Miracle-dz/UniFAR-FTUs huggingface dataset:
RNNAccel: A Fusion Recurrent Neural Network Accelerator for Edge Intelligence

Many edge devices employ Recurrent Neural Networks (RNN) to enhance their product intelligence. However, the increasing computation complexity poses challenges for performance, energy efficiency and product development time. In this paper, we present an RNN deep learning accelerator, called RNNAccel, which supports Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, and Fully Connected Layer (FC)/ Multiple-Perceptron Layer (MLP) networks. This RNN accelerator addresses (1) computing unit utilization bottleneck caused by RNN data dependency, (2) inflexible design for specific applications, (3) energy consumption dominated by memory access, (4) accuracy loss due to coefficient compression, and (5) unpredictable performance resulting from processor-accelerator integration. Our proposed RNN accelerator consists of a configurable 32-MAC array and a coefficient decompression engine. The MAC array can be scaled-up to meet throughput requirement and power budget. Its sophisticated off-line compression and simple hardware-friendly on-line decompression, called NeuCompression, reduces memory footprint up to 16x and decreases memory access power. Furthermore, for easy SOC integration, we developed a tool set for bit-accurate simulation and integration result validation. Evaluated using a keyword spotting application, the 32-MAC RNN accelerator achieves 90% MAC utilization, 1.27 TOPs/W at 40nm process, 8x compression ratio, and 90% inference accuracy.
An ensemble algorithm for breast cancer histopathology image classification

Abstract Breast cancer is considered among prime reasons of deaths among women. Early detection can improve the survival rate and can decrease the mortality rate too. Though it’s a difficult and tedious task for pathologists. The motive of this research is to assess the efficiency and accuracy of various deep learning models and ensemble methods. Authors have proposed Stacked Generalized Ensemble algorithm that classifies the images into benign and malignant. The dataset used in experimentation is H&E breast cancer image dataset. Experimental results show that SGE has outperformed on various deep learning single classifiers. The evaluation criteria used for measuring the efficiency of algorithm is accuracy, precision, recall and F1 measure. A comparative analysis has been done with the existing deep learning methods. All the experimental results demonstrate that the Stacked Generalized Ensemble approach performed exponentially good on the histopathological breast cancer image classification and achieved an accuracy of 97.53%.
Using deep programmability to put network owners in control

Controlling an opaque system by reading some "dials" and setting some "knobs," without really knowing what they do, is a hazardous and fruitless endeavor, particularly at scale. What we need are transparent networks, that start at the top with a high-level intent and map all the way down, through the control plane to the data plane. If we can specify the behavior we want in software, then we can check that the system behaves as we expect. This is impossible if the implementation is opaque. We therefore need to use open-source software or write it ourselves (or both), and have mechanisms for checking actual behavior against the specified intent. With fine-grain checking (e.g., every packet, every state variable), we can build networks that are more reliable, secure, and performant. In the limit, we can build networks that run autonomously under verifiable, closed-loop control. We believe this vision, while ambitious, is finally within our reach, due to deep programmability across the stack, both vertically (control and data plane) and horizontally (end to end). It will emerge naturally in some networks, as network owners take control of their software and engage in open-source efforts; whereas in enterprise networks it may take longer. In 5G access networks, there is a pressing need for our community to engage, so these networks, too, can operate autonomously under verifiable, closed-loop control.
Desiging Social Play Things

Social play is a key factor in children’s positive development. Children with different interactional styles, such as neurodivergent children, face challenges to create opportunities for playing with peers to practice and acquire complex social skills, putting their social and emotional wellbeing at risk. We report on the design of social play technologies that aim to scaffold and support, in non-normative ways, co-located play of heterogeneous, neurodiverse groups of children aged 6 to 8. In three case studies we describe the participatory design processes that led to the development of three prototypical social play things. We report on the methodological challenges to design with these highly diverse groups and critically reflect on negotiating design decisions across abilities, agendas and conceptions of play within the process. Drawing on our successes and failures in these three cases, we synthesise design guidance for creating social play technologies for diverse groups of children.
Monoamniotic twins pregnancy complicated by a fetal congenital heart defect – a challenge for perinatal decisions

