Reviewerly/exHarmony huggingface dataset:
N-dimensional nonlinear prediction with MLP

In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for speech coding, based on Multi-Layer Perceptrons. With this scheme we have improved the results of our previous ADPCM coder with nonlinear prediction, and we have reduced the bit rate up to 1 bit per sample.
Counting characters of small degree in upper unitriangular groups

Let $U_n$ denote the group of upper $n \times n$ unitriangular matrices over a fixed finite field $\mathbb{F}$ of order $q$. That is, $U_n$ consists of upper triangular $n \times n$ matrices having every diagonal entry equal to $1$. It is known that the degrees of all irreducible complex characters of $U_n$ are powers of $q$. It was conjectured by Lehrer that the number of irreducible characters of $U_n$ of degree $q^e$ is an integer polynomial in $q$ depending only on $e$ and $n$. We show that there exist recursive (for $n$) formulas that this number satisfies when $e$ is one of $1, 2$ and $3$, and thus show that the conjecture is true in those cases.
Strategies for variational quantum compiling of a zero-phase beamsplitter on the Xanadu X8 processor

In the context of variational compiling of a continuous-variable (CV) unitary operation, the architecture and parameter space of the Xanadu X8 processor constrain both the set of feasible compiling problems and the allowed cost functions. In this paper, we motivate a faithful cost function for variational compiling of a two-mode, continuous-variable beamsplitter gate with real matrix elements (i.e., a "zero-phase" beamsplitter) that complies with the constraints of the X8 processor. This cost function is then computed on the X8. Despite the noise in the processor, we find that the cost function exhibits optimum parameter resilience and, therefore, that this variational compiling problem is feasible on the X8. The intent of the paper is partly to report a proof-of-principle cost function calculation on near-term CV hardware, and partly to present methods that may be relevant for CV variational compiling problems in more complicated, large scale settings.
Truthful Online Scheduling of Cloud Workloads under Uncertainty

Cloud computing customers often submit repeating jobs and computation pipelines on approximately regular schedules, with arrival and running times that exhibit variance. This pattern, typical of training tasks in machine learning, allows customers to partially predict future job requirements. We develop a model of cloud computing platforms that receive statements of work (SoWs) in an online fashion. The SoWs describe future jobs whose arrival times and durations are probabilistic, and whose utility to the submitting agents declines with completion time. The arrival and duration distributions, as well as the utility functions, are considered private customer information and are reported by strategic agents to a scheduler that is optimizing for social welfare.
Robust Speech Representation Learning via Flow-based Embedding  Regularization

Over the recent years, various deep learning-based methods were proposed for extracting a fixed-dimensional embedding vector from speech signals. Although the deep learning-based embedding extraction methods have shown good performance in numerous tasks including speaker verification, language identification and anti-spoofing, their performance is limited when it comes to mismatched conditions due to the variability within them unrelated to the main task. In order to alleviate this problem, we propose a novel training strategy that regularizes the embedding network to have minimum information about the nuisance attributes. To achieve this, our proposed method directly incorporates the information bottleneck scheme into the training process, where the mutual information is estimated using the main task classifier and an auxiliary normalizing flow network. The proposed method was evaluated on different speech processing tasks and showed improvement over the standard training strategy in all experimentation.
Fusing finetuned models for better pretraining

Pretrained models are the standard starting point for training. This approach consistently outperforms the use of a random initialization. However, pretraining is a costly endeavour that few can undertake. In this paper, we create better base models at hardly any cost, by fusing multiple existing fine tuned models into one. Specifically, we fuse by averaging the weights of these models. We show that the fused model results surpass the pretrained model ones. We also show that fusing is often better than intertraining. We find that fusing is less dependent on the target task. Furthermore, weight decay nullifies intertraining effects but not those of fusing.
Exploring Credibility Scoring Metrics of Perception Systems for Autonomous Driving

