Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds

This technical report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process. The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features. Experiments were held, using the six machine type datasets of the challenge. Overall, competitive results were achieved by the proposed dense and convolutional AE, outperforming the baseline challenge method.
Introduction of the Great-Tailed Grackle (Quiscalus mexicanus) by Aztec Emperor Auitzotl: Provenance of the Historical Account

ABSTRACT. The introduction of the Great-tailed Grackle (Quiscalus mexicanus) into the Valley of Mexico by Aztec Emperor Auitzotl (1486–1502) is the earliest documented case of human-mediated bird introduction in the Western Hemisphere. I investigated the origin of the account of this ancient translocation and the team that collected it: Bernardino de Sahagun's research group of elite native scholars from the Royal College of the Holy Cross, Tlatelolco. These scholars, the top academic research group in 16th-century Mexico, used expert native consultants as sources of information and an early form of peer review to detect errors and omissions. The account first appears in extant drafts written by the research group during its field work in Tlatelolco (1561–1565), 59–79 years after Auitzotl introduced the grackles. The account then successfully passed through peer review in Tenochtitlan before being added to the Florentine Codex. The research group was composed of graduates, professors, and an administrator of the Royal College. Professor Antonio Valeriano, a key member of the research group, was married to Isabel Huanitzin, an Aztec princess closely related to Auitzotl. Because all the many details of the account successfully passed peer review, and also because the account lacks the usual warning phrases used to alert the reader to doubtful information such as fables, it is clear that the research group judged the story of Auitzotl's introduction of the Great-tailed Grackle to be true. The presence of an actual conversation in the Aztec text suggests eyewitness testimony or reliance on an earlier-written native manuscript.
Total phenols, antioxidant potential and antimicrobial activity of the methanolic extracts of Ephedra laristanica

= 0.926 and 0.913, respectively, p < 0.0001). The antimicrobial capacity was screened against Gram positive and Gram negative bacteria, and fungi. The extract inhibited the growth of Gram negative bacteria, being Escherichia coli the most susceptible one with MIC of 32 µg/mL for the extract. The results obtained indicate that E. laristanica may become important in the obtainment of a noticeable source of compounds with health protective potential and antimicrobial activity. Key words:
Reproductive performance of Karakul ewes following different oestrous synchronisation treatments outside the natural breeding season

This study was designed to evaluate the efficiency of five different hormonal treatments of oestrous synchronization in Karakul ewes reared in southern Iran. During mid- to late spring, (outside the natural breeding season), 180 Karakul ewes were stratified based on age and body weight and then randomly allocated to six groups (n = 30/group). The oestrous cycles of the ewes in each group were synchronized using one of the following hormonal treatments: T1 - intramuscular (im) administration of 20 mg progesterone acetate in oil every second day for a 12-day period and an im administration of 500 IU equine chorionic gonadotrophin (eCG) on day 12; T2 - insertion of a controlled intravaginal drug releasing device (CIDR) containing 0.3 g progesterone for 12 days; T3 - administrating an intravaginal sponge containing 60 mg medroxyprogesterone acetate (MAP) for 12 days; T4 - a double injection (im) of 10 mg prostaglandin F2α nine days apart; T5 - a double injection (im) of 10 mg prostaglandin F2α nine days apart plus an im injection of 500 IU eCG on the day of the second prostaglandin F2α administration; T6 - control group. The ewes in Treatments 2 and 3 were intramuscularly injected with a 500 IU eCG at CIDR or sponge withdraw. Twenty-four hours after the last hormonal treatment, three fertile Karakul rams were introduced to the ewes in each experimental group and remained there for five days for oestrous detection and mating. Significant differences in oestrous response, number of ewes that lambed within the 152 ± 7 day period, fecundity and fertility rate were recorded between treatment groups. The oestrous response was comparable in Treatments 2 (93%) and 3 (100%), and significantly higher than the other treatments (T1 = 80; T4 = 37; T5 = 30 and T6 = 20%). The highest and the lowest number of ewes that lambed within 152 ± 7 days were 27 (90%) in Treatments 3 and 4 and 13% in Treatments 4 and 5, respectively. The highest recorded fecundity rate was 133% in Treatment 3 and the lowest, 75% in Treatment 4. Furthermore, the fertility rate was 90% in Treatment 3 that was significantly higher than in Treatments 4 (36%) and 5 (44%). The results of the present study indicated that oestrous synchronisation with progestagens plus eCG induced a synchronized oestrus in a higher percentage of Iranian fat-tailed Karakul ewes than prostaglandin F2α (with or without eCG) outside the breeding season. Furthermore, the administration of an impregnated intravaginal sponge for a 12-day period plus a dose of 500 IU eCG resulted in higher fertility, fecundity and lambing rates compared to the other hormonal treatments considered. Keywords : Karakul ewes, oestrous synchronization, reproductive performance South African Journal of Animal Science Vol. 36 (4) 2006: pp. 229-234
Artificial Musical Intelligence: A Survey

Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial intelligence research. While still nascent, several different approaches have been employed to tackle what may broadly be referred to as "musical intelligence. " This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit, with a particular emphasis on machine learning methods.
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario

Complex activity recognition can benefit from understanding the steps that compose them. Current datasets, however, are annotated with one label only, hindering research in this direction. In this paper, we describe a new dataset for sensor-based activity recognition featuring macro and micro activities in a cooking scenario. Three sensing systems measured simultaneously, namely a motion capture system, tracking 25 points on the body; two smartphone accelerometers, one on the hip and the other one on the forearm; and two smartwatches one on each wrist. The dataset is labeled for both the recipes (macro activities) and the steps (micro activities). We summarize the results of a baseline classification using traditional activity recognition pipelines. The dataset is designed to be easily used to test and develop activity recognition approaches.
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning framework for CO problems on graphs that can provide integral solutions of certified quality. Inspired by Erdos' probabilistic method, we use a neural network to parametrize a probability distribution over sets. Crucially, we show that when the network is optimized w.r. t. a suitably chosen loss, the learned distribution contains, with controlled probability, a low-cost integral solution that obeys the constraints of the combinatorial problem. The probabilistic proof of existence is then derandomized to decode the desired solutions. We demonstrate the efficacy of this approach to obtain valid solutions to the maximum clique problem and to perform local graph clustering. Our method achieves competitive results on both real datasets and synthetic hard instances.
Space Sound Effects Short Film Festival: using the film festival model to inspire creative art–science and reach new audiences

Abstract. The ultra-low frequency analogues of sound waves in Earth's magnetosphere play a crucial role in space weather; however, the public is largely unaware of this risk to our everyday lives and technology. As a way of potentially reaching new audiences, SSFX (Space Sound Effects) made 8 years of satellite wave recordings audible to the human ear with the aim of using it to create art. Partnering with film industry professionals, the standard processes of international film festivals were adopted by the project in order to challenge independent filmmakers to incorporate these sounds into short films in creative ways. Seven films covering a wide array of topics and genres (despite coming from the same sounds) were selected for screening at a special film festival out of 22 submissions. The works have subsequently been shown at numerous established film festivals and screenings internationally. These events have attracted diverse non-science audiences resulting in several unanticipated impacts on them, thereby demonstrating how working with the art world can open up dialogues with both artists and audiences who would not ordinarily engage with science.
Compositional Generalization by Learning Analytical Expressions

Compositional generalization is a basic but essential intellective capability of human beings, which allows us to recombine known parts readily. However, existing neural network based models have been proven to be extremely deficient in such a capability. Inspired by work in cognition which argues compositionality can be captured by variable slots with symbolic functions, we present a refreshing view that connects a memory-augmented neural model with analytical expressions, to achieve compositional generalization. Our model consists of two cooperative neural modules Composer and Solver, fitting well with the cognitive argument while still being trained in an end-to-end manner via a hierarchical reinforcement learning algorithm. Experiments on a well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization, solving all challenges addressed by previous works with 100% accuracies.
MIMICS: A Large-Scale Data Collection for Search Clarification

Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still feels the lack of a large-scale dataset for studying different aspects of search clarification. In this paper, we introduce MIMICS, a collection of search clarification datasets for real web search queries sampled from the Bing query logs. Each clarification in MIMICS is generated by a Bing production algorithm and consists of a clarifying question and up to five candidate answers. MIMICS contains three datasets: (1) MIMICS-Click includes over 400k unique queries, their associated clarification panes, and the corresponding aggregated user interaction signals (i.e., clicks). (2) MIMICS-ClickExplore is an exploration data that includes aggregated user interaction signals for over 60k unique queries, each with multiple clarification panes. (3) MIMICS-Manual includes over 2k unique real search queries. Each query-clarification pair in this dataset has been manually labeled by at least three trained annotators. It contains graded quality labels for the clarifying question, the candidate answer set, and the landing result page for each candidate answer. MIMICS is publicly available for research purposes, thus enables researchers to study a number of tasks related to search clarification, including clarification generation and selection, user engagement prediction for clarification, click models for clarification, and analyzing user interactions with search clarification. We also release the results returned by the Bing's web search API for all the queries in MIMICS. This would allow researchers to utilize search results for the tasks related to search clarification.
Matern Gaussian processes on Riemannian manifolds