Abstract Objectives To analyze pregnancy management and postnatal follow-up in monochorionic monoamniotic (MCMA) pregnancies complicated by a congenital heart defect (CHD) in one twin and to compare the results with current recommendations concerning time and mode of delivery in MCMA pregnancies. Cases presentation Perinatal medical records of five pairs of monoamniotic twins referred to Fetal Cardiology Department were analyzed. 5 out of 23 MCMA pregnancies (21.7%) were complicated by CHD in one fetus. Cesarean section (CS) was performed between 32 and 35 weeks of gestation (WoG). 9 out of 10 neonates had respiratory failure, including all patients with CHD. Twins without congenital abnormalities spent median 21 days (range 10–40 days) in neonatal units. Patients with CHD were transferred to cardiology departments on average 6th day of life. All were operated on later than term-born neonates, 4 out of 5 required stage surgery and their median stay in the hospital was 75 days (range 48–106 days). Conclusions According to current recommendations, delivery in MCMA pregnancies should be scheduled at 32–34 weeks. In cases complicated by CHD in one twin, such early delivery complicates surgical treatment and may affect the final outcome. Low body weight and respiratory disorders increase the risk of complications in the perioperative period and prolong hospitalization.
ModelMine: a tool to facilitate mining models from open source repositories

Mining Software Repositories (MSR) has opened up new pathways and rich sources of data for research and practical purposes. This research discipline facilitates mining data from open source repositories and analyzing software defects, development activities, processes, patterns, and more. Contemporary mining tools are geared towards data extraction, analysis primarily from textual artifacts and have limitations in representation, ranking and availability. This paper presents ModelMine, a novel mining tool focuses on mining model-based artifacts and designs from open source repositories. ModelMine is designed particularly to mine software repositories, artifacts and commit history to uncover information about software designs and practices in open-source communities. ModelMine supports features that include identification and ranking of open source repositories based on the extent of presence of model-based artifacts and querying repositories to extract models and design artifacts based on customizable criteria. It supports phase-by-phase caching of intermediate results to speed up the processing to enable efficient mining of data. We compare ModelMine against a state-of-the-art tool named PyDriller in terms of performance and usability. The results show that ModelMine has the potential to become instrumental for cross-disciplinary research that combines modeling and design with repository mining and artifacts extraction. URL: https://www.smreza. com/projects/modelmine/
Secrecy

Without it, Masonry would cease to exist, or else become something so different from what it is as to be unrecognizable. For that reason, if no other, the very first lesson taught a candidate, and impressed upon him at every turn in unforgettable ways, is the duty of secrecy. Yet, strictly speaking, Masonry is not a secret society, if by that we mean a society whose very existence is hidden. Everybody knows that the Masonic Fraternity exists, and no effort is made to hide that fact. Its organization is known; its Temples stand in our cities; its members are proud to be know as Masons. Anyone may obtain from the records of a Grand Lodge, if not from the printed reports of Lodges, the names of the members of the Craft. Nor can it be said that Masonry has any secret truth to teach, unknown to the best wisdom of the race. Most of the talk about esoteric Masonry misses the mark. When the story is told the only secret turns out to be some odd theory, some fanciful philosophy, of no real importance. The wisdom of Masonry is hidden, not because it is subtle, but because it is simple. Its secret is profound, not obscure.
Neoleptosphaeria jonesii sp. nov., a novel saprobic sexual species, in Leptosphaeriaceae

Neoleptosphaeria is a genus of ascomycetes known only from its asexual morphs (coelomycetous) and its species have saprobic and / or endophytic life modes. We obtained LSU, SSU and ITS sequence data from a single spore isolation of a freshly collected specimen. A phylogeny of representative strains of the genus and other taxa in Leptosphaeriaceae was obtained. Neoleptosphaeria proved to be strongly monophyletic but related to other genera in Leptosphaeriaceae. Phylogenetic analyses place our new isolate in a strongly supported clade with the generic type of Neoleptosphaeria (N. rubefaciens). The sexual morph of Neoleptosphaeria is therefore established and includes the first genus with muriform ascospores in Leptosphaeriaceae.
Barriers Affecting the Oral Health of People Diagnosed with Depression: A Systematic Review

