Reviewerly/exHarmony huggingface dataset:
Pairwise Supervised Contrastive Learning of Sentence Representations

Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss. Nevertheless, they share a common weakness: sentences in a contradiction pair are not necessarily from different semantic categories. Therefore, optimizing the semantic entailment and contradiction reasoning objective alone is inadequate to capture the high-level semantic structure. The drawback is compounded by the fact that the vanilla siamese or triplet losses only learn from individual sentence pairs or triplets, which often suffer from bad local optima. In this paper, we propose PairSupCon, an instance discrimination based approach aiming to bridge semantic entailment and contradiction understanding with high-level categorical concept encoding. We evaluate PairSupCon on various downstream tasks that involve understanding sentence semantics at different granularities. We outperform the previous state-of-the-art method with 10%–13% averaged improvement on eight clustering tasks, and 5%–6% averaged improvement on seven semantic textual similarity (STS) tasks.
Multi Distance Face Recognition of Eye Localization with Modified Gaussian Derivative Filter

Face recognition at a distance (FRAD) is one of the most difficult types of face recognition applications, particularly at a distance. Due to the poor resolution of facial image, it is difficult to identify faces from a distance. Recently, while recording individuals, the camera view is broad and just a small portion of a person's face is visible in the image. To ensure that the facial image has a low resolution, which deteriorates both face detection and identification engines, the facial image is constantly at low resolution. As an immediate solution, employing a high-definition camera is considered as a simple and practical approach to improve the reliability of algorithm and perform well on low-resolution facial images. While facial detection will be somewhat decreased, a picture with higher quality will result in a slower face detection rate. The proposed work aims to recognize faces with good accuracy even at a distance. The eye localization works for the face and eye location in the face of a human being with varied sizes at multiple distances. This process is used to detect the face quickly with a comparatively high accuracy. The Gaussian derivative filter is used to reduce the feature size in the storage element, which improves the speed of the recognition ratio. Besides, the proposed work includes benchmark datasets to evaluate the recognition process. As a result, the proposed system has achieved a 93.24% average accuracy of face recognition.
Image captioning for effective use of language models in knowledge-based visual question answering

Integrating outside knowledge for reasoning in visio-linguistic tasks such as visual question answering (VQA) is an open problem. Given that pretrained language models have been shown to include world knowledge, we propose to use a unimodal (text-only) train and inference procedure based on automatic off-the-shelf captioning of images and pretrained language models. More specifically, we verbalize the image contents and allow language models to better leverage their implicit knowledge to solve knowledge-intensive tasks. Focusing on a visual question answering task which requires external knowledge (OK-VQA), our contributions are: (i) a text-only model that outperforms pretrained multimodal (image-text) models of comparable number of parameters; (ii) confirmation that our text-only method is specially effective for tasks requiring external knowledge, as it is less effective in standard a VQA task (VQA 2.0); and (iii) our method attains results in the state-of-the-art when increasing the size of the language model. We also significantly outperform current multimodal systems, even though augmented with external knowledge. Our qualitative analysis on OK-VQA reveals that automatic captions often fail to capture relevant information in the images, which seems to be balanced by the better inference ability of the text-only language models. Our work opens up possibilities to further improve inference in visio-linguistic tasks.
Bounds on approximating Max <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>XOR with quantum and classical local algorithms

We consider the power of local algorithms for approximately solving Max <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>XOR, a generalization of two constraint satisfaction problems previously studied with classical and quantum algorithms (MaxCut and Max E3LIN2). In Max <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>XOR each constraint is the XOR of exactly <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> variables and a parity bit. On instances with either random signs (parities) or no overlapping clauses and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>D</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:math> clauses per variable, we calculate the expected satisfying fraction of the depth-1 QAOA from Farhi et al [arXiv:1411.4028] and compare with a generalization of the local threshold algorithm from Hirvonen et al [arXiv:1402.2543]. Notably, the quantum algorithm outperforms the threshold algorithm for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&amp;#x003E;</mml:mo></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>4</mml:mn></mml:math>.On the other hand, we highlight potential difficulties for the QAOA to achieve computational quantum advantage on this problem. We first compute a tight upper bound on the maximum satisfying fraction of nearly all large random regular Max <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>XOR instances by numerically calculating the ground state energy density <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> of a mean-field <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-spin glass [arXiv:1606.02365]. The upper bound grows with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> much faster than the performance of both one-local algorithms. We also identify a new obstruction result for low-depth quantum circuits (including the QAOA) when <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>3</mml:mn></mml:math>, generalizing a result of Bravyi et al [arXiv:1910.08980] when <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:math>. We conjecture that a similar obstruction exists for all <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>.
In-situ bandaged Josephson junctions for superconducting quantum processors

