Miracle-dz/UniFAR-FTUs huggingface dataset:
COMMERCIAL DEVELOPMENTS IN MICROALGAL BIOTECHNOLOGY

A number of important advances have occurred in microalgal biotechnology in recent years that are slowly moving the field into new areas. New products are being developed for use in the mass commercial markets as opposed to the “health food” markets. These include algal‐derived long‐chained polyunsaturated fatty acids, mainly docosahexaenoic acid, for use as supplements in human nutrition and animals. Large‐scale production of algal fatty acids is possible through the use of heterotrophic algae and the adaptation of classical fermentation systems providing consistent biomass under highly controlled conditions that result in a very high quality product. New products have also been developed for use in the development of pharmaceutical and research products. These include stable‐isotope biochemicals produced by algae in closed‐system photobioreactors and extremely bright fluorescent pigments. Cryopreservation has also had a tremendous impact on the ability of strains to be maintained for long periods of time at low cost and maintenance while preserving genetic stability.
LibriMix: An Open-Source Dataset for Generalizable Speech Separation

In recent years, wsj0-2mix has become the reference dataset for single-channel speech separation. Most deep learning-based speech separation models today are benchmarked on it. However, recent studies have shown important performance drops when models trained on wsj0-2mix are evaluated on other, similar datasets. To address this generalization issue, we created LibriMix, an open-source alternative to wsj0-2mix, and to its noisy extension, WHAM! Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM! Using Conv-TasNet, we achieve competitive performance on all LibriMix versions. In order to fairly evaluate across datasets, we introduce a third test set based on VCTK for speech and WHAM! for noise. Our experiments show that the generalization error is smaller for models trained with LibriMix than with WHAM! , in both clean and noisy conditions. Aiming towards evaluation in more realistic, conversation-like scenarios, we also release a sparsely overlapping version of LibriMix's test set.
AC Loop Current Attacks Against The KLJN Secure Key Exchange Scheme

Recently, several passive and active attack methods have been proposed against the Kirchhoff–Law–Johnson–Noise (KLJN) secure key exchange scheme by utilizing direct (DC) loop currents. The DC current attacks are relatively easy, but their practical importance is low. On the other hand, parasitic alternating (AC) currents are virtually omnipresent in wire-based systems. Such situations exist due to AC ground loops and electromagnetic interference (EMI). However, utilizing AC currents for attacks is a harder problem. Here, we introduce and demonstrate AC current attacks in various frequency ranges. The attacks exploit a parasitic/periodic AC voltage-source at either Alice’s or Bob’s end. In the low-frequency case, the procedure is the generalized form of the former DC ground-loop-based attack. In the high-frequency case, the power density spectrum of the wire voltage is utilized. The attack is demonstrated in both the low and the high-frequency situations. Defense protocols against the attack are also discussed.
GeoCoV19

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this largescale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.
Succinct Trit-array Trie for Scalable Trajectory Similarity Search

Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Locality sensitive hashing (LSH) is a powerful technique for fast similarity searches. Recent methods employ LSH and attempt to realize an efficient similarity search of trajectories; however, those methods are inefficient in terms of search time and memory when applied to massive datasets. To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie. We also present two novel techniques that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is a space-efficient representation that leverages the idea behind succinct data structures (i.e., a compressed data structure supporting fast data operations). We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly in comparison to state-of-the-art similarity search methods.
Vision: Biophysics and Biochemistry of the Retinal Photoreceptors.

