Miracle-dz/UniFAR-FTUs huggingface dataset:
Ticking clocks as dependent right adjoints: Denotational semantics for clocked type theory

Clocked Type Theory (CloTT) is a type theory for guarded recursion useful for programming with coinductive types, allowing productivity to be encoded in types, and for reasoning about advanced programming language features using an abstract form of step-indexing. CloTT has previously been shown to enjoy a number of syntactic properties including strong normalisation, canonicity and decidability of the equational theory. In this paper we present a denotational semantics for CloTT useful, e.g., for studying future extensions of CloTT with constructions such as path types. The main challenge for constructing this model is to model the notion of ticks on a clock used in CloTT for coinductive reasoning about coinductive types. We build on a category previously used to model guarded recursion with multiple clocks. In this category there is an object of clocks but no object of ticks, and so tick-assumptions in a context can not be modelled using standard tools. Instead we model ticks using dependent right adjoint functors, a generalisation of the category theoretic notion of adjunction to the setting of categories with families. Dependent right adjoints are known to model Fitch-style modal types, but in the case of CloTT, the modal operators constitute a family indexed internally in the type theory by clocks. We model this family using a dependent right adjoint on the slice category over the object of clocks. Finally we show how to model the tick constant of CloTT using a semantic substitution. This work improves on a previous model by the first two named authors which not only had a flaw but was also considerably more complicated.
Efficient Estimation for Generalized Linear Models on a Distributed System with Nonrandomly Distributed Data

Distributed systems have been widely used in practice to accomplish data analysis tasks of huge scales. In this work, we target on the estimation problem of generalized linear models on a distributed system with nonrandomly distributed data. We develop a Pseudo-Newton-Raphson algorithm for efficient estimation. In this algorithm, we first obtain a pilot estimator based on a small random sample collected from different Workers. Then conduct one-step updating based on the computed derivatives of log-likelihood functions in each Worker at the pilot estimator. The final one-step estimator is proved to be statistically efficient as the global estimator, even with nonrandomly distributed data. In addition, it is computationally efficient, in terms of both communication cost and storage usage. Based on the one-step estimator, we also develop a likelihood ratio test for hypothesis testing. The theoretical properties of the one-step estimator and the corresponding likelihood ratio test are investigated. The finite sample performances are assessed through simulations. Finally, an American Airline dataset is analyzed on a Spark cluster for illustration purpose.
Modeling multicellular dynamics regulated by extracellular-matrix-mediated mechanical communication via active particles with polarized effective attraction.

Collective cell migration is crucial to many physiological and pathological processes such as embryo development, wound healing, and cancer invasion. Recent experimental studies have indicated that the active traction forces generated by migrating cells in a fibrous extracellular matrix (ECM) can mechanically remodel the ECM, giving rise to bundlelike mesostructures bridging individual cells. Such fiber bundles also enable long-range propagation of cellular forces, leading to correlated migration dynamics regulated by the mechanical communication among the cells. Motivated by these experimental discoveries, we develop an active-particle model with polarized effective attractions (APPA) to investigate emergent multicellular migration dynamics resulting from ECM-mediated mechanical communications. In particular, the APPA model generalizes the classic active-Brownian-particle (ABP) model by imposing a pairwise polarized attractive force between the particles, which depends on the instantaneous dynamic states of the particles and mimics the effective mutual pulling between the cells via the fiber bundle bridge. The APPA system exhibits enhanced aggregation behaviors compared to the classic ABP system, and the contrast is more apparent at lower particle densities and higher rotational diffusivities. Importantly, in contrast to the classic ABP system where the particle velocities are not correlated for all particle densities, the high-density phase of the APPA system exhibits strong dynamic correlations, which are characterized by the slowly decaying velocity correlation functions with a correlation length comparable to the linear size of the high-density phase domain (i.e., the cluster of particles). The strongly correlated multicellular dynamics predicted by the APPA model is subsequently verified in in vitro experiments using MCF-10A cells. Our studies indicate the importance of incorporating ECM-mediated mechanical coupling among the migrating cells for appropriately modeling emergent multicellular dynamics in complex microenvironments.
Neurosciences and 6G: Lessons from and Needs of Communicative Brains