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance. Motivated by applications in the physical sciences, the widely-used Matern class of Gaussian processes has recently been generalized to model functions whose domains are Riemannian manifolds, by re-expressing said processes as solutions of stochastic partial differential equations. In this work, we propose techniques for computing the kernels of these processes via spectral theory of the Laplace--Beltrami operator in a fully constructive manner, thereby allowing them to be trained via standard scalable techniques such as inducing point methods. We also extend the generalization from the Matern to the widely-used squared exponential Gaussian process. By allowing Riemannian Matern Gaussian processes to be trained using well-understood techniques, our work enables their use in mini-batch, online, and non-conjugate settings, and makes them more accessible to machine learning practitioners.
The equation $(x-d)^5+x^5+(x+d)^5=y^n$

In this paper, we solve the equation of the title under the assumption that $\gcd(x,d)=1$ and $n\geq 2$. This generalizes earlier work of the first author, Patel and Siksek [BPS16]. Our main tools include Frey-Hellegouarch curves and associated modular forms, and an assortment of Chabauty-type techniques for determining rational points on curves of small positive genus.
Video Semantic Segmentation With Distortion-Aware Feature Correction

Video semantic segmentation aims to generate an accurate semantic map for each frame in a video. For such a task, conducting per-frame image segmentation is generally unacceptable in practice due to high computation cost. To address this issue, many works perform the flow-based feature propagation to reuse the features of previous frames, which essentially exploits the content continuity of consecutive frames. However, the estimated optical flow would inevitably suffer inaccuracy and then make the propagated features distorted. In this article, we propose a distortion-aware feature correction method with the goal of improving video segmentation performance at a low price. Our core idea is to correct the features on distorted regions using the current frame while reserving the propagated features for other regions. In this way, a lightweight network is enough for achieving promising segmentation results. In particular, we propose to predict the distorted regions by utilizing the consistency of distortion patterns in images and features, such that the high-cost feature extraction from current frames can be avoided. We conduct extensive experiments on Cityscapes, CamVid, and UAVid, and the results show that our proposed method significantly outperforms previous methods and achieves the state-of-the-art performance on both segmentation accuracy and speed. Code and pretrained models are available at https://github.com/jfzhuang/DAVSS.
Language Guided Networks for Cross-modal Moment Retrieval

We address the challenging task of cross-modal moment retrieval, which aims to localize a temporal segment from an untrimmed video described by a natural language query. It poses great challenges over the proper semantic alignment between vision and linguistic domains. Existing methods independently extract the features of videos and sentences and purely utilize the sentence embedding in the multi-modal fusion stage, which do not make full use of the potential of language. In this paper, we present Language Guided Networks (LGN), a new framework that leverages the sentence embedding to guide the whole process of moment retrieval. In the first feature extraction stage, we propose to jointly learn visual and language features to capture the powerful visual information which can cover the complex semantics in the sentence query. Specifically, the early modulation unit is designed to modulate the visual feature extractor's feature maps by a linguistic embedding. Then we adopt a multi-modal fusion module in the second fusion stage. Finally, to get a precise localizer, the sentence information is utilized to guide the process of predicting temporal positions. Specifically, the late guidance module is developed to linearly transform the output of localization networks via the channel attention mechanism. The experimental results on two popular datasets demonstrate the superior performance of our proposed method on moment retrieval (improving by 5.8\% in terms of Rank1@IoU0.5 on Charades-STA and 5.2\% on TACoS). The source code for the complete system will be publicly available.
Demonstration of an All-Microwave Controlled-Phase Gate between Far-Detuned Qubits

A challenge in building large-scale superconducting quantum processors is to find the right balance between coherence, qubit addressability, qubit-qubit coupling strength, circuit complexity and the number of required control lines. Leading all-microwave approaches for coupling two qubits require comparatively few control lines and benefit from high coherence but suffer from frequency crowding and limited addressability in multi-qubit settings. Here, we overcome these limitations by realizing an all-microwave controlled-phase gate between two transversely coupled transmon qubits which are far detuned compared to the qubit anharmonicity. The gate is activated by applying a single, strong microwave tone to one of the qubits, inducing a coupling between the two-qubit $|f,g\rangle$ and $|g,e\rangle$ states, with $|g\rangle$, $|e\rangle$, and $|f\rangle$ denoting the lowest energy states of a transmon qubit. Interleaved randomized benchmarking yields a gate fidelity of $97.5\pm 0.3 \%$ at a gate duration of $126\,\rm{ns}$, with the dominant error source being decoherence. We model the gate in presence of the strong drive field using Floquet theory and find good agreement with our data. Our gate constitutes a promising alternative to present two-qubit gates and could have hardware scaling advantages in large-scale quantum processors as it neither requires additional drive lines nor tunable couplers.
Estimating ancestral distributions of lineages with uncertain sister groups: a statistical approach to Dispersal–Vicariance Analysis and a case using Aesculus L. (Sapindaceae) including fossils

