Miracle-dz/UniFAR-FTUs huggingface dataset:
ZEXE: Enabling Decentralized Private Computation

Ledger-based systems that support rich applications often suffer from two limitations. First, validating a transaction requires re-executing the state transition that it attests to. Second, transactions not only reveal which application had a state transition but also reveal the application’s internal state.We design, implement, and evaluate ZEXE, a ledger-based system where users can execute offline computations and subsequently produce transactions, attesting to the correctness of these computations, that satisfy two main properties. First, transactions hide all information about the offline computations. Second, transactions can be validated in constant time by anyone, regardless of the offline computation.The core of ZEXE is a construction for a new cryptographic primitive that we introduce, decentralized private computation (DPC) schemes. In order to achieve an efficient implementation of our construction, we leverage tools in the area of cryptographic proofs, including succinct zero knowledge proofs and recursive proof composition. Overall, transactions in ZEXE are 968 bytes regardless of the offline computation, and generating them takes less than 1min plus a time that grows with the offline computation.We demonstrate how to use ZEXE to realize privacy-preserving analogues of popular applications: private user-defined assets and private decentralized exchanges for these assets.
Reasoning about POSIX file systems

POSIX is a standard for operating systems, with a substantial part devoted to specifying file-system operations. File-system operations exhibit complex concurrent behaviour, comprising multiple actions affecting different parts of the state: typically, multiple atomic reads followed by an atomic update. However, the standard’s description of concurrent behaviour is unsatisfactory: it is fragmented; contains ambiguities; and is generally under-specified. We provide a formal concurrent specification of POSIX file systems and demonstrate scalable reasoning for clients. Our specification is based on a concurrent specification language, which uses a modern concurrent separation logic for reasoning about abstract atomic operations, and an associated refinement calculus. Our reasoning about clients highlights an important difference between reasoning about modules built over a heap, where the interference on the shared state is restricted to the operations of the module, and modules built over a file system, where the interference cannot be restricted as the file system is a public namespace. We introduce specifications conditional on context invariants used to restrict the interference, and apply our reasoning to lock files and named pipes. Program reasoning based on separation logic has been successful at verifying that programs do not crash due to illegal use of resources, such invalid memory accesses. The underlying assumption of separation logics, however, is that machines do not fail. In practice, machines can fail unpredictably for various reasons, such as power loss, corrupting resources or resulting in permanent data loss. Critical software, such as file systems and databases, employ recovery methods to mitigate these effects. We introduce an extension of the Views framework to reason about programs in the presence of such events and their associated recovery methods. We use concurrent separation logic as an instance of the framework to illustrate our reasoning, and explore programs using write-ahead logging, such a stylised ARIES recovery algorithm.
Data-driven autocompletion for keyframe animation

We explore the potential of learned autocompletion methods for synthesizing animated motions from input keyframes. Our model uses an autoregressive two-layer recurrent neural network that is conditioned on target keyframes. The model is trained on the motion characteristics of example motions and sampled keyframes from those motions. Given a set of desired key frames, the trained model is then capable of generating motion sequences that interpolate the keyframes while following the style of the examples observed in the training corpus. We demonstrate our method on a hopping lamp, using a diverse set of hops from a physics-based model as training data. The model can then synthesize new hops based on a diverse range of keyframes. We discuss the strengths and weaknesses of this type of approach in some detail.
Aspect Based Sentiment Analysis into the Wild

In this paper, we test state-of-the-art Aspect Based Sentiment Analysis (ABSA) systems trained on a widely used dataset on actual data. We created a new manually annotated dataset of user generated data from the same domain as the training dataset, but from other sources and analyse the differences between the new and the standard ABSA dataset. We then analyse the results in performance of different versions of the same system on both datasets. We also propose light adaptation methods to increase system robustness.
A ladder to human-comparable intelligence: an empirical metric

ABSTRACT The field of Artificial General Intelligence needs a simple and understandable measure of progress to aid in passing the difficult Turing Test. The proposed Ladder Metric introduces a sequence of levels that intelligent agents must climb in order to reach the final, Turing indistinguishable, level. Just like the Turing test, the levels can only be defined in terms of general properties and behaviour rather than specific operational requirements. To track the progress and make comparisons one must climb and determine the achieved rung of the proposed ladder.
Evaluating Named-Entity Recognition approaches in plant molecular biology