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains: Neurosciences and wireless communications, motivated by the ongoing efforts to define how the sixth generation of mobile networks (6G) will be. In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two, seemingly disparate fields. Then, we present the state-of-the-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to 6G in terms of new applications and systems architecture (Neurosciences for Wireless), and the other focused on how wireless communication theory and 6G systems can provide new ways to study the brain (Wireless for Neurosciences). For the first group, we concretely explain how current scientific understanding of the brain would enable new application for 6G within the context of a new type of service that we dub braintype communications and that has more stringent requirements than human- and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and we discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication system paradigms, including Internet of Bio-nano Things and chaosbased communications, in addition to highlighting how complex systems tools can help bridging 6G and neuroscience applications. Brain-controlled vehicles are then presented as our case study. All in all, this tutorial is expected to provide a largely missing articulation between these two emerging fields while delineating concrete ways to move forward in such an interdisciplinary endeavor.
On the Efficient Design of LSM Stores

In the last decade, key-value data storage systems have gained significantly more interest from academia and industry. These systems face numerous challenges concerning storage space- and read optimization. There exists a large potential for improving current solutions by introducing new management techniques and algorithms. In this paper we give an overview of the basic concept of key-value data storage systems and provide an explanation for bottlenecks. Further we introduce two new memory management algorithms and a improved index structure. Finally, these solutions are compared to each other and discussed.
Security analysis and fault detection against stealthy replay attacks

ABSTRACT This paper investigates the security issue of the data replay attacks on control systems with the LQG controller. The attacker tries to store measurements and replay them in further times. The main novelty in this paper is stated as proposing a different attack detection criterion under the existence of a packet-dropout feature in the network by using the Kullback-Leibler divergence method to cover more general problems and with higher-order dynamics. Formulations and numerical simulations prove the effectiveness of the newly proposed attack detection procedure. Unlike previous approaches that the trade-off between attack detection delay or LQG the performance was significant, in this approach it is proved that the difference in this trade-off is not considered in early moments when the attack happens since the attack detection rate is rapid and thus, the attacks can be stopped with defense strategies in the first moments with the proposed attack detection criterion.
An Early Benchmark of Quality of Experience Between HTTP/2 and HTTP/3 using Lighthouse

Google’s QUIC (GQUIC) is an emerging transport protocol designed to reduce HTTP latency. Deployed across its platforms and positioned as an alternative to TCP+TLS, GQUIC is feature rich: offering reliable data transmission and secure communication. It addresses TCP+TLS’s (i) Head of Line Blocking (HoLB), (ii) excessive round-trip times on connection establishment, and (iii) entrenchment. Efforts by the IETF are in progress to standardize the next generation of HTTP’s (HTTP/3, or H3) delivery, with their own variant of QUIC. While perfor-mance benchmarks have been conducted between GQUIC and HTTP/2-over-TCP (H2), few analyses, to our knowledge, have taken place between H2 and H3. In addition, past studies rely on Page Load Time as their main, if not only, metric. The purpose of this article is to benchmark the latest draft specification of H3 and dig into a user’s Quality of Experience (QoE) by using Lighthouse: an open-source (and metric diverse) auditing tool. Our findings show that, for one of H3’s early implementations, H3 is mostly worse but achieves a higher average throughput.
Reducing Data Motion to Accelerate the Training of Deep Neural Networks

This paper reduces the cost of DNNs training by decreasing the amount of data movement across heterogeneous architectures composed of several GPUs and multicore CPU devices. In particular, this paper proposes an algorithm to dynamically adapt the data representation format of network weights during training. This algorithm drives a compression procedure that reduces data size before sending them over the parallel system. We run an extensive evaluation campaign considering several up-to-date deep neural network models and two high-end parallel architectures composed of multiple GPUs and CPU multicore chips. Our solution achieves average performance improvements from 6.18\% up to 11.91\%.
AR: Auto-Repair the Synthetic Data for Neural Machine Translation

Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translation (BT) or forward translation (FT, or selftraining), into the NMT training process can significantly improve translation quality. However, as a well-known shortcoming, synthetic parallel data is noisy because they are generated by an imperfect NMT system. As a result, the improvements in translation quality bring by the synthetic parallel data are greatly diminished. In this paper, we propose a novel Auto- Repair (AR) framework to improve the quality of synthetic data. Our proposed AR model can learn the transformation from low quality (noisy) input sentence to high quality sentence based on large scale monolingual data with BT and FT techniques. The noise in synthetic parallel data will be sufficiently eliminated by the proposed AR model and then the repaired synthetic parallel data can help the NMT models to achieve larger improvements. Experimental results show that our approach can effective improve the quality of synthetic parallel data and the NMT model with the repaired synthetic data achieves consistent improvements on both WMT14 EN! DE and IWSLT14 DE! EN translation tasks.
Steady-state fluctuations of a genetic feedback loop with fluctuating rate parameters using the unified colored noise approximation