Abstract Introduction The problems of oral health of people diagnosed with depression are not adequately recognized, either in developed or developing countries. Social stigma, lack of self-interest, or even inadequate approaches of dental doctors towards the unique situation of this group of people this lead to excessive oral health problems. Methods The bibliographic database PubMed/Medline, Google Scholar, and Whiley online library were searched using the following text and MeSH as separate key terms and in combination: depression and oral health/dental caries/periodontal disease/tooth loss/utilization of oral health services/and barriers. The content of documents was analysed using qualitative methodology. Results Twenty-six original studies were included in the review. Level/severity of depression, medication and medical comorbidity are the most important medical barriers influencing the oral health of people diagnosed with depression. Dental fear and anxiety are mostly combined with low oral hygiene and bad oral health. Socioeconomic status, dental insurance, bad habits and education also have important roles in the oral health status of people diagnosed with depression. Conclusion Including individuals with depression and oral health problems in national health programs, creating specific prevention programs, or subsidizing the cost of treatment are some of the recommendations suggested as solutions.
Genetic and phenotypic variation in Lotus japonicus (Regel) K. Larsen, a model legume species

Mimura, M. 2013. Genetic and phenotypic variation inLotus japonicus(Regel) K. Larsen, a model legume species. Can. J. Plant Sci. 93: 435-444. Lotus japonicus is a model legume species with more than 90% of its gene space determined; however, its ecological and evolutionary background is little known. The genetic and phenotypic variation of this model species was investigated within the Japanese Archipelago, where it exists in various climates and has experienced repeated vegetative shifts in conjunction with historical climate changes, using nuclear microsatellite loci and common garden experiments. The partial Mantel test was performed to detect the influence of phylogeographic effects on phenotypic variation among accessions along environmental gradients. Western Japan showed more complex genetic population structures than northern Japan, which may reflect past population dynamics. The total biomass demonstrated clinal variation with a climatic variable (ClimatePC). The trend was significant in a partial Mantel test when controlling for genetic distance, which is independent of the environmental distance. This suggests adaptive divergence within the Japanese Archipelago. With highly accessible genome information, L. japonicus appears to be a promising species for future ecological and evolutionary studies.
Geometric Exploration for Online Control

We study the control of an \emph{unknown} linear dynamical system under general convex costs. The objective is minimizing regret vs. the class of disturbance-feedback-controllers, which encompasses all stabilizing linear-dynamical-controllers. In this work, we first consider the case of known cost functions, for which we design the first polynomial-time algorithm with $n^3\sqrt{T}$-regret, where $n$ is the dimension of the state plus the dimension of control input. The $\sqrt{T}$-horizon dependence is optimal, and improves upon the previous best known bound of $T^{2/3}$. The main component of our algorithm is a novel geometric exploration strategy: we adaptively construct a sequence of barycentric spanners in the policy space. Second, we consider the case of bandit feedback, for which we give the first polynomial-time algorithm with $poly(n)\sqrt{T}$-regret, building on Stochastic Bandit Convex Optimization.
Hyperparameter Transfer Across Developer Adjustments

After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many approaches exist that leverage knowledge obtained on previous tasks, so far, knowledge from previous development steps remains entirely untapped. In this work, we remedy this situation and propose a new research framework: hyperparameter transfer across adjustments (HT-AA). To lay a solid foundation for this research framework, we provide four simple HT-AA baseline algorithms and eight benchmarks changing various aspects of ML algorithms, their hyperparameter search spaces, and the neural architectures used. The best baseline, on average and depending on the budgets for the old and new HPO, reaches a given performance 1.2--2.6x faster than a prominent HPO algorithm without transfer. As HPO is a crucial step in ML development but requires extensive computational resources, this speedup would lead to faster development cycles, lower costs, and reduced environmental impacts. To make these benefits available to ML developers off-the-shelf and to facilitate future research on HT-AA, we provide python packages for our baselines and benchmarks.
A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses.