Autonomous and semi-autonomous vehicles' perception algorithms can encounter situations with erroneous object detection, such as misclassification of objects on the road, which can lead to safety violations and potentially fatal consequences. While there has been substantial work in the robustness of object detection algorithms and online metric learning, there is little research on benchmarking scoring metrics to determine any possible indicators of potential misclassification. An emphasis is put on exploring the potential of taking these scoring metrics online in order to allow the AV to make perception-based decisions given real-time constraints. In this work, we explore which, if any, metrics act as online indicators of when perception algorithms and object detectors are failing. Our work provides insight on better design principles and characteristics of online metrics to accurately evaluate the credibility of object detectors. Our approach employs non-adversarial and realistic perturbations to images, on which we evaluate various quantitative metrics. We found that offline metrics can be designed to account for real-world corruptions such as poor weather conditions and that the analysis of such metrics can provide a segue into designing online metrics. This is a clear next step as it can allow for error-free autonomous vehicle perception and safer time-critical and safety-critical decision-making.
Towards a Domain Ontology for the Analysis of Ancient Fabrics The SILKNOW Project and the Case of European Silk Heritage

In this article, we present the SILKNOW project (Silk heritage in the Knowledge Society: from punched card to Big Data, Deep Learning and visual/tangible simulations) (2018-2021). This project aimed to use Semantic Web technologies to give greater visibility to silk objects produced and consumed in Europe between the 15th and 19th centuries. Silk is a particularly important material in European history, and it has produced some exceptional objects of great historical interest. However, it is a threatened heritage that is little known to the general public. We show the interest of using Semantic Web technologies to give more visibility to such a heritage, by describing the results we have obtained. We present the methodology used to develop a knowledge graph, and in particular the different steps that were necessary to create the underlying data model, based on the CIDOC CRM or CIDOC Conceptual Reference Model. We also propose a CIDOC CRM-compatible extension to express the complex semantics of the creation and production process of ancient silk fabrics.
Adaptive Single Image Deblurring

This paper tackles the problem of dynamic scene deblurring. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by a simple increment in the number of generic convolution layers, kernel-size, which comes with the burden of the increase in model size and inference speed. In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations within and across different images. We also propose an effective content-aware global-local filtering module that significantly improves the performance by considering not only the global dependencies of the pixel but also dynamically using the neighboring pixels. We use a patch hierarchical attentive architecture composed of the above module that implicitly discover the spatial variations in the blur present in the input image and in turn perform local and global modulation of intermediate features. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate the superiority of the proposed network.
Minotaur: Multi-Resource Blockchain Consensus

Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engaged in the system. The variety of different resources (and related proof protocols, some times referred to as PoX in the literature) raises the fundamental question whether it is possible to utilize many of them in tandem and build multi-resource consensus protocols. The challenge in combining different resources is to achieve fungibility between them, in the sense that security would hold as long as the cumulative adversarial power across all resources is bounded. In this work, we put forth Minotaur, a multi-resource blockchain consensus protocol that combines proof-of-work (PoW) and proof-of-stake (PoS), and we prove it optimally fungible. At the core of our design, Minotaur operates in epochs while continuously sampling the active computational power to provide a fair exchange between the two resources, work and stake. Further, we demonstrate the ability of Minotaur to handle a higher degree of work fluctuation as compared to the Bitcoin blockchain; we also generalize Minotaur to any number of resources. We demonstrate the simplicity of Minotaur via implementing a full stack client in Rust (available open source). We use the client to test the robustness of Minotaur to variable mining power and combined work/stake attacks and demonstrate concrete empirical evidence towards the suitability of Minotaur to serve as the consensus layer of a real-world blockchain.
Decentralizing Permissioned Blockchain with Delay Towers

Growing excitement around permissionless blockchains is uncovering its latent scalability concerns. Permissioned blockchains offer high transactional throughput and low latencies while compromising decentralization. In the quest for a decentralized, scalable blockchain fabric, i.e., to offer the scalability of permissioned blockchain in a permissionless setting, we present L4L to encourage decentralization over the permissioned Libra network without compromising its sustainability. L4L employs delay towers, -- puzzle towers that leverage verifiable delay functions -- for establishing identity in a permissionless setting. Delay towers cannot be parallelized due to their sequential execution, making them an eco-friendly alternative. We also discuss methodologies to replace validators participating in consensus to promote compliant behavior. Our evaluations found that the cost of enabling decentralization over permissioned networks is almost negligible. Furthermore, delay towers offer an alternative to existing permissionless consensus mechanisms without requiring airdrops or pre-sale of tokens.
Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation

Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existing methods for SBR, there are still several limitations: (i) Almost all existing works concentrate on single interest extraction and fail to disentangle multiple interests of user, which easily results in suboptimal representations for SBR. (ii) Furthermore, previous methods also ignore the multi-form temporal information, which is significant signal to obtain current intention for SBR. To address the limitations mentioned above, we propose a novel method, called \emph{Temporal aware Multi-Interest Graph Neural Network} (TMI-GNN) to disentangle multi-interest and yield refined intention representations with the injection of two level temporal information. Specifically, by appending multiple interest nodes, we construct a multi-interest graph for current session, and adopt the GNNs to model the item-item relation to capture adjacent item transitions, item-interest relation to disentangle the multi-interests, and interest-item relation to refine the item representation. Meanwhile, we incorporate item-level time interval signals to guide the item information propagation, and interest-level time distribution information to assist the scattering of interest information. Experiments on three benchmark datasets demonstrate that TMI-GNN outperforms other state-of-the-art methods consistently.
On expressive rule-based logics

We investigate a family of rule-based logics. The focus is on very expressive languages. We provide a range of characterization results for the expressive powers of the logics and relate them with corresponding game systems.
The Mathematics of Comparing Objects

"After reading two different crime stories, an artificial intelligence concludes that in both stories the police has found the murderer just by random." -- To what extend and under which assumptions this is a description of a realistic scenario?
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected primarily from drones. In contrast to single object scenes (on which NeRFs are traditionally evaluated), our scale poses multiple challenges including (1) the need to model thousands of images with varying lighting conditions, each of which capture only a small subset of the scene, (2) prohibitively large model capacities that make it infeasible to train on a single GPU, and (3) significant challenges for fast rendering that would enable interactive fly-throughs. To address these challenges, we begin by analyzing visibility statistics for large-scale scenes, motivating a sparse network structure where parameters are specialized to different regions of the scene. We introduce a simple geometric clustering algorithm for data parallelism that partitions training images (or rather pixels) into different NeRF submodules that can be trained in parallel. We evaluate our approach on existing datasets (Quad 6k and UrbanScene3D) as well as against our own drone footage, improving training speed by 3x and PSNR by 12%. We also evaluate recent NeRF fast renderers on top of Mega-NeRF and introduce a novel method that exploits temporal coherence. Our technique achieves a 40x speedup over conventional NeRF rendering while remaining within 0.8 db in PSNR quality, exceeding the fidelity of existing fast renderers.
MicroRacer: a didactic environment for Deep Reinforcement Learning

MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or the need of exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD2 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.
Transfer Learning In Differential Privacy's Hybrid-Model

The hybrid-model (Avent et al 2017) in Differential Privacy is a an augmentation of the local-model where in addition to N local-agents we are assisted by one special agent who is in fact a curator holding the sensitive details of n additional individuals. Here we study the problem of machine learning in the hybrid-model where the n individuals in the curators dataset are drawn from a different distribution than the one of the general population (the local-agents). We give a general scheme -- Subsample-Test-Reweigh -- for this transfer learning problem, which reduces any curator-model DP-learner to a hybrid-model learner in this setting using iterative subsampling and reweighing of the n examples held by the curator based on a smooth variation of the Multiplicative-Weights algorithm (introduced by Bun et al, 2020). Our scheme has a sample complexity which relies on the chi-squared divergence between the two distributions. We give worst-case analysis bounds on the sample complexity required for our private reduction. Aiming to reduce said sample complexity, we give two specific instances our sample complexity can be drastically reduced (one instance is analyzed mathematically, while the other - empirically) and pose several directions for follow-up work.
Self-supervised Video Transformer

In this paper, we propose self-supervised training for video transformers using unlabeled video data. From a given video, we create local and global spatiotemporal views with varying spatial sizes and frame rates. Our self-supervised objective seeks to match the features of these different views representing the same video, to be invariant to spatiotemporal variations in actions. To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT). Further, owing to the flexibility of Transformer models, SVT supports slow-fast video processing within a single architecture using dynamically adjusted positional encoding and supports long-term relationship modeling along spatiotemporal dimensions. Our approach performs well on four action recognition benchmarks (Kinetics-400, UCF-101, HMDB-51, and SSv2) and converges faster with small batch sizes. Code: https://git.io/J1juJ
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs

Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human and have the means to help them. We present a novel formulation of the interaction between the human and the AI as a sequential game where the agents are modelled using Bayesian best-response models. We show that in this case the AI's problem of helping bounded-rational humans make better decisions reduces to a Bayes-adaptive POMDP. In our simulated experiments, we consider an instantiation of our framework for humans who are subjectively optimistic about the AI's future behaviour. Our results show that when equipped with a model of the human, the AI can infer the human's bounds and nudge them towards better decisions. We discuss ways in which the machine can learn to improve upon its own limitations as well with the help of the human. We identify a novel trade-off for centaurs in partially observable tasks: for the AI's actions to be acceptable to the human, the machine must make sure their beliefs are sufficiently aligned, but aligning beliefs might be costly. We present a preliminary theoretical analysis of this trade-off and its dependence on task structure.
College English Teaching in the Multimedia Environment

Multimedia assisted teaching has been a most popular teaching mode in college.This thesis will be developed firstly from the basic features of multimedia technology and then discuss the application of multimedia technology in college English teaching.The diversity, integration and interaction of multimedia technology brings convenience for college English teaching and promotes its reformation.The organic integration of computer technology into the teaching of college English contributes to the formation of the student-oriented teaching structure, which effectively stimulates the agency of students and further promotes the teaching efficiency.The multi-modality and multi-environment constructed by computer technology together with the clear instructions of teacher help students understand the cultural phenomenon and values in the text, obtain knowledge in values and finally leads to the students' ability of switching information to wisdom.
Snakes and Ladders and Intransitivity, or what mathematicians do in their time off

This recreational mathematics article shows that the game of Snakes and Ladders is intransitive: square 69 has a winning edge over 79, which in turn beats 73, which beats 69. Analysis of the game is a nice illustration of Markov chains, simulations of different sorts, and size-biased sampling. Connecting this to "intransitive dice" illustrates the power of a name, and the joy of working with colleagues. When draws do not count, we show a minimal example of intransitive dice, with one die having just a single "face" and two dice each having two faces.
Writing Support System that Enriches Expressions Using Encoder-Decoder Model

Improving texts by using rhetorical expressions is important when we write literary works such as novels and essays. Although expression dictionaries can help authors broaden the range of their expressions, these dictionaries are often exhaustive, and selecting appropriate expressions from them requires a certain amount of writing experience. In this study, we propose a writing support system that help authors improve their wordings by generating and suggesting improved sentences with rhetorical expressions. This system is based on the Encoder-Decoder model that generates sentences in a context-sensitive manner. As a result, we observe that our system generates different modifiers for a same word depending on each context. Through a manual evaluation by a questionnaire, our system was shown to be comprehensively useful for writing support. Furthermore, we showed that attention scores calculated in our model can be used to understand the relationship between added expressions and words in input.
RheFrameDetect: A Text Classification System for Automatic Detection of Rhetorical Frames in AI from Open Sources

Rhetorical Frames in AI can be thought of as expressions that describe AI development as a competition between two or more actors, such as governments or companies. Examples of such Frames include robotic arms race, AI rivalry, technological supremacy, cyberwarfare dominance and 5G race. Detection of Rhetorical Frames from open sources can help us track the attitudes of governments or companies towards AI, specifically whether attitudes are becoming more cooperative or competitive over time. Given the rapidly increasing volumes of open sources (online news media, twitter, blogs), it is difficult for subject matter experts to identify Rhetorical Frames in (near) real-time. Moreover, these sources are in general unstructured (noisy) and therefore, detecting Frames from these sources will require state-of-the-art text classification techniques. In this paper, we develop RheFrameDetect, a text classification system for (near) real-time capture of Rhetorical Frames from open sources. Given an input document, RheFrameDetect employs text classification techniques at multiple levels (document level and paragraph level) to identify all occurrences of Frames used in the discussion of AI. We performed extensive evaluation of the text classification techniques used in RheFrameDetect against human annotated Frames from multiple news sources. To further demonstrate the effectiveness of RheFrameDetect, we show multiple case studies depicting the Frames identified by RheFrameDetect compared against human annotated Frames.