Miracle-dz/UniFAR-FTUs huggingface dataset:
OREBA: A Dataset for Objectively Recognizing Eating Behavior and Associated Intake

Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of labeled sensor data to automatically learn how to make detections. One characteristic, especially for deep learning models, is the need for large datasets. To meet this need, we collected the Objectively Recognizing Eating Behavior and Associated Intake (OREBA) dataset. The OREBA dataset aims to provide comprehensive multi-sensor data recorded during the course of communal meals for researchers interested in intake gesture detection. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consist of synchronized frontal video and IMU with accelerometer and gyroscope for both hands. We report the details of data collection and annotation, as well as details of sensor processing. The results of studies on IMU and video data involving deep learning models are reported to provide a baseline for future research. Specifically, the best baseline models achieve performances of $F_{1}=0.853$ for the discrete dish using video and $F_{1}=0.852$ for the shared dish using inertial data.
Ultra-light deep MIR by trimming lottery tickets

Current state-of-the-art results in Music Information Retrieval are largely dominated by deep learning approaches. These provide unprecedented accuracy across all tasks. However, the consistently overlooked downside of these models is their stunningly massive complexity, which seems concomitantly crucial to their success. In this paper, we address this issue by proposing a model pruning method based on the lottery ticket hypothesis. We modify the original approach to allow for explicitly removing parameters, through structured trimming of entire units, instead of simply masking individual weights. This leads to models which are effectively lighter in terms of size, memory and number of operations. We show that our proposal can remove up to 90% of the model parameters without loss of accuracy, leading to ultra-light deep MIR models. We confirm the surprising result that, at smaller compression ratios (removing up to 85% of a network), lighter models consistently outperform their heavier counterparts. We exhibit these results on a large array of MIR tasks including audio classification, pitch recognition, chord extraction, drum transcription and onset estimation. The resulting ultra-light deep learning models for MIR can run on CPU, and can even fit on embedded devices with minimal degradation of accuracy.
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation

This study presents a systematic comparison of methods for individual treatment assignment. We group the various methods proposed in the literature into three general classes of algorithms (or metalearners): learning models to predict outcomes (the O-learner), learning models to predict causal effects (the E-learner), and learning models to predict optimal treatment assignments (the A-learner). We discuss how the metalearners differ in their level of generality and their objective function, which has critical implications for modeling and decision making. Notably, we demonstrate that optimizing for the prediction of outcomes or causal effects is not the same as optimizing for treatment assignments, suggesting that, in general, the A-learner should lead to better treatment assignments than the other metalearners. We then compare the metalearners in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the three different metalearners on a real-world application at scale (based on more than half a billion treatment assignments). In addition to supporting our analytical findings, the results show how large A/B tests can provide substantial value for learning treatment-assignment policies, rather than simply choosing the variant that performs best on average.
Universal relation between the static structure factor and the condensate fraction

We present an analytical method to estimate the condensate fraction $n_0/n$ in strongly correlated systems for which the zero-temperature static structure factor $S(p)$ is known. The advantage of the proposed method is that it allows to predict the long-range behavior of the one-body density matrix (i) in macroscopic and mesoscopic systems, (ii) in three- and two-dimensional geometry, (iii) at zero and low finite temperature, and (iv) in weakly and strongly correlated regimes. Our method is tested against exact values obtained with quantum Monte Carlo methods in a number of strongly correlated systems showing an excellent agreement. The proposed technique is also useful in numerical simulations as it allows to extrapolate the condensate fraction to the thermodynamic limit for particle numbers as small as hundreds. Our method is especially valuable for extracting the condensate fraction from the experimentally measured static structure factor $S(p)$, thus providing a new simple alternative technique for the estimation of $n_0/n$. We analyze available experimental data for $S(p)$ of superfluid helium and find an excellent agreement with the experimental value of $n_0/n$.
Baryonic Feedback Measurement From KV450 Cosmic Shear Analysis