Text mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge. However, few thorough studies on text mining and applications are developed for plant molecular biology data, especially rice, thus resulting a lack of datasets available to train models able to detect entities such as genes, proteins and phenotypic traits. Since there is rare benchmarks for rice, we have to face various difficulties in exploiting advanced machine learning methods for accurate analysis of rice bibliography. In this article, we developed a new training datasets (Oryzabase) as the benchmark. Then, we evaluated the performance of several current approaches to find a methodology with the best results and assigned it as the state of the art method for our own technique in the future. We applied Name Entities Recognition (NER) tagger, which is built from a Long Short Term Memory (LSTM) model, and combined with Conditional Random Fields (CRFs) to extract information of rice genes and proteins. We analyzed the performance of LSTM-CRF when applying to the Oryzabase dataset and improved the results up to 86% in F1. We found that on average, the result from LSTM-CRF is more exploitable with the new benchmark.
Learning to Recognize Actions with Weak Supervision. (Reconnaissance d'actions de manière faiblement supervisée)

With the rapid growth of digital video content, automatic video understanding has become an increasingly important task. Video understanding spans many possible applications such as web-video content analysis, autonomous vehicles, human-machine interfaces (e.g., Kinect or video surveillance). This thesis makes contributions addressing two major tasks in video understanding: webly-supervised action detection and human action local-
Manageability, availability and performance in Porcupine: a highly scalable, cluster-based mail service

This paper describes the motivation, design, and performance of Porcupine, a scalable mail server. The goal of Porcupine is to provide a highly available and scalable electronic mail service using a large cluster of commodity PCs. We designed Porcupine to be easy to manage by emphasizing dynamic load balancing, automatic configuration, and graceful degradation in the presence of failures. Key to the system's manageability, availability, and performance is that sessions, data, and underlying services are distributed homogeneously and dynamically across nodes in a cluster.
opt\_einsum - A Python package for optimizing contraction order for einsum-like expressions

einsum is a powerful Swiss army knife for arbitrary tensor contractions and general linear algebra found in the popular numpy (Walt, Colbert, and Varoquaux 2011) package. While these expressions can be used to form most mathematical operations found in NumPy, the optimization of these expressions becomes increasingly important as naive implementations increase the overall scaling of these expressions resulting in a dramatic increase in overall execution time. Expressions with many tensors are particularly prevalent in many-body theories such as quantum chemistry, particle physics, and nuclear physics in addition to other fields such as machine learning. At the extreme case, matrix product state theory can have thousands of tensors meaning that the computation cannot procede in a naive fashion.
‘Optimism bias’ in contemporary national clinical trial network phase III trials: are we improving?

Background Previous studies have found that overestimating treatment effects (i.e. 'optimism bias') leads to underpowered clinical trials. The prevalence of 'optimism bias' in contemporary National Clinical Trials Network (NCTN) cancer clinical trials is unknown. Methods We conducted a systematic review of NCTN phase III randomized trials published from January 2007 to January 2017. We compared the hypothesized versus observed treatment effects in each trial, and examined whether trial-related factors were correlated with the study results. We also reviewed the methods of each protocol to assess whether a rationale for the hypothesized effect size was provided. Results We identified 161 clinical trials, of which 130 were eligible for analysis. Original protocols could not be located for 8 trials (5.0%). Twenty-eight trials (21.5%) observed a statistically significant difference in the primary end point favoring the experimental treatment. The median ratio of observed-to-expected hazard ratios among trials that observed a statistically significant effect on the primary end point was 1.07 (range: 0.33-1.28) versus 1.32 (range: 0.86-2.02) for trials that did not, compared with 1.34 and 1.86, respectively, for National Cancer Institute (NCI) trials published between 1955 and 2006. An effect size at least as large as the one projected in the protocol trials was observed in 9.8% of trials, compared with 17% of NCI trials published from 1955 to 2006. Most trials (64.6%) provided no rationale to support the magnitude of the proposed treatment effect in the protocol. Conclusions Despite a reduction in 'optimism bias' compared with previous eras, most contemporary NCTN phase III trials failed to establish statistically significant benefits of new cancer therapies. Better rationalization of proposed effect sizes in research protocols is needed.
Study of Internet addiction in children with attention-deficit hyperactivity disorder and normal control

