalable manner. We use a simple QA example from a children’s story to motivate Braid-BC’s design and explain how the various components work together to produce a coherent logical explanation.
To Appear In Evolution in Action : Past , Present and Future

In the last decade or so we have seen tremendous progress in Artificial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling, i.e. machine learning of statistical models that make it possible to predict what the right decision might be in future situations. For example, we now have object recognition, speech recognition, game playing, language understanding, and machine translation systems that rival human performance, and in many cases exceed it (Hassan Awadalla et al. 2018; Hessel et al. 2017; Russakovsky et al. 2014). In each of these cases, massive amounts of supervised data exists, specifying the right answer to each input case. With the massive amounts of computation that is now available, it is possible to train neural networks to take advantage of the data. Therefore, AI works great in tasks where we already know what needs to be done.
A Robust Abstractive System for Cross-Lingual Summarization

We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank

We provide an explicit analysis of the dynamics of vanilla gradient descent for deep matrix factorization in a setting where the minimizer of the loss function is unique. We show that the recovery rate of ground-truth eigenvectors is proportional to the magnitude of the corresponding eigenvalues and that the differences among the rates are amplified as the depth of the factorization increases. For exactly characterized time intervals, the effective rank of gradient descent iterates is provably close to the effective rank of a low-rank projection of the ground-truth matrix, such that early stopping of gradient descent produces regularized solutions that may be used for denoising, for instance. In particular, apart from few initial steps of the iterations, the effective rank of our matrix is monotonically increasing, suggesting that "matrix factorization implicitly enforces gradient descent to take a route in which the effective rank is monotone". Since empirical observations in more general scenarios such as matrix sensing show a similar phenomenon, we believe that our theoretical results shed some light on the still mysterious "implicit bias" of gradient descent in deep learning.
A Measure of Smoothness in Synthesized Speech

The articulators typically move smoothly during speech production. Therefore, speech features of natural speech are generally smooth. However, over-smoothness causes "muffleness" and, hence, reduction in ability to identify emotions/expressions/styles in synthesized speech that can affect the perception of naturalness in synthesized speech. In the literature, statistical variances of static spectral features have been used as a measure of smoothness in synthesized speech but they are not sufficient enough. This paper proposes another measure of smoothness that can be efficiently applied to evaluate the smoothness of synthesized speech. Experiments showed that the proposed measure is reliable and efficient to measure the smoothness of different kinds of synthesized speech.
A Fokker–Planck control framework for stochastic systems

A new framework for the optimal control of probability density functions (PDF) of stochastic processes is reviewed. This framework is based on Fokker-Planck (FP) partial differential equations that govern the time evolution of the PDF of stochastic systems and on control objectives that may require to follow a given PDF trajectory or to minimize an expectation functional. Corresponding to different stochastic processes, different FP equations are obtained. In particular, FP equations of parabolic, fractional parabolic, integro parabolic, and hyperbolic type are discussed. The corresponding optimization problems are deterministic and can be formulated in an open-loop framework and within a closed-loop model predictive control strategy. The connection between the Dynamic Programming scheme given by the Hamilton-Jacobi-Bellman equation and the FP control framework is discussed. Under appropriate assumptions, it is shown that the two strategies are equivalent. Some applications of the FP control framework to different models are discussed and its extension in a mean-field framework is elucidated. This is a preprint of the paper Mario Annunziato and Alfio Borzì A Fokker–Planck control framework for stochastic systems EMS Surveys In Mathematical Sciences, 5 (2018), 65 98. (DOI: 10.4171/EMSS/27)
Can Temporal Information Help with Contrastive Self-Supervised Learning?

Leveraging temporal information has been regarded as essential for developing video understanding models. However, how to properly incorporate temporal information into the recent successful instance discrimination based contrastive self-supervised learning (CSL) framework remains unclear. As an intuitive solution, we find that directly applying temporal augmentations does not help, or even impair video CSL in general. This counter-intuitive observation motivates us to re-design existing video CSL frameworks, for better integration of temporal knowledge. To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL. Specifically, TaCo selects a set of temporal transformations not only as strong data augmentation but also to constitute extra self-supervision for video understanding. By jointly contrasting instances with enriched temporal transformations and learning these transformations as self-supervised signals, TaCo can significantly enhance unsupervised video representation learning. For instance, TaCo demonstrates consistent improvement in downstream classification tasks over a list of backbones and CSL approaches. Our best model achieves 85.1% (UCF-101) and 51.6% (HMDB-51) top-1 accuracy, which is a 3% and 2.4% relative improvement over the previous state-of-the-art.
Evidence for interaction between the 2 a polymerase protein and the 3 a movement protein of Cucumber mosaic virus