Professor Wolken has taken this opportunity to summarize his own research interests, supplemented by enough additional material to make a coherent story. The book deals with vertebrate photoreceptor biochemistry and its relation to spectral sensitivity of the retina. A second section considers photoreceptors and photosensitivity of invertebrate forms. This is followed by a biophysical analysis of the vertebrate rod, and finally a general summary is made. Interspersed in the book are rather too detailed expositions of techniques and apparatus used. Unfortunately, the book bears the signs of hasty preparation: many grammatical and typographical errors, and unclear passages. On page 35, for example, in a discussion of the breakdown of rhodopsin, we are told that "the direction of the equilibrium is to favor the production of vitamin A. " The phenomenon in question is a steady state, not an equilibrium, and cannot have direction. What is meant is that the steady state
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS models cannot be trained without guidance from autoregressive TTS models as their external aligners. In this work, we propose Glow-TTS, a flow-based generative model for parallel TTS that does not require any external aligner. By combining the properties of flows and dynamic programming, the proposed model searches for the most probable monotonic alignment between text and the latent representation of speech on its own. We demonstrate that enforcing hard monotonic alignments enables robust TTS, which generalizes to long utterances, and employing generative flows enables fast, diverse, and controllable speech synthesis. Glow-TTS obtains an order-of-magnitude speed-up over the autoregressive model, Tacotron 2, at synthesis with comparable speech quality. We further show that our model can be easily extended to a multi-speaker setting.
Expression of transforming growth factor-β in the rat ventral prostate during castration-induced programmed cell death

Castration-induced androgen deprivation leads to the activation of the programmed death of the androgen-dependent prostatic epithelial cells in the rat ventral prostate. In order to identify potential mediators of this programmed cell death, the expression of transforming growth factor-beta (TGF beta) in the rat ventral prostate was studied, after castration induced-androgen withdrawal. Steady state levels of TGF beta mRNA were determined by Northern blot analysis and compared with mRNA levels for prostatein C3, the major androgen-dependent secretory protein of ventral prostate and also with mRNA levels for TRPM-2, a gene that is specifically expressed during castration induced prostatic cell death. Within the first day after castration there was a dramatic increase in the levels of TGF beta mRNA in the ventral prostate (approximately 10-fold) and by 4 days after castration TGF beta mRNA was maximally expressed (approximately 40-fold increase), by which time the androgen-dependent C3 secretory protein mRNA transcripts have diminished to undetectable levels. Androgen administration to 4-day castrated rats led to a marked decrease in TGF beta mRNA to a level comparable to its constitutive expression obtained in the intact control animals, indicating that expression of TGF beta in the rat ventral prostate is under negative androgenic regulation. The transcript levels encoding TRPM-2 initially increased 10-fold within the first day after castration and by day 4 post castration there was a dramatic increase (approximately 50-fold) which correlated well with the maximal rate of cell death of the androgen-dependent prostatic epithelial cells. (ABSTRACT TRUNCATED AT 250 WORDS)
Leveraging WiFi Network Logs to Infer Social Interactions: A Case Study of Academic Performance and Student Behavior

On university campuses, social interactions among students can explain their academic experiences. However, assessing these interactions with surveys fails to capture their dynamic nature. While these behaviors can be captured with client-based passive sensing, these techniques are limited in scalability. By contrast, infrastructure-based approaches can scale to a large cohort and infer social interactions based on collocation of students. This paper investigates one such approach by leveraging WiFi association logs archived by a managed campus network. In their raw form, access point logs can approximate a student's location but with low spatio-temporal resolution. This paper first demonstrates that processing these logs can infer the collocation of 46 students in 34 lectures over 3 months, with a precision of 0.89 and a recall of 0.75. Next, we investigate how this WiFi-based coarse collocation reflects signals of social interaction. With 163 students in 54 project groups, we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through 14 weeks. Additionally, this paper presents preliminary insights for other campus-centric applications of automatically inferred social interactions. Finally, this paper discusses how repurposing archival WiFi logs can facilitate applications for other domains like mental wellbeing and physical health.
Reversal of Rényi Entropy Inequalities Under Log-Concavity

We establish a discrete analog of the Rényi entropy comparison due to Bobkov and Madiman. For log-concave variables on the integers, the min entropy is within <inline-formula> <tex-math notation="LaTeX">$\log e$ </tex-math></inline-formula> of the usual Shannon entropy. Additionally we investigate the entropic Rogers-Shephard inequality studied by Madiman and Kontoyannis, and establish a sharp Rényi version for certain parameters in both the continuous and discrete cases.
Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video