Background: In the current era, the use of electronic media in the form of Internet has increased exponentially, particularly among children, and has led to their excessive involvement in Internet. In this context, attention-deficit hyperactivity disorder (ADHD) children were found to have increased tendency for this addiction. Aims and Objectives: The aim is to study and compare Internet addiction between ADHD and normal children and the relation of demographic profile to Internet addiction. Materials and Methods: This was a cross-sectional study including 100 children (50 ADHD cases and 50 normal children without any psychiatric illness as controls) between the ages of 8 and 16 years. A semi-structured pro forma for demographic profile and Internet usage using Young's Internet Addiction Test (YIAT) was used. Statistical analysis was done using SPSS 20. Results: Internet addiction among ADHD children was 56% (54% having “probable Internet addiction” and 2% having “definite Internet addiction”). This was statistically significant (P < 0.05) in comparison with normal children where only 12% had Internet addiction (all 12% had “probable Internet addiction”). ADHD children were 9.3 times more prone to the development of Internet addiction as compared to normal (odds ratio – 9.3). Significant increase in average duration of Internet usage in ADHD children with increasing score of YIAT (P < 0.05) was seen. The incidence of Internet addiction was more in male ADHD children as compared to normal (P < 0.05). Conclusions: ADHD children are more prone to Internet addiction as compared to normal children and thus require preventive strategies.
A low‐power digital IC emulating intracellular calcium dynamics

Low power/area cytomorphic chips may be interfaced and ultimately implanted in the human body for cell‐sensing and cell‐control applications of the future. In such electronic platforms, it is crucial to accurately mimic the biological timescales and operate in real time. This paper proposes a methodology where slow nonlinear dynamical systems describing the behaviour of naturally encountered biological systems can be efficiently realised in hardware. To this end, as a case study, a low power and efficient digital Application‐Specific Integrated Circuit capable of emulating slow intracellular calcium dynamics with timescales reaching to seconds has been fabricated in the commercially available AMS 0.35 μm technology and compared with its analog counterpart. The fabricated chip occupies an area of 1.5 mm2 (excluding the area of the pads) and consumes 18.93 nW for each calcium unit from a power supply of 3.3 V. The presented cytomimetic topology follows closely the behaviour of its biological counterpart, exhibiting similar time‐domain calcium ions dynamics. Results show that the implemented design has the potential to speed up large–scale simulations of slow intracellular dynamics by sharing cellular units in real time.
Comparison of cluster validation indices with missing data

Clustering is an unsupervised machine learning technique, which aims to divide a given set of data into subsets. The number of hidden groups in cluster analysis is not always obvious and, for this purpose, various cluster validation indices have been suggested. Recently some studies reviewing validation indices have been provided, but any experiments against missing data are not yet available. In this paper, performance of ten well-known indices on ten synthetic data sets with various ratios of missing values is measured using squared euclidean and city block distances based clustering. The original indices are modiﬁed for a city block distance in a novel way. Experiments illustrate the diﬀerent degree of stability for the indices with respect to the missing data.
comorbidity: An R package for computing comorbidity scores

Comorbidity scores are extensively used in observational medical research studies to avoid potential bias when the burden of disease could be confounding the association of interest. This is of primary importance, given the increasing availability and use of administrative data (such as medical records and insurance claims) for research purposes. Several comorbidity scores have been proposed throughout the years; however, two of them are used most frequently in practice: the Charlson comorbidity index (Charlson et al. 1987) and the Elixhauser comorbidity index (Elixhauser et al. 1998). The Charlson comorbidity index defines a set of comorbid conditions using International Classification of Disease (ICD) diagnostic codes. Each comorbid condition has an associated weight, and the sum of all weights results in a single comorbidity score per patient. The current version of the Charlson score includes 17 comorbidities. Similarly, the Elixhauser comorbidity index is based on ICD diagnostic codes and includes 31 comorbidities. In origin, the Elixhauser index score was based on the cumulative number of conditions present; since then, several weighting systems accounting for the increase or decrease in mortality risk associated with each condition have been proposed and used in practice (van Walraven et al. 2009; Moore et al. 2017).
dit: a Python package for discrete information theory

dit(“Dit: A Python Package for Discrete Information Theory. Available at: Https://Github.com/Dit/Dit” n.d.) is a Python package for the study of discrete information theory. Information theory is a mathematical framework for the study of quantifying, compressing, and communicating random variables (Cover and Thomas 2006)(MacKay 2003)(Yeung 2008). More recently, information theory has been utilized within the physical and social sciences to quantify how different components of a system interact. dit is primarily concerned with this aspect of the theory.
Exponential linear unit dilated residual network for digital image denoising

Abstract. Over the past couple of years, deep learning in convolutional neural networks (CNNs) has proven to be extremely powerful in image processing tasks including image denoising. We deal with digital image denoising based on deep learning. In this regard, dilated CNNs have been successful in achieving impressive denoising quality by obtaining a considerably effective trade-off between receptive field size and network depth, and thus reducing the computational intensity of the CNN architecture. Such networks in general use rectifier linear units as the activation function for introducing nonlinearity into the network. We propose a dilated CNN with exponential linear units for image denoising. Our aim is to enhance the performance of dilated networks in image denoising and develop an effective denoising CNN architecture by optimizing computational intensity. We experiment with a (conventional) 10-layer network, as well as a 5-layer version of the proposed model. It is observed that the proposed 5-layer network succeeds to achieve state-of-the-art denoised image quality as well as high computational efficiency. Our experimental results prove the efficiency of the proposed models in image denoising as well as complexity reduction.
Long-Term Cryopreservation and Revival of Tissue-Engineered Skeletal Muscle.