Abstract We propose a simple statistical approach for using Dispersal–Vicariance Analysis (DIVA) software to infer biogeographic histories without fully bifurcating trees. In this approach, ancestral ranges are first optimized for a sample of Bayesian trees. The probability P of an ancestral range r at a node is then calculated as where Y is a node, and F(rY) is the frequency of range r among all the optimal solutions resulting from DIVA optimization at node Y, t is one of n topologies optimized, and Pt is the probability of topology t. Node Y is a hypothesized ancestor shared by a specific crown lineage and the sister of that lineage “x”, where x may vary due to phylogenetic uncertainty (polytomies and nodes with posterior probability <100%). Using this method, the ancestral distribution at Y can be estimated to provide inference of the geographic origins of the specific crown group of interest. This approach takes into account phylogenetic uncertainty as well as uncertainty from DIVA optimization. It is an extension of the previously described method called Bayes‐DIVA, which pairs Bayesian phylogenetic analysis with biogeographic analysis using DIVA. Further, we show that the probability P of an ancestral range at Y calculated using this method does not equate to pp*F(rY) on the Bayesian consensus tree when both variables are <100%, where pp is the posterior probability and F(rY) is the frequency of range r for the node containing the specific crown group. We tested our DIVA‐Bayes approach using Aesculus L. , which has major lineages unresolved as a polytomy. We inferred the most probable geographic origins of the five traditional sections of Aesculus and of Aesculus californica Nutt. and examined range subdivisions at parental nodes of these lineages. Additionally, we used the DIVA‐Bayes data from Aesculus to quantify the effects on biogeographic inference of including two wildcard fossil taxa in phylogenetic analysis. Our analysis resolved the geographic ranges of the parental nodes of the lineages of Aesculus with moderate to high probabilities. The probabilities were greater than those estimated using the simple calculation of pp*F(ry) at a statistically significant level for two of the six lineages. We also found that adding fossil wildcard taxa in phylogenetic analysis generally increased P for ancestral ranges including the fossil's distribution area. The ΔP was more dramatic for ranges that include the area of a wildcard fossil with a distribution area underrepresented among extant taxa. This indicates the importance of including fossils in biogeographic analysis. Exmination of range subdivision at the parental nodes revealed potential range evolution (extinction and dispersal events) along the stems of A. californica and sect. Parryana.
The compact triply eclipsing triple star TIC 209409435 discovered with TESS

We report the discovery in TESS Sectors 3 and 4 of a compact triply eclipsing triple star system. TIC 209409435 is a previously unknown eclipsing binary with a period of 5.717 d, and the presence of a third star in an outer eccentric orbit of 121.872-d period was found from two sets of third-body eclipses and from eclipse timing variations. The latter exhibits signatures of strong third-body perturbations. After the discovery, we obtained follow-up ground-based photometric observations of several binary eclipses as well as another of the third-body eclipses. We carried out comprehensive analyses, including the simultaneous photodynamical modelling of TESS and ground-based light curves (including both archival WASP data, and our own follow-up measurements), as well as eclipse timing variation curves. Also, we have included in the simultaneous fits multiple star spectral energy distribution data and theoretical PARSEC stellar isochrones. We find that the inner binary consists of near twin stars of mass 0.90 M⊙ and radius 0.88 R⊙. The third star is just 9 per cent more massive and 18 per cent larger in radius. The inner binary has a rather small eccentricity, while the outer orbit has e = 0.40. The inner binary and outer orbit have inclination angles within 0.1° and 0.2° of 90°, respectively. The mutual inclination angle is ≲1/4°. All of these results were obtained without radial velocity observations.
Breach of the Greater Omentum through the Bladder then the Urethra: A Case Report

Case Report Perforation of the bladder, as a late complication of radiation therapy, is a rare condition, especially on a non-necrotic bladder. We report in this publication, the case of a patient, who 1 and a half year after the end of her irradiation, presented a complication characterized by a bladder perforation with intrusion of the greater omentum through the bladder, along the ureter to the outside, to subsequently necrose and expose the patient to a risk of urinary peritonitis.