Miracle-dz/UniFAR-FTUs huggingface dataset:
Renibacterium salmoninarum—The Causative Agent of Bacterial Kidney Disease in Salmonid Fish

Renibacterium salmoninarum is one of the oldest known bacterial pathogens of fish. This Gram-positive bacterium is the causative agent of bacterial kidney disease, a chronic infection that is mostly known to infect salmonid fish at low temperatures. Externally, infected fish can display exophthalmia as well as blebs on the skin and ulcerations alongside haemorrhages at the base of the fins and alongside the lateral line. Internally, the kidney, heart, spleen and liver can show signs of swelling. Granulomas can be seen on various internal organs, as can haemorrhages, and the organs can be covered with a false membrane. Ascites can also accumulate in the abdominal cavity. The bacterium is generally cultivated on specialized media such as kidney disease medium-1 (KDM-1), KDM-2 and selective kidney disease medium (SKDM), and a diagnostic is performed using molecular tools such as PCRs or real-time quantitative PCRs (RT-qPCRs). Several virulence mechanisms have been identified in R. salmoninarum, in particular the protein p57 that is known to play a role in both agglutination and immunosuppression of the host’s defense mechanisms. Control of the disease is difficult; the presence of asymptomatic carriers complicates the eradication of the disease, as does the ability of the bacterium to gain entrance inside the eggs. Bacterin-killed vaccines have proven to be of doubtful efficacy in controlling the disease, and even more recent application of a virulent environmental relative of R. salmoninarum is of limited efficacy. Treatment by antibiotics such as erythromycin, azithromycin and enrofloxacin can be effective but it is slow and requires prolonged treatment. Moreover, antibiotic-resistant strains have been reported. Despite being known for a long time, there is still much to be discovered about R. salmoninarum, notably regarding its virulence mechanisms and its vaccine potential. Consequently, these gaps in knowledge continue to hinder control of this bacterial disease in aquaculture settings.
Differential Expression of Maize and Teosinte microRNAs under Submergence, Drought, and Alternated Stress

Submergence and drought stresses are the main constraints to crop production worldwide. MicroRNAs (miRNAs) are known to play a major role in plant response to various stresses. In this study, we analyzed the expression of maize and teosinte miRNAs by high-throughput sequencing of small RNA libraries in maize and its ancestor teosinte (Zea mays ssp. parviglumis), under submergence, drought, and alternated stress. We found that the expression patterns of 67 miRNA sequences representing 23 miRNA families in maize and other plants were regulated by submergence or drought. miR159a, miR166b, miR167c, and miR169c were downregulated by submergence in both plants but more severely in maize. miR156k and miR164e were upregulated by drought in teosinte but downregulated in maize. Small RNA profiling of teosinte subject to alternate treatments with drought and submergence revealed that submergence as the first stress attenuated the response to drought, while drought being the first stress did not alter the response to submergence. The miRNAs identified herein, and their potential targets, indicate that control of development, growth, and response to oxidative stress could be crucial for adaptation and that there exists evolutionary divergence between these two subspecies in miRNA response to abiotic stresses.
A Survey of Free Space Optical Communications in Satellites

Free space optical (FSO) communications is an up and coming set of technologies that promises significantly higher data rates at lower size, weight, and power than currently achievable by RF communications. FSO communications in satellites has been a subject of research for decades. It is strongly believed that FSO communications will be incorporated in next generation communications relay satellites and that FSO communications will enable new types of interplanetary missions previously infeasible due to bandwidth or power restrictions. FSO technologies are becoming mature enough to be fielded for use and represent a complex trade space of new components and technologies. This paper presents an overview of concepts in satellite FSO communications including deep space communications, pointing, cloud coverage, modulation, and detection, and discusses the current devices and technologies that enable FSO
Methodology for modeling and determining the frequency response trace of robotic composite structures

The methodology of modeling and determining the natural frequencies of oscillations of a robotic structure made of composite material including motors, bearings, gears, and ratio changers is considered. The vibration forms of a multi-stage bench for semi-natural modeling from composite material determined using the finite element method are presented. The developed methods allow the study of complex robotic systems of homogeneous and composite materials. Streszczenie. Rozważana jest metodologia modelowania i określania naturalnych częstotliwości drgań konstrukcji robotycznej wykonanej z materiału kompozytowego, w tym silników, łożysk, kół zębatych i zmieniaczy przełożeń. Opracowane metody pozwalają na badanie złożonych systemów robotycznych z materiałów jednorodnych i kompozytowych. Metoda modelowania i określania częstotliwości drgań kompozytowych konstrukcji robotyki
Compressive Sensing a New Approach For Data Compression: A Review