A common model of stochastic auto-regulatory gene expression describes promoter switching via cooperative protein binding, effective protein production in the active state and dilution of proteins. Here we consider an extension of this model whereby colored noise with a short correlation time is added to the reaction rate parameters—we show that when the size and timescale of the noise is appropriately chosen it accounts for fast reactions that are not explicitly modeled, e.g., in models with no mRNA description, fluctuations in the protein production rate can account for rapid multiple stages of nuclear mRNA processing which precede translation in eukaryotes. We show how the unified colored noise approximation can be used to derive expressions for the protein number distribution that is in good agreement with stochastic simulations. We find that even when the noise in the rate parameters is small, the protein distributions predicted by our model can be significantly different than models assuming constant reaction rates.
Greedy Block Gauss-Seidel Methods for Solving Large Linear Least Squares Problem

With a greedy strategy to construct control index set of coordinates firstly and then choosing the corresponding column submatrix in each iteration, we present a greedy block Gauss-Seidel (GBGS) method for solving large linear least squares problem. Theoretical analysis demonstrates that the convergence factor of the GBGS method can be much smaller than that of the greedy randomized coordinate descent (GRCD) method proposed recently in the literature. On the basis of the GBGS method, we further present a pseudoinverse-free greedy block Gauss-Seidel method, which doesn't need to calculate the Moore-Penrose pseudoinverse of the column submatrix in each iteration any more and hence can be achieved greater acceleration. Moreover, this method can also be used for distributed implementations. Numerical experiments show that, for the same accuracy, our methods can far outperform the GRCD method in terms of the iteration number and computing time.
Bootstrapping a Crosslingual Semantic Parser

Recent progress in semantic parsing scarcely considers languages other than English but professional translation can be prohibitively expensive. We adapt a semantic parser trained on a single language, such as English, to new languages and multiple domains with minimal annotation. We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models. We develop a Transformer-based parser combining paraphrases by ensembling attention over multiple encoders and present new versions of ATIS and Overnight in German and Chinese for evaluation. Experimental results indicate that MT can approximate training data in a new language for accurate parsing when augmented with paraphrasing through multiple MT engines. Considering when MT is inadequate, we also find that using our approach achieves parsing accuracy within 2% of complete translation using only 50% of training data.
Knowledge Guided Metric Learning for Few-Shot Text Classification

Humans can distinguish new categories very efficiently with few examples, largely due to the fact that human beings can leverage knowledge obtained from relevant tasks. However, deep learning based text classification model tends to struggle to achieve satisfactory performance when labeled data are scarce. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate different metrics for different tasks. Armed with this network, similar tasks can use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the SoTA few-shot text classification models.
Kollaps: decentralized and dynamic topology emulation

The performance and behavior of large-scale distributed applications is highly influenced by network properties such as latency, bandwidth, packet loss, and jitter. For instance, an engineer might need to answer questions such as: What is the impact of an increase in network latency in application response time? How does moving a cluster between geographical regions affect application throughput? What is the impact of network dynamics on application stability? Currently, answering these questions in a systematic and reproducible way is very hard due to the variability and lack of control over the underlying network. Unfortunately, state-of-the-art network emulation or testbed environments do not scale beyond a single machine or small cluster (i.e., MiniNet), are focused exclusively on the control-plane (i.e., CrystalNet) or lack support for network dynamics (i.e., EmuLab). In this paper, we address these limitations with Kollaps, a fully distributed network emulator. Kollaps hinges on two key observations. First, from an application's perspective, what matters are the emergent end-to-end properties (e.g., latency, bandwidth, packet loss, and jitter) rather than the internal state of the routers and switches leading to those properties. This premise allows us to build a simpler, dynamically adaptable, emulation model that does not require maintaining the full network state. Second, this simplified model is amenable to be maintained in a fully decentralized way, allowing the emulation to scale with the number of machines required by the application. Kollaps is fully decentralized, agnostic of the application language and transport protocol, scales to thousands of processes and is accurate when compared against a bare-metal deployment or state-of-the-art approaches that emulate the full state of the network. We showcase how Kollaps can accurately reproduce results from the literature and predict the behaviour of a complex unmodified distributed key-value store (i.e., Cassandra) under different deployments.
Deep Multimodal Feature Encoding for Video Ordering