While baryonic feedback is one of the most important astrophysical systematics that we need to address in order to achieve precision cosmology, few weak lensing studies have directly measured its impact on the matter power spectrum. We report measurement of the baryonic feedback parameter with the constraints on its lower and upper limits from cosmic shear. We use the public data from the Kilo-Degree Survey and the VISTA Kilo-Degree Infrared Galaxy Survey spanning 450 deg2. Estimating both cosmological and feedback parameters simultaneously, we obtain A b = 1.01 − 0.85 + 0.80 , which shows a consistency with the dark matter-only (DMO) case at the ∼1. 2σ level and a tendency toward positive feedback; the Ab = 0 (0.81) value corresponds to the DMO (OWLS AGN) case. Despite this full constraint of the feedback parameter, our S 8 ( ≡ σ 8 Ω m / 0.3 ) measurement ( 0.739 − 0.035 + 0.036 ) shifts by only ∼6% of the statistical error, compared to the previous measurement. When we assume the flat ΛCDM cosmology favored by the Nine-Year Wilkinson Microwave Anisotropy Probe (Planck) result, the feedback parameter is constrained to be A b = 1.21 − 0.54 + 0.61 ( 1.60 − 0.52 + 0.53 ), which excludes the DMO case at the ∼2. 2σ (∼3.1σ) level.
X-Ray Spectroscopy in the Microcalorimeter Era. II. A New Diagnostic on Column Density from the Case A to B Transition in H- and He-like Iron

The Soft X-ray Spectrometer on board Hitomi, with the unprecedented resolving power of R ∼ 1250, allowed the detection of members of the Fe xxv Kα complex emission spectra from the center of the Perseus Cluster. In this paper, we introduce a novel method of measuring the column density using the optically thin (Case A) to optically thick (Case B) transition for one- and two-electron systems. We compare the Fe xxv K α line ratios computed with CLOUDY with that from the Hitomi observations in the outer region of the Perseus core using collision strengths from different atomic data sets, and obtain good agreement. We also show the effect of turbulence on Fe xxv Kα line ratios and interplay between column density and metallicity. Additionally, we discuss the atomic number dependence of transition probabilities for allowed and unallowed transitions, which causes highly charged He-like systems, such as Fe xxv, to behave fundamentally differently from He i.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://aka.ms/BLURB.
A Novel Emergency Light Based Smart Building Solution: Design, Implementation, and Use Cases

Deployment of the Internet of Things (IoT) in smart buildings has received considerable interest from both the academic community and commercial sectors. Unfortunately, the widespread adoption of current smart building solutions is inhibited by the high costs associated with installation and maintenance. Moreover, different types of IoT devices from different manufacturers typically form distinct networks and data silos. There is a need to use a common backbone network that facilitates interoperability and seamless data exchange in a uniform way. In this paper, we present EMIoT, a novel solution for smart buildings that breaks these barriers by leveraging existing emergency lighting systems. In EMIoT, we embed a wireless LoRa module in each emergency light to turn them into wireless routers. EMIoT has been deployed in more than 50 buildings of different types in Sydney, Australia and has been successfully running over two years. We present the design and implementation of EMIoT in this paper. Moreover, we use the deployment in a residential building as a use case to show the performance of EMIoT in real-world environments and share lessons learned. Finally, we discuss the advantages and disadvantages of EMIoT. This paper provides practical insights for IoT deployment in smart buildings for practitioners and solution providers.
Gamma-rays from ultracompact minihaloes: effects on the Earth’s atmosphere and links to mass extinction events

Recent studies of the effects on the Earth's atmosphere by astrophysical sources, such as nearby gamma-ray bursts or supernovae, have shown that these events could lead to severe changes in atmospheric composition. Depletion of ozone, the most notable of these changes, is extremely dangerous to living organisms as any decrease in ozone levels leads to an increase in the irradiance of harmful solar radiation at the Earth's surface. In this work we consider dark matter as an astrophysical source of gamma rays, by the annihilation and decay of WIMPs found within dark compact halo objects known as UltraCompact Minihaloes (UCMHs). We calculate the fluence of gamma rays produced in this way and simulate the resulting changes to terrestrial ozone levels using the Goddard Space Flight Center 2D Atmospheric Model. We also calculate the rate at which such events would occur, using estimates for the mass distribution of these haloes within the Milky Way. We find that the ozone depletion from UCMHs can be significant, and even of similar magnitude to the levels which have been linked to the cause of the Late-Ordovician mass extinction event. However, the probability of such encounters over the Earth's entire history is relatively low. This suggests that, while dark compact objects like UCMHs could have had an impact on the Earth's biosphere, other astrophysical phenomena like gamma-ray bursts or supernovae seem a more likely source of these effects.
COLD: Towards the Next Generation of Pre-Ranking System