Received 27 April 2005 Accepted 18 August 2005 The genome of Cucumber mosaic virus consists of three single-stranded RNA molecules, RNAs 1, 2 and 3. RNAs 1 and 2 encode the 1a and 2a proteins, respectively, which are necessary for replication of the viral genome and have been implicated in movement of the viral RNAs in plants. The 3a movement protein (MP), encoded by RNA 3, is essential for transferring the RNA genomes from infected cells to adjacent cells across the plasmodesmata. Far-Western analysis demonstrated that bacterially expressed 2a polymerase protein directly interacted with the MP. Interaction was confirmed in a yeast two-hybrid assay, and co-immunoprecipitation analysis showed that the MP interacted only with the 2a polymerase protein. A yeast three-hybrid assay showed that the 1a–2a protein interaction relevant for replicase complex formation was not affected by theMP. Although theMP has no affinity for the 1a protein, it interacted indirectly with the 1a protein via the 2a polymerase protein. These results suggest that the replicase complex may be involved in movement through its interaction with the MP.
Towards Developing a Model to Handle Multiparty Conversations for Healthcare Agents

Virtual agents are playing a key role in several application domains for learning, gaming, healthcare. Social virtual agents are being used in different healthcare applications to provide assistance, coaching, explanation. Most existing applications rely on dyadic interaction. However multiparty conversation where each agent can be designed with a specific role has been shown to increase the passing of messages. One of the most common aspect of human-agent interaction is turn-taking, where the participants take turns to speak and allow for a smooth exchange. Several nonverbal signals and lexical and semantic information is used to facilitate this. Several studies have provided models to handle turn-taking in conversations involving three or more participants. The Council of Coaches project aims to provide virtual coaching to users by making use of multiple healthcare agents with various domain expertise. To achieve this, we aim to develop a model that will be able to handle multi-conversation and adapt the turn strategy depending on the group size and the agent role dynamically. This paper presents an overview of the existing literature and the research questions we intend to address.
New Phase Transition of Lanthanum at High Pressure

Lanthanum (La), the first member of the rare-earth elements, recently aroused strong interest due to its unique superhydride with superconducting properties. Although there is much theoretical and experimental work about phase transitions and superconductivity in metallic La, we got a new body-centred tetragonal ($bct$) phase in metallic La with space group $I4/mmm$ at $190$ GPa exchanging from face-centered cubic ($fcc$) phase in previous work, which expanded the phase transition sequence. The $bct$ phase shows an abnormal packing way that turned to non-closed packing at high pressure. And more detailed properties of the new phase are discussed.
Integrating Selective Herbicide and Native Plant Restoration to Control Alternanthera philoxeroides (Alligator Weed)

Exotic invasive aquatic weeds such as alligator weed (Alternanthera philoxeroides) threaten native ecosystems by interfering with native plant communities, disrupting hydrology, and diminishing water quality. Development of new tools to combat invaders is important for the well being of these sensitive areas. Integrated pest management offers managers an approach that combines multiple control methods for better control than any one method used exclusively. In a greenhouse and field study, we tested the effects of selective herbicide application frequency, native competitor plant introduction, and their integration on alligator weed. In the greenhouse study, alligator weed shoot, root, and total biomass were reduced with one herbicide application, and further reduced with two. Alligator weed cumulative stem length and shoot/root ratio was only reduced after two applications. In the greenhouse, introduction of competitors did not affect alligator weed biomass, but did affect shoot/root ratio. The interaction of competitor introduction and herbicide did not significantly influence alligator weed growth in the greenhouse study. In the field, alligator weed cover was reduced after one herbicide application, but not significantly more after a second. Introduction of competitor species had no effect on alligator weed cover, nor did the interaction of competitor species and herbicide application. This study demonstrates that triclopyr amine herbicide can reduce alligator weed biomass and cover, and that two applications are more effective than one. To integrate selective herbicides and native plant introduction successfully for alligator weed control, more research is needed on the influence competition can potentially have on alligator weed growth, and the timing of herbicide application and subsequent introduction of plants.
A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems

Using reviews to learn user and item representations is important for recommender system. Current review based methods can be divided into two categories: (1) the Convolution Neural Network (CNN) based models that extract n-gram features from user/item reviews; (2) the Recurrent Neural Network (RNN) based models that learn global contextual representations from reviews for users and items. Despite their success, both CNN and RNN based models in previous studies suffer from their own drawbacks. While CNN based models are weak in modeling long-dependency relation in text, RNN based models are slow in training and inference due to their incapability with parallel computing. To alleviate these problems, we propose a new text encoder module for review modeling in recommendation by combining convolution networks with self-attention networks to model local and global interactions in text together.As different words, sentences, reviews have different importance for modeling user and item representations, we construct review models hierarchically in sentence-level, review-level, and user/item level by encoding words for sentences, encoding sentences for reviews, and encoding reviews for user and item representations. Experiments on Amazon Product Benchmark show that our model can achieve significant better performance comparing to the state-of-the-art review based recommendation models.