True understanding of videos comes from a joint analysis of all its modalities: the video frames, the audio track, and any accompanying text such as closed captions. We present a way to learn a compact multimodal feature representation that encodes all these modalities. Our model parameters are learned through a proxy task of inferring the temporal ordering of a set of unordered videos in a timeline. To this end, we create a new multimodal dataset for temporal ordering that consists of approximately 30K scenes (2-6 clips per scene) based on the "Large Scale Movie Description Challenge". We analyze and evaluate the individual and joint modalities on three challenging tasks: (i) inferring the temporal ordering of a set of videos; and (ii) action recognition. We demonstrate empirically that multimodal representations are indeed complementary, and can play a key role in improving the performance of many applications.
Free-form grale lens inversion of galaxy clusters with up to 1000 multiple images

In the near future, ultra deep observations of galaxy clusters with HST or JWST will uncover $300-1000$ lensed multiple images, increasing the current count per cluster by up to an order of magnitude. This will further refine our view of clusters, leading to a more accurate and precise mapping of the total and dark matter distribution in clusters, and enabling a better understanding of background galaxy population and their luminosity functions. However, to effectively use that many images as input to lens inversion will require a re-evaluation of, and possibly upgrades to the existing methods. In this paper we scrutinize the performance of the free-form lens inversion method Grale in the regime of $150-1000$ input images, using synthetic massive galaxy clusters. Our results show that with an increasing number of input images, Grale produces improved reconstructed mass distributions, with the fraction of the lens plane recovered at better than $10\%$ accuracy increasing from $40-50\%$ for $\sim\! \! 150$ images to $65\%$ for $\sim\! 1000$ images. The reconstructed time delays imply a more precise measurement of $H_0$, with $\lesssim 1\%$ bias. While the fidelity of the reconstruction improves with the increasing number of multiple images used as model constraints, $\sim 150$ to $\sim 1000$, the lens plane rms deteriorates from $\sim 0.11''$ to $\sim 0.28''$. Since lens plane rms is not necessarily the best indicator of the quality of the mass reconstructions, looking for an alternative indicator is warranted.
Global Entity Disambiguation with Pretrained Contextualized Embeddings of Words and Entities

We propose a new global entity disambiguation (ED) model based on contextualized embeddings of words and entities. Our model is based on a bidirectional transformer encoder (i.e., BERT) and produces contextualized embeddings for words and entities in the input text. The model is trained using a new masked entity prediction task that aims to train the model by predicting randomly masked entities in entity-annotated texts obtained from Wikipedia. We further extend the model by solving ED as a sequential decision task to capture global contextual information. We evaluate our model using six standard ED datasets and achieve new state-of-the-art results on all but one dataset.
Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers

In open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classes by measuring distance in a network's logit space, assuming that known classes cluster closer to the training data than unknown classes. However, this approach is applied post-hoc to networks trained with cross-entropy loss, which does not guarantee this clustering behaviour. To overcome this limitation, we introduce the Class Anchor Clustering (CAC) loss. CAC is a distance-based loss that explicitly trains known classes to form tight clusters around anchored class-dependent cluster centres in the logit space. We show that training with CAC achieves state-of-the-art open set performance for distance-based open set classifiers on the standard benchmark datasets, with a 2.4% performance increase in AUROC on the challenging TinyImageNet, without sacrificing classification accuracy. We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning

Bayesian deep neural networks (DNNs) can provide a mathematically grounded framework to quantify uncertainty in predictions from image captioning models. We propose a Bayesian variant of policy-gradient based reinforcement learning training technique for image captioning models to directly optimize non-differentiable image captioning quality metrics such as CIDEr-D. We extend the well-known Self-Critical Sequence Training (SCST) approach for image captioning models by incorporating Bayesian inference, and refer to it as B-SCST. The "baseline" for the policy-gradients in B-SCST is generated by averaging predictive quality metrics (CIDEr-D) of the captions drawn from the distribution obtained using a Bayesian DNN model. We infer this predictive distribution using Monte Carlo (MC) dropout approximate variational inference. We show that B-SCST improves CIDEr-D scores on Flickr30k, MS COCO and VizWiz image captioning datasets, compared to the SCST approach. We also provide a study of uncertainty quantification for the predicted captions, and demonstrate that it correlates well with the CIDEr-D scores. To our knowledge, this is the first such analysis, and it can improve the interpretability of image captioning model outputs, which is critical for practical applications.