— As we know that for data compression generally Shannon – Nyquist theorem taken in to consideration. But a severe problem which is associated with the traditional theory is the storage problem. According to this theorem the sampling rate must be twice the largest frequency component of the signal which we want to reconstruct. Due to this the data which is required to transmit a signal or to store it is too large. So to overcome this problem a new method is proposed, which is known as Compressive sensing. The sampling rate which is required reconstruct the signal is comparatively low in the compressive sensing. The various aspects about the compressive sensing and literature review with some important properties is given below
High school writing teacher feedback on word choice errors

This study scrutinizes the range and types of feedback given for word choice errors occurring in the English Taiwan Learner Corpus (ETLC), which contains Taiwanese high school students’ English writings and the corrective feedback provided by L2 writing teachers. All instances of word choice error tags (n = 1,439) were extracted from the ETLC for analyses. Results showed L2 writing teachers provided indirect feedback more often than direct feedback, requiring students to self-correct without guidance. Furthermore, many errors tagged as word choice were grammar errors, further questioning L2 writing teachers’ understanding of word choice errors and competence to correct such errors. This study highlights the importance of raising students’ awareness to targeted lexis prior to completing L2 writing tasks. We also argue that there are benefits for L2 writing teachers to provide focused and direct word choice error feedback after the completion of such tasks.
VALIDITY AND RELIABILITY OF VISUAL ANALOGUE SCALE (VAS) FOR PAIN MEASUREMENT

Aim: The aim of the study was to find out the validity and reliability of visual analogue scale in pain measurement. Methodology: A critical review was done for this study. Total ten studies are included in this study. The majority of study designs are cross sectional study. In analyses most of the studies used spearman correlation was used to see the strength of the study. The participants’ age was more than 18 years. The most of the studies seen the post-operative pain by pain measurement scale. The used scales were visual analogue scale, numeric rating scale, color analogue scale and faces pain scales. Conclusion: The majority of the studies showed that visual analogue scale is a valid and reliable scale. Also, it is an interval scale. So, in clinical practice we can use this scale in case of pain measurement as an outcome measure tool.
Optimal Off-Policy Evaluation from Multiple Logging Policies

We study off-policy evaluation (OPE) from multiple logging policies, each generating a dataset of fixed size, i.e., stratified sampling. Previous work noted that in this setting the ordering of the variances of different importance sampling estimators is instance-dependent, which brings up a dilemma as to which importance sampling weights to use. In this paper, we resolve this dilemma by finding the OPE estimator for multiple loggers with minimum variance for any instance, i.e., the efficient one. In particular, we establish the efficiency bound under stratified sampling and propose an estimator achieving this bound when given consistent $q$-estimates. To guard against misspecification of $q$-functions, we also provide a way to choose the control variate in a hypothesis class to minimize variance. Extensive experiments demonstrate the benefits of our methods' efficiently leveraging of the stratified sampling of off-policy data from multiple loggers.
Differential dispersal costs and sex-biased dispersal distance in a cooperatively breeding bird

In most bird species, dispersal distance from the natal territory to a breeding territory is greater for females than for males. Two main hypotheses have been proposed to explain sex-biased dispersal: 1) it serves as an inbreeding-avoidance mechanism or 2) it is linked to a sex difference in resource-holding potential and territory establishment. Additionally, in species where individuals delay dispersal and become subordinates in a natal territory, differences in benefits of philopatry (e.g. territory inheritance, own reproduction) may also affect sex-biased dispersal. We show that in the group-living Seychelles warbler, Acrocephalus sechellensis, females disperse further to obtain a breeding position than males do. However, we found no evidence that female-biased dispersal distance can be explained by the above-mentioned hypotheses: further dispersal does not lead to less-related partners, both sexes defend and can inherit a territory, and subordinate females are more likely to obtain some reproduction than subordinate males. Instead, we provide evidence for a little-explored hypothesis based on sex differences in dispersal costs: namely that extra-territorial forays, pursued to search for limited vacancies, are more costly for males in terms of increased mortality, although the exact mechanism for this is unclear. In line with differential dispersal costs, males foray less far than females and often wait for local dispersal opportunities, ultimately resulting in a shorter average dispersal distance. Our results may help future studies in explaining sex-biased dispersal in social and perhaps also non-social species, and we suggest some mechanisms that may explain why sex-biased dispersal differs between species.
RigidFusion: Robot Localisation and Mapping in Environments With Large Dynamic Rigid Objects