Abstract Shadow evaporation is commonly used to micro-fabricate the key element of superconducting qubits—the Josephson junction. However, in conventional two-angle deposition circuit topology, unwanted stray Josephson junctions are created which contribute to dielectric loss. So far, this could be avoided by shorting the stray junctions with a so-called bandage layer deposited in an additional lithography step, which may further contaminate the chip surface. Here, we present an improved shadow evaporation technique allowing one to fabricate sub-micrometer-sized Josephson junctions together with bandage layers in a single lithography step. We also show that junction aging is significantly reduced when junction electrodes are passivated in an oxygen atmosphere directly after deposition.
Free versus bound entanglement, a NP-hard problem tackled by machine learning

Abstract Entanglement detection in high dimensional systems is a NP-hard problem since it is lacking an efficient way. Given a bipartite quantum state of interest free entanglement can be detected efficiently by the PPT-criterion (Peres-Horodecki criterion), in contrast to detecting bound entanglement, i.e. a curious form of entanglement that can also not be distilled into maximally (free) entangled states. Only a few bound entangled states have been found, typically by constructing dedicated entanglement witnesses, so naturally the question arises how large is the volume of those states. We define a large family of magically symmetric states of bipartite qutrits for which we find $$82\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>82</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> to be free entangled, $$2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> to be certainly separable and as much as $$10\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> to be bound entangled, which shows that this kind of entanglement is not rare. Via various machine learning algorithms we can confirm that the remaining $$6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> of states are more likely to belonging to the set of separable states than bound entangled states. Most important we find via dimension reduction algorithms that there is a strong two-dimensional (linear) sub-structure in the set of bound entangled states. This revealed structure opens a novel path to find and characterize bound entanglement towards solving the long-standing problem of what the existence of bound entanglement is implying.
Controllable Neural Dialogue Summarization with Personal Named Entity Planning

In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning.The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks.This framework supports two types of use cases: (1) Comprehensive Perspective, which is a generalpurpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation.During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation.Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.
Big data for whose sake? Governing migration through artificial intelligence

Abstract Although human activity constantly generates massive amounts of data, these data can only be analysed by mainly the private sector and governmental institutes due to data accessibility restrictions. However, neither migrants (as the producers of this data) nor migration scholars (as scientific experts on the topic) are in a position to monitor or control how governments and corporations use such data. Big Data analytics and Artificial Intelligence (AI) technologies are promoted as cutting-edge solutions to ongoing and emerging social, economic and governance challenges. Meanwhile, states increasingly rely on digital and frontier technologies to manage borders and control migratory movements, and the defence industry and military–intelligence sectors provide high-tech tools to support these efforts. Worryingly, during the design and testing of algorithmic tools, migrants are often portrayed as a security threat instead of human beings with fundamental rights and liberties. Thus, privacy, data protection, and confidentiality issues continue to pose risks and challenges to migrant communities and raise important questions for the public and decision-makers alike. This comment seeks to shed light on the lack of effective regulation of AI and Big Data as they are applied in migration ‘management’. Additionally, from the perspective of privacy issues and immigrant rights (seeking asylum as a human right, it aims at advocating improved access to Big Data for scientific research which might act as a social control function for the smart border and existing/ongoing migration governance practices of countries. We argue that the use of Big Data and AI for migration governance requires much better collaboration between migrants (including the civil society and grassroots organisations solidarity that represent them), data scientists, migration scholars and policymakers if the potential of these technologies is to be reached in a way that is reasonable and ethical. Numerous critical privacy questions arise are regarding the legal requirements, confidentiality, and rules of engagement as well as the ethical concerns of (mis)use of new technologies. When the secretive nature of the ongoing exploitation of migrant data by states and corporations is considered raising such questions is essential for progress.
Toward a Reference Model for Artificial Intelligence Supporting Big Data Analysis

This publication will introduce the reference model AI2VIS4BigData for the application domains Big Data analysis, AI, and visualization. Without a reference model, developing a software system and other scientific and industrial activities in this topic field lack a common specification and a common basis for discussion and thus pose a high risk of inefficiency, reinventing the wheel and solving problems that have already been solved elsewhere. To prevent these disadvantages, this publication systematically derives the reference model AI2VIS4BigData with special focus on use cases where Big Data analysis, artificial intelligence (AI), and visualization mutually support each other: AI-powered algorithms empower data scientists to analyze Big Data and thereby exploit its full potential. Big Data enables AI specialists to comfortably design, validate, and deploy AI models. In addition, AI’s algorithms and methods offer the opportunity to make Big Data exploration more efficient for both, involved users and computing and storage resources. Visualization of data, algorithms, and processing steps improves comprehension and lowers entry barriers for all user stereotypes involved in these use cases.
Measuring the network performance of Google cloud platform

Public cloud platforms are vital in supporting online applications for remote learning and telecommuting during the COVID-19 pandemic. The network performance between cloud regions and access networks directly impacts application performance and users' quality of experience (QoE). However, the location and network connectivity of vantage points often limits the visibility of edge-based measurement platforms (e.g., RIPE Atlas).
The Thick Machine: Anthropological AI between explanation and explication