A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other possibilities for the nature of the dark matter, such as a warm thermal relic or a sterile neutrino (WDM) predict a cutoff in the mass function at a mass of $\sim 10^8~{\rm M}_\odot$. We use mock observations to demonstrate the viability of a forward modelling approach to extract information on the cosmological number density of low-mass dark matter haloes along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, $m_\mathrm{DM}$. With 50 strong lenses at Hubble Space Telescope resolution and signal-to-noise (similar to the existing SLACS survey), the expected 2$\sigma$ constraint for CDM is $m_\mathrm{DM} > 3.7 \, \mathrm{keV}$. If, however, the dark matter is a warm particle of $m_\mathrm{DM}=2.2 \, \mathrm{keV}$, one could rule out $m_\mathrm{DM} > 3.2 \, \mathrm{keV}$. Our [Approximate Bayesian Computation] method can be extended to the large samples of strong lenses that will be observed by future space telescopes, potentially to rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts).
ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. A generalized Gaussian distribution (GGD) is used to model band-pass responses, while entropy variations between reference and distorted videos under the GGD model are used to capture video quality variations arising from frame rate changes. The entropic differences are calculated across multiple temporal and spatial subbands, and merged using a learned regressor. We show through extensive experiments that GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models. The features used in GREED are highly generalizable and obtain competitive performance even on standard, non-HFR VQA databases. The implementation of GREED has been made available online: https://github.com/pavancm/GREED.
Secure and Efficient Trajectory-Based Contact Tracing using Trusted Hardware

The COVID-19 pandemic has prompted techno-logical measures to control the spread of the disease. Private contact tracing (PCT) is a promising technique for this purpose. However, the recently proposed Bluetooth-based PCT has several limitations in terms of functionality and flexibility. The existing systems are only able to detect direct contact (i.e., human-human contact) but cannot detect indirect contact (i.e., human-object, such as disease transmission through a surface). Moreover, the rule of risky contact cannot be flexibly changed with the environmental situation and the nature of the virus. In this paper, we propose a secure and efficient trajectory-based PCT system using trusted hardware. We formalize trajectory-based PCT as a generalization of the well-studied private set intersection (PSI), which is mostly based on cryptographic primitives and is thus insufficient. We solve the problem by leveraging trusted hardware such as Intel SGX and designing a novel algorithm to achieve a secure, efficient and flexible PCT system. Our experiments on real-world data show that the proposed system can achieve high performance and scalability. Specifically, our system (one single machine with Intel SGX) can process thousands of queries on 100 million records of trajectory data in a few seconds.
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions

Existing conversational systems are mostly agent-centric, which assumes the user utterances would closely follow the system ontology (for NLU or dialogue state tracking). However, in real-world scenarios, it is highly desirable that the users can speak freely in their own way. It is extremely hard, if not impossible, for the users to adapt to the unknown system ontology. In this work, we attempt to build a user-centric dialogue system. As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions. Learning such a mapping poses new challenges on reasoning over existing knowledge, ranging from factoid knowledge, commonsense knowledge to the users' own situations. To this end, we build a new dataset named NUANCED that focuses on such realistic settings for conversational recommendation. Collected via dialogue simulation and paraphrasing, NUANCED contains 5.1k dialogues, 26k turns of high-quality user responses. We conduct experiments, showing both the usefulness and challenges of our problem setting. We believe NUANCED can serve as a valuable resource to push existing research from the agent-centric system to the user-centric system. The code and data will be made publicly available.
Understanding Deep Learning: Expected Spanning Dimension and Controlling the Flexibility of Neural Networks

Neural networks (NN) provide state-of-the-art performance in many problem domains. They can accommodate a vast number of parameters and still perform well when classic machine learning techniques provided with the same number of parameters would tend to overfit. To further the understanding of such incongruities, we develop a metric called the expected spanning dimension (ESD) which allows one to measure the intrinsic flexibility of an NN. We analyze NNs from the small, in which the ESD can be exactly computed, to large real-world networks with millions of parameters, in which we demonstrate how the ESD can be numerically approximated efficiently. The small NNs we study can be understood in detail, their ESD can be computed analytically, and they provide opportunities for understanding their performance from a theoretical perspective. On the other hand, applying the ESD to large-scale NNs sheds light on their relative generalization performances and provides suggestions as to how such NNs may be improved.