This work presents a novel RGB-D SLAM approach to simultaneously segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view. Previous approaches treat dynamic parts of a scene as outliers and are thus limited to a small amount of changes in the scene, or rely on prior information for all objects in the scene to enable robust camera tracking. Here, we propose to treat all dynamic parts as one rigid body and simultaneously segment and track both static and dynamic components. We, therefore, enable simultaneous localisation and reconstruction of both the static background and rigid dynamic components in environments where dynamic objects cause large occlusion. We evaluate our approach on multiple challenging scenes with large dynamic occlusion. The evaluation demonstrates that our approach achieves better motion segmentation, localisation and mapping without requiring prior knowledge of the dynamic object's shape and appearance.
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions, and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.
"Are you home alone?" "Yes" Disclosing Security and Privacy Vulnerabilities in Alexa Skills

The home voice assistants such as Amazon Alexa have become increasingly popular due to many interesting voice-activated services provided through special applications called skills. These skills, though useful, have also introduced new security and privacy challenges. Prior work has verified that Alexa is vulnerable to multiple types of voice attacks, but the security and privacy risk of using skills has not been fully investigated. In this work, we study an adversary model that covers three severe privacy-related vulnerabilities, namely,over-privileged resource access, hidden code-manipulation and hidden content-manipulation. By exploiting these vulnerabilities, malicious skills can not only bypass the security tests in the vetting process, but also surreptitiously change their original functions in an attempt to steal users' personal information. What makes the situation even worse is that the attacks can be extended from virtual networks to the physical world. We systematically study the security issues from the feasibility and implementation of the attacks to the design of countermeasures. We also made a comprehensive survey study of 33,744 skills in Alex Skills Store.
A stochastic maximum principle for forward-backward stochastic control systems with quadratic generators and sample-wise constraints

Abstract. This paper examines the stochastic maximum principle (SMP) for a forward-backward stochastic control system where the backward state equation is characterized by the backward stochastic differential equation (BSDE) with quadratic growth and the forward state at the terminal time is constrained in a convex set with probability one. With the help of the theory of BSDEs with quadratic growth and the bounded mean oscillation (BMO) martingales, we employ the terminal perturbation approach and Ekeland’s variational principle to obtain a dynamic stochastic maximum principle. The main result has a wide range of applications in mathematical finance and we investigate a robust recursive utility maximization problem with bankruptcy prohibition as an example. 1991 Mathematics Subject Classification. 93E20, 60H10, 49K45.
Bayesian Algorithms for Decentralized Stochastic Bandits

We study a decentralized cooperative multi-agent multi-armed bandit (MAB) problem with <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> arms and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> agents connected over a network. In this model, each arm’s reward distribution is the same for every agent, and rewards are drawn independently across agents and over time steps. At each iteration, agents independently choose an arm to play and exchange at most <inline-formula> <tex-math notation="LaTeX">$\mathsf {poly}(K)$ </tex-math></inline-formula> real-valued messages with their neighbors. Existing lower bound on the average per-agent regret over the network shows that cooperation with other agents can achieve a reduction of <inline-formula> <tex-math notation="LaTeX">$O\left({\frac {1}{N}}\right)$ </tex-math></inline-formula> over when playing in isolation. Motivated by this, we study a message-passing algorithm that can be combined with existing Bayesian MAB algorithms. Using this, we propose a decentralized Thompson Sampling (TS) algorithm and a decentralized Bayes-UCB algorithm. Under decentralized TS for bounded rewards, we establish a problem-dependent upper bound on the average per-agent regret in terms of the number of agents in the network and the network topology. For Bernoulli rewards, our upper bound asymptotically matches the lower bound. We empirically show that the proposed decentralized TS algorithm incurs significantly lesser per-agent regret than previously proposed algorithms. Furthermore, we show that the proposed decentralized TS can be extended to general bandit problems, where posterior distribution cannot be computed in closed form. We combine our decentralized TS algorithm with Variational Inference to apply our proposed algorithm to complex realistic reward distributions in a computationally efficient manner. We implement our proposed decentralized TS under gossip protocol and over time-varying networks, where each communication link has a fixed probability of failure and show that it incurs logarithmic regret.
Improving the Iterative Closest Point Algorithm using Lie Algebra

Mapping algorithms that rely on registering point clouds inevitably suffer from local drift, both in localization and in the built map. Applications that require accurate maps, such as environmental monitoring, benefit from additional sensor modalities that reduce such drift. In our work, we target the family of mappers based on the Iterative Closest Point (ICP) algorithm which use additional orientation sources such as the Inertial Measurement Unit (IMU). We introduce a new angular penalty term derived from Lie algebra. Our formulation avoids the need for tuning arbitrary parameters. Orientation covariance is used instead, and the resulting error term fits into the ICP cost function minimization problem. Experiments performed on our own real-world data and on the KITTI dataset show consistent behavior while suppressing the effect of outlying IMU measurements. We further discuss promising experiments, which should lead to optimal combination of all error terms in the ICP cost function minimization problem, allowing us to smoothly combine the geometric and inertial information provided by robot sensors.
Logistic $Q$-Learning