According to Clifford Geertz, the purpose of anthropology is not to explain culture but to explicate it. That should cause us to rethink our relationship with machine learning. It is, we contend, perfectly possible that machine learning algorithms, which are unable to explain, and could even be unexplainable themselves, can still be of critical use in a process of explication. Thus, we report on an experiment with anthropological AI. From a dataset of 175K Facebook comments, we trained a neural network to predict the emoji reaction associated with a comment and asked a group of human players to compete against the machine. We show that a) the machine can reach the same (poor) accuracy as the players (51%), b) it fails in roughly the same ways as the players, and c) easily predictable emoji reactions tend to reflect unambiguous situations where interpretation is easy. We therefore repurpose the failures of the neural network to point us to deeper and more ambiguous situations where interpretation is hard and explication becomes both necessary and interesting. We use this experiment as a point of departure for discussing how experiences from anthropology, and in particular the tension between formalist ethnoscience and interpretive thick description, might contribute to debates about explainable AI.
Depth Inpainting via Vision Transformer

Depth inpainting is a crucial task for working with augmented reality. In previous works missing depth values are completed by convolutional encoder-decoder networks, which is a kind of bottleneck. But nowadays vision transformers showed very good quality in various tasks of computer vision and some of them became state of the art. In this study, we presented a supervised method for depth inpainting by RGB images and sparse depth maps via vision transformers. The proposed model was trained and evaluated on the NYUv2 dataset. Experiments showed that a vision transformer with a restrictive convolutional tokenization model can improve the quality of the inpainted depth map.
Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators

Heavily pre-trained transformers for language modeling, such as BERT, have shown to be remarkably effective for Information Retrieval (IR) tasks, typically applied to re-rank the results of a first-stage retrieval model. IR benchmarks evaluate the effectiveness of retrieval pipelines based on the premise that a single query is used to instantiate the underlying information need. However, previous research has shown that (I) queries generated by users for a fixed information need are extremely variable and, in particular, (II) neural models are brittle and often make mistakes when tested with modified inputs. Motivated by those observations we aim to answer the following question: how robust are retrieval pipelines with respect to different variations in queries that do not change the queries’ semantics? In order to obtain queries that are representative of users’ querying variability, we first created a taxonomy based on the manual annotation of transformations occurring in a dataset (UQV100) of user-created query variations. For each syntax-changing category of our taxonomy, we employed different automatic methods that when applied to a query generate a query variation. Our experimental results across two datasets for two IR tasks reveal that retrieval pipelines are not robust to these query variations, with effectiveness drops of $$\approx 20\%$$ on average. The code and datasets are available at https://github.com/Guzpenha/query_variation_generators .
Robust Incremental Outlier Detection Approach Based on a New Metric in Data Streams

Detecting outliers in real time from multivariate streaming data is a vital and challenging research topic in many areas. Recently introduced the incremental Local Outlier Factor (iLOF) approach and its variants have received considerable attention as they achieve high detection performance in data streams with varying distributions. However, these iLOF-based approaches still have some major limitations: i) Poor detection in high-dimensional data; ii) The difficulty of determining the proper nearest neighbor number k; iii) Instead of labeling the outlier, assigning a score to each sample that indicates the probability to be an outlier; iv) Inability to detect a long sequence (small cluster) of outliers. This article proposes a new robust outlier detection method (RiLOF) based on iLOF that can effectively overcome these limitations. In the RiLOF method, a novel metric called Median of Nearest Neighborhood Absolute Deviation (MoNNAD) has been developed that uses the median of the local absolute deviation of the samples LOF values. Unlike the previously reported LOF-based approaches, RiLOF is capable of achieving outlier detection in different data stream applications using the same hyperparameters. Extensive experiments performed on 15 different real-world data sets demonstrate that RiLOF remarkably outperforms 12 different state-of-the-art competitors.
A survey of challenges and solutions for the integration of renewable energy in datacenters

As more and more activities are moving online, the need for datacenters is increasing which raises their energy consumption and the resulting greenhouse gas emissions. The supply of datacenters with renewable energy is often seen as the main solution to this nexus. However, multiple challenges are posed by their integration. Solutions to these challenges, mainly in the form of flexibility and energy efficiency, require multidisciplinary skills. As a consequence, this article aims to review scientific efforts in several complementary disciplines: electrical engineering, computer science, control system engineering and operations research. It covers datacenter architecture, infrastructure sizing and resource management on both information and energy sides. Conclusions on current progresses are drawn and challenges that remain to be addressed are listed as well.
Deep Joint Demosaicing and High Dynamic Range Imaging Within a Single Shot

Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVE-based HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over- and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over- and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-to-end single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods. Related codes and datasets will be provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yilun-xu/SVEHDRI/</uri> .
Quantum generative adversarial networks with multiple superconducting qubits

Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts--called quantum generative adversarial networks (QGANs)--may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and multi-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.