We present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled synthetic video (or, more generally, for discovering and modeling predictable features in time-series data). We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion are as simple as possible, by minimizing a combination of nonlinearity, acceleration, and prediction error. Differential equations describing the motion are then discovered using Pareto-optimal symbolic regression. We find that our pre-regression ("pregression") step is able to rediscover Cartesian coordinates of unlabeled moving objects even when the video is distorted by a generalized lens. Using intuition from multidimensional knot theory, we find that the pregression step is facilitated by first adding extra latent space dimensions to avoid topological problems during training and then removing these extra dimensions via principal component analysis. An inertial frame is autodiscovered by minimizing the combined equation complexity for multiple experiments.
VDO-SLAM: A Visual Dynamic Object-aware SLAM System

The scene rigidity assumption, also known as the static world assumption, is common in SLAM algorithms. Most existing algorithms operating in complex dynamic environments simplify the problem by removing moving objects from consideration or tracking them separately. Such strong assumptions limit the deployment of autonomous mobile robotic systems in a wide range of important real world applications involving highly dynamic and unstructured environments. This paper presents VDO-SLAM, a robust object-aware dynamic SLAM system that exploits semantic information to enable motion estimation of rigid objects in the scene without any prior knowledge of the objects shape or motion models. The proposed approach integrates dynamic and static structures in the environment into a unified estimation framework resulting in accurate robot pose and spatio-temporal map estimation. We provide a way to extract velocity estimates from object pose change of moving objects in the scene providing an important functionality for navigation in complex dynamic environments. We demonstrate the performance of the proposed system on a number of real indoor and outdoor datasets. Results show consistent and substantial improvements over state-of-the-art algorithms. An open-source version of the source code is available.
Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation

Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that "simplifies" the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.
A physical background model for the Fermi Gamma-ray Burst Monitor

We present the first physically motivated background model for the Gamma-ray Burst Monitor (GBM) on board the Fermi satellite. Such a physically motivated background model has the potential to significantly improve the scientific output of Fermi/GBM, as it can be used to improve the background estimate for spectral analysis and localization of gamma-ray bursts (GRBs) and other sources. Additionally, this model can also lead to detections of new transient events, since long and weak, or slowly rising, events do not activate one of the existing trigger algorithms. In this paper we show the derivation of such a physically motivated background model, which includes the modeling of the different background sources and the correct handling of the response of GBM. While the goal of the paper is to introduce the model rather than developing a transient search algorithm, we demonstrate the ability of the model to fit the background seen by GBM by showing the following four applications for (1) a canonical GRB, (2) the ultra-long GRB 091024, (3) the V404 Cygni outburst in June 2015, and (4) the ultra-long GRB 130925A.
Structural Analysis of Antioxidative Peptides from Soybean .beta.-Conglycinin

Protease hydrolyses of a soybean protein, beta-conglycinin (7S protein), yielded antioxidative activity against the peroxidation of linoleic acid in an aqueous system at pH 7.0. Six antioxidative peptides were isolated from the hydrolysate prepared with protease S by size exclusion chromatography and reversed-phase HPLC. The amino acid sequences of the peptides were determined using a gas-phase protein sequencer and electron spray mass spectrometry. The peptides were composed of 5-16 amino acid residues, including hydrophobic amino acids, valine or leucine, at the N-terminal positions, and proline, histidine, or tyrosine in the sequences.
Modeling and Optimization of Latency in Erasure-coded Storage Systems