Multi-stage cascade architecture exists widely in many industrial systems such as recommender systems and online advertising, which often consists of sequential modules including matching, pre-ranking, ranking, etc. For a long time, it is believed pre-ranking is just a simplified version of the ranking module, considering the larger size of the candidate set to be ranked. Thus, efforts are made mostly on simplifying ranking model to handle the explosion of computing power for online inference. In this paper, we rethink the challenge of the pre-ranking system from an algorithm-system co-design view. Instead of saving computing power with restriction of model architecture which causes loss of model performance, here we design a new pre-ranking system by joint optimization of both the pre-ranking model and the computing power it costs. We name it COLD (Computing power cost-aware Online and Lightweight Deep pre-ranking system). COLD beats SOTA in three folds: (i) an arbitrary deep model with cross features can be applied in COLD under a constraint of controllable computing power cost. (ii) computing power cost is explicitly reduced by applying optimization tricks for inference acceleration. This further brings space for COLD to apply more complex deep models to reach better performance. (iii) COLD model works in an online learning and severing manner, bringing it excellent ability to handle the challenge of the data distribution shift. Meanwhile, the fully online pre-ranking system of COLD provides us with a flexible infrastructure that supports efficient new model developing and online A/B testing.Since 2019, COLD has been deployed in almost all products involving the pre-ranking module in the display advertising system in Alibaba, bringing significant improvements.
PHYLOGENETIC DIVERSITY IN SHIITAKE INFERRED FROM NUCLEAR RIBOSOMAL DNA SEQUENCES

Phylogenetic relationships of the edible shi? itake mushroom (Lentinula, Tricholomataceae) were studied using DNA sequences from the internal tran? scribed spacers (ITS) of nuclear ribosomal DNA. The ingroup consisted of seven isolates of L. edodes from Japan and Thailand, nine isolates of L. lateritia from Borneo, Papua New Guinea, and Tasmania, and five isolates of L. novaezelandieae from New Zealand. These species designations are based on morphological spe? cies concepts in Lentinula. However, because Lentinula isolates from throughout Asia-Australasia are mating compatible, some authors treat all of these as belong- ing to L. edodes. The outgroup included two isolates of L. boryana from Mexico. Parsimony, distance, and maximum likelihood analyses were performed, with various combinations of taxa, characters, and char? acter codings, and bootstrap and decay index mea- sures of robustness. Alternate topologies were evalu? ated in terms of tree lengths, maximum likelihood ratios, and Templeton's nonparametric test of parsi? mony. Results suggest that there are four independent lineages of shiitake in Asia-Australasia, which provides partial support for the morphologically based species concepts. Lentinula novaezelandieae and L. edodes sensu
Dietetic intervention in haemodialysis patients improves malnutrition

Objective: Protein-energy malnutrition is an independent predictor of morbidity and mortality in dialysis patients with a prevalence as high as 75%. It is associated with poor dialysis outcomes, including decreased quality of life, increased hospital admissions and increased mortality. We hypothesised that regular dietetic input following evidence-based guidelines would reduce malnutrition and improve patient health. Design and Methods: Two specialist renal dietitians reviewed patients at a single satellite dialysis unit in Perth over a seven-month period in 2015. Demographic, laboratory and malnutrition data using the Subjective Global Assessment (SGA) were collated at baseline and follow-up. One-to-one, individualised dietetic education was given to patients during the seven-month period. Paired t-test and Wilcoxon signed rank tests were used to assess differences between baseline and follow-up results. Results: One hundred and fifty-two patients were recruited. At baseline, mean age was 71.0+-12.4 years and 63% were male. Considerable malnutrition was noted at baseline (46%) with mean hand grip strength of 23.7+-12.7 kg ( Conclusions: Dietetic intervention in haemodialysis patients reduced rates of malnutrition, underlining the value of dietetic input in this setting.
Microorganisms associated with computer keyboards and mice in a university environment

A study was carried out to isolate and to identify microorganisms associated with computer keyboards and mice in computer centers and cyber cafe located in Kogi State University, Anyigba, Nigeria and its environs. Samples were collected from five different cyber cafe and computer centers. The samples were collected from three computer keyboards and mice in each cyber cafe and computer centers. The collected samples were inoculated on nutrient agar, MacConkey agar and potato dextrose agar by following standard methods. The isolates obtained were examined and identified by colonial morphology, Gram reaction and biochemical characteristics. Four bacterial and four fungal species were isolated from the samples. The bacterial isolates include Staphylococcus aureus, Enterococcus sp. , Staphylococcus epidermidis and Streptococcus sp. The fungal isolates are as follows Aspergillus sp. , Mucor sp. , Penicillum sp. and Rhizopus sp. These microorganisms have pathogenic potential and hence their presence on such surfaces (computer keyboards and mice) may be additional reservoirs for the transmission of microorganisms and become vectors for cross-transmission of bacterial and fungal infections in the university and its environs.
Phenomenology and scaling of optimal flapping wing kinematics