IMPACT STATEMENT The ability to freeze, revive, and prolong the lifetime of tissue-engineered skeletal muscle without incurring any loss of function represents a significant advancement in the field of tissue engineering. Cryopreservation enables the efficient fabrication, storage, and shipment of these tissues. This in turn facilitates multidisciplinary collaboration between research groups, enabling advances in skeletal muscle regenerative medicine, organ-on-a-chip models of disease, drug testing, and soft robotics. Furthermore, the observation that freezing undifferentiated skeletal muscle enhances functional performance may motivate future studies developing stronger and more clinically relevant engineered muscle.
Motif and Hypergraph Correlation Clustering

Motivated by applications in social and biological network analysis we introduce a new form of agnostic clustering termed motif correlation clustering, which aims to minimize the cost of clustering errors associated with both edges and higher-order network structures. The problem may be succinctly described as follows: Given a complete graph $G$ , partition the vertices of the graph so that certain predetermined “important” subgraphs mostly lie within the same cluster, while “less relevant” subgraphs are allowed to lie across clusters. Our contributions are as follows: We first introduce several variants of motif correlation clustering and then show that these clustering problems are NP-hard. We then proceed to describe polynomial-time clustering algorithms that provide constant approximation guarantees for the problems at hand. Despite following the frequently used LP relaxation and rounding procedure, the algorithms involve a sophisticated and carefully designed neighborhood growing step that combines information about both edges and motifs. We conclude with several examples illustrating the performance of the developed algorithms on synthetic and real networks.
A Multithreaded Initial Detailed Routing Algorithm Considering Global Routing Guides

Detailed routing is the most complicated and time-consuming stage in VLSI design and has become a critical process for advanced node enablement. To handle the high complexity of modern detailed routing, initial detailed routing is often employed to minimize design-rule violations to facilitate final detailed routing, even though it is still not violation-free after initial routing. This paper presents a novel initial detailed routing algorithm to consider industrial design-rule constraints and optimize the total wirelength and via count. Our algorithm consists of three major stages: (1) an effective pin-access point generation method to identify valid points to model a complex pin shape, (2) a via-aware track assignment method to minimize the overlaps between assigned wire segments, and (3) a detailed routing algorithm with a novel negotiation-based rip-up and re-route scheme that enables multithreading and honors global routing information while minimizing design-rule violations. Experimental results show that our router outperforms all the winning teams of the 2018 ACM ISPD Initial Detailed Routing Contest, where the top-3 routers result in 23%, 52%, and 1224% higher costs than ours.
Security for Safety: A Path Toward Building Trusted Autonomous Vehicles

Automotive systems have always been designed with safety in mind. In this regard, the functional safety standard, ISO 26262, was drafted with the intention of minimizing risk due to random hardware faults or systematic failure in design of electrical and electronic components of an automobile. However, growing complexity of a modern car has added another potential point of failure in the form of cyber or sensor attacks. Recently, researchers have demonstrated that vulnerability in vehicle's software or sensing units could enable them to remotely alter the intended operation of the vehicle. As such, in addition to safety, security should be considered as an important design goal. However, designing security solutions without the consideration of safety objectives could result in potential hazards. Consequently, in this paper we propose the notion of security for safety and show that by integrating safety conditions with our system-level security solution, which comprises of a modified Kalman filter and a Chi-squared detector, we can prevent potential hazards that could occur due to violation of safety objectives during an attack. Furthermore, with the help of a car-following case study, where the follower car is equipped with an adaptive-cruise control unit, we show that our proposed system-level security solution preserves the safety constraints and prevent collision between vehicle while under sensor attack.
Shallow and Deep Models for Domain Adaptation problems

Manual labeling of suﬃcient training data for diverse application domains is a costly, laborious task and often prohibitive. Therefore, designing models that can leverage rich labeled data in one domain and be applicable to a diﬀerent but related domain is highly desirable. In particular, domain adaptation or transfer learning algorithms seek to generalize a model trained in a source domain to a new target domain. Recent years has witnessed increasing interest in these types of models due to their practical importance in real-life applications. In this paper we provide a brief overview of recent techniques with both shallow and deep architectures for domain adaptation models.