As consumers are increasingly engaged in social networking and E-commerce activities, businesses grow to rely on Big Data analytics for intelligence, and traditional IT infrastructures continue to migrate to the cloud and edge, these trends cause distributed data storage demand to rise at an unprecedented speed. Erasure coding has seen itself quickly emerged as a promising technique to reduce storage cost while providing similar reliability as replicated systems, widely adopted by companies like Facebook, Microsoft and Google. However, it also brings new challenges in characterizing and optimizing the access latency when erasure codes are used in distributed storage. The aim of this monograph is to provide a review of recent progress (both theoretical and practical) on systems that employ erasure codes for distributed storage. In this monograph, we will first identify the key challenges and taxonomy of the research problems and then give an overview of different approaches that have been developed to quantify and model latency of erasure-coded storage. This includes recent work leveraging MDS-Reservation, Fork-Join, Probabilistic, and Delayed-Relaunch scheduling policies, as well as their applications to characterize access latency (e.g., mean, tail, asymptotic latency) of erasure-coded distributed storage systems. We will also extend the problem to the case when users are streaming videos from erasure-coded distributed storage systems. Next, we bridge the gap between theory and practice, and discuss lessons learned from prototype implementation. In particular, we will discuss exemplary implementations of erasure-coded storage, illuminate key design degrees of freedom and tradeoffs, and summarize remaining challenges in real-world storage systems such as in content delivery and caching. Open problems for future research are discussed at the end of each chapter.
BPS Skyrme neutron stars in generalized gravity

We study the coupling of nuclear matter described by the BPS Skyrme model to generalized gravity. Concretely, we consider the Starobinsky model which provides the leading-order correction to the Einstein-Hilbert action. Static solutions describing neutron stars are found both for the full field theory and for the mean-field approximation. We always consider the full Starobinsky model in the nonperturbative approach, using appropriately generalized shooting methods for the numerical neutron star calculations. Many of our results are similar to previous investigations of neutron stars for the Starobinsky model using other models of nuclear matter, but there are some surprising discrepancies. The “Newtonian mass” relevant for the surface redshift, e.g., results larger than the ADM mass in our model, in contrast to other investigations. This difference is related to the particularly high stiffness of nuclear matter described by the BPS Skyrme model and offers an interesting possibility to distinguish different models of nuclear matter within generalized gravity.
Deep Residual Learning via Large Sample Mean-Field Stochastic Optimization

We study a class of stochastic optimization problems of the mean-field type arising in the optimal training of a deep residual neural network. We consider the sampling problem arising from a continuous layer idealization, and establish the existence of optimal relaxed controls when the training set has finite size. The core of our paper is to prove the Gamma-convergence of the sequence of sampled objective functionals, i.e., show that as the size of the training set grows large, the minimizer of the sampled relaxed problem converges to that of the limiting optimization problem. We connect the limit of the large sampled objective functional to the unique solution, in the trajectory sense, of a nonlinear Fokker-Planck-Kolmogorov (FPK) equation in a random environment. We construct an example to show that, under mild assumptions, the optimal network weights can be numerically computed by solving a second-order differential equation with Neumann boundary conditions in the sense of distributions.
Conditions for the Maintenance of Prezygotic and Zygotic Isolation in a Continent-Island Model

Abstract A continent-island model is studied in which differences are initially present at a gene locus with assortative mating (which causes prezygotic reproductive isolation) and at one or two unlinked selected loci, (which cause zygotic reproductive isolation). This model simulates secondary contact between two populations which have diverged allopatrically at loci which control isolating mechanisms. Two different models of prezygotic isolation and three different models of zygotic isolation are studied. The analysis is focused on those conditions under which differentiation is not lost through the swamping effect of gene now. In each case the critical migration rate ( m c ) below which differences can be maintained is found. Results depend on the degree of assortative mating, on the level of selection on the genes involved, and on the particular models of reproductive isolation considered. However, in all the models, for high values of such parameters the two isolating mechanisms interact strongly, and consequently the stability of the difference at each locus (as measured by m c ) is noticeably influenced by the presence of the difference at the other locus (or loci). The nature and strength of the effects of this interaction differ according to whether or not there is selection on the assortative mating locus and whether selection against hybrids or directional selection is hypothesized.