Biological flapping wing fliers operate efficiently and robustly in a wide range of flight conditions and are a great source of inspiration to engineers. The unsteady aerodynamics of flapping wing flight are dominated by large-scale vortical structures that augment the aerodynamic performance but are sensitive to minor changes in the wing actuation. We experimentally optimise the pitch angle kinematics of a flapping wing system in hover to maximise the stroke average lift and hovering efficiency with the help of an evolutionary algorithm and in situ force and torque measurements at the wing root. Additional flow field measurements are conducted to link the vortical flow structures to the aerodynamic performance for the Pareto-optimal kinematics. The optimised pitch angle profiles yielding maximum stroke-average lift coefficients have trapezoidal shapes and high average angles of attack. These kinematics create strong leading-edge vortices early in the cycle which enhance the force production on the wing. The most efficient pitch angle kinematics resemble sinusoidal evolutions and have lower average angles of attack. The leading-edge vortex grows slower and stays close-bound to the wing throughout the majority of the stroke-cycle. This requires less aerodynamic power and increases the hovering efficiency by 93% but sacrifices 43% of the maximum lift in the process. In all cases, a leading-edge vortex is fed by vorticity through the leading edge shear layer which makes the shear layer velocity a good indicator for the growth of the vortex and its impact on the aerodynamic forces. We estimate the shear layer velocity at the leading edge solely from the input kinematics and use it to scale the average and the time-resolved evolution of the circulation and the aerodynamic forces. The experimental data agree well with the shear layer velocity prediction, making it a promising metric to quantify and predict the aerodynamic performance of the flapping wing hovering motion.
Arabinogalactan-proteins in the evolution of gravity resistance in land plants.

The cell walls of land plants developed under the influence of earth's gravity. Arabinogalactan-proteins (AGPs) are a family of proteoglycans that localize on plasma membranes and in cell walls of higher plants. Recent studies have revealed that the expression levels of genes encoding the core proteins of AGPs are modified by hypergravity, indicating the involvement of AGPs in gravity resistance. A BLAST search in the genome databases of various organisms for genes encoding proteins related to fasciclin-like AGPs (FLAs), found FLAs in land plants including a moss, Physcomitrella patens subsp. patens, but not in the green algae, Chlamydomonas reinhardtii or Volvox carteri. On the other hand, the backbone structure of arabinogalactan moieties of AGPs, β-1,3:1,6-galactan, is widely distributed among organisms and has been confirmed in a species of Chlorellaceae, a snail and a mammal. These facts suggest that acquisition of some AGPs similar to those currently found, and FLAs in particular, was important in the evolution of the resistance of plants to gravitational force. By studying the molecular functions of AGPs under diverse gravitational conditions, we should be able to deepen our understanding of the evolutional process that turned aquatic organisms into terrestrial plants.
MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs

Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strategies. However, directly applying grey-box fuzzing to input-dependent multithreaded programs can be extremely inefficient. In practice, multithreading-relevant bugs are usually buried in sophisticated program flows. Meanwhile, the existing grey-box fuzzing techniques do not stress thread-interleavings which affect execution states in multithreaded programs. Therefore, mainstream grey-box fuzzers cannot effectively test problematic segments in multithreaded programs despite they might obtain high code coverage statistics. To this end, we propose MUZZ, a new grey-box fuzzing technique that hunts for bugs in multithreaded programs. MUZZ owns three novel thread-aware instrumentations, namely coverage-oriented instrumentation, thread-context instrumentation, and schedule-intervention instrumentation. During fuzzing, these instrumentations engender runtime feedback to stress execution states caused by thread interleavings. By leveraging the feedback in the dynamic seed selection and execution strategies, MUZZ preserves more valuable seeds that expose bugs in a multithreading context. We evaluate MUZZ on 12 real-world software programs. Experiments show that MUZZ outperforms AFL in both multithreading-relevant seed generation and concurrency-vulnerability detection. Further, by replaying the target programs against the generated seeds, MUZZ also reveals more concurrency-bugs (e.g., data-races, thread-leaks) than AFL. In total, MUZZ detected 8 new concurrency-vulnerabilities and 19 new concurrency-bugs. At the time of writing, 4 CVE IDs have been assigned to the reported issues.