We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The method is closely related to the classic Relative Entropy Policy Search (REPS) algorithm of Peters et al. (2010), with the key difference that our method introduces a Q-function that enables efficient exact model-free implementation. The main feature of our algorithm (called QREPS) is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error. We provide a practical saddle-point optimization method for minimizing this loss function and provide an error-propagation analysis that relates the quality of the individual updates to the performance of the output policy. Finally, we demonstrate the effectiveness of our method on a range of benchmark problems.
TMT: A Transformer-based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-aware Dialog

Audio Visual Scene-aware Dialog (AVSD) is a task to generate responses when discussing about a given video. The previous state-of-the-art model shows superior performance for this task using Transformer-based architecture. However, there remain some limitations in learning better representation of modalities. Inspired by Neural Machine Translation (NMT), we propose the Transformer-based Modal Translator (TMT) to learn the representations of the source modal sequence by translating the source modal sequence to the related target modal sequence in a supervised manner. Based on Multimodal Transformer Networks (MTN), we apply TMT to video and dialog, proposing MTN-TMT for the video-grounded dialog system. On the AVSD track of the Dialog System Technology Challenge 7, MTN-TMT outperforms the MTN and other submission models in both Video and Text task and Text Only task. Compared with MTN, MTN-TMT improves all metrics, especially, achieving relative improvement up to 14.1% on CIDEr. Index Terms: multimodal learning, audio-visual scene-aware dialog, neural machine translation, multi-task learning
A preliminary survey of Aroids (Family Araceae) in Maliau Basin, Sabah, Malaysia

Fourteen Aroids genera (Family Araceae) were collected during a fi eld expedition to Maliau Basin from 11 to 15 March 2005. The genera are Aglaonema (A. nitidum); Alocasia (A. cuprea, A. longiloba, A. princeps, A. macrorrhiza, A. scabriuscula); Amorphophallus (A. sp.); Amydrium (A. medium); Anadendrum (A. montanum); Colocasia (C. esculenta, C. oresbia); Homalomena (H. humilis, H. sp.1, H. sp.2); Aridarum (A. nicolsonii); Bucephalandra (B. motleyana); Epipremnum (Epipremnum giganteum); Pothos (Pothos sp.); Rhaphidophora (R. korthalsii); Schismatoglottis (S. calyptrata, S. viridissima, Schismatoglottis sp.) and Scindapsus (S. pictus). The most dominant genera found are Homalomena and Schismatoglottis. Most of the species, except Amorphophallus sp. , were generally widely distributed along undisturbed riverbanks. It is generally concluded that most of aroids are found in wet areas particularly along river banks.
Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin

Nigerian Pidgin remains one of the most popular languages in West Africa. With at least 75 million speakers along the West African coast, the language has spread to diasporic communities through Nigerian immigrants in England, Canada, and America, amongst others. In contrast, the language remains an under-resourced one in the field of natural language processing, particularly on speech recognition and translation tasks. In this work, we present the first parallel (speech-to-text) data on Nigerian pidgin. We also trained the first end-to-end speech recognition system (QuartzNet and Jasper model) on this language which were both optimized using Connectionist Temporal Classification (CTC) loss. With baseline results, we were able to achieve a low word error rate (WER) of 0.77% using a greedy decoder on our dataset. Finally, we open-source the data and code along with this publication in order to encourage future research in this direction.
Towards Causal Understanding of Fake News Dissemination

Recent years have witnessed remarkable progress made towards the computational detection of fake news. To mitigate its negative impact, however, we argue that a critical element is to understand why people spread fake news. Central to the question of "why" is the need to study the fake news sharing behavior. Deeply related to user characteristics and online activities, fake news sharing behavior is important to uncover the causal relationships between user attributes and the probability of this user to spread fake news. One obstacle in learning such user behavior is that most data is subject to selection bias, rendering partially observed fake news dissemination among users. To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination. Drawing on theories in causal inference, in this work, we first propose a principled approach to unbiased modelings of fake news dissemination under selection bias. We then consider the learned fake news sharing behavior as the measured confounder and further identify the user attributes that potentially cause users to spread fake news. We theoretically and empirically characterize the effectiveness of the proposed approach and find that it could be useful in protecting society from the perils of fake news.