pwc-archive/papers-with-abstracts huggingface dataset:
Neural Structure Mapping For Learning Abstract Visual Analogies
Authors: Shashank Shekhar, Graham W. Taylor
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2021 12
Tasks: Visual Analogies, Visual Reasoning

Building conceptual abstractions from sensory information and then reasoning about them is central to human intelligence. Abstract reasoning both relies on, and is facilitated by, our ability to make analogies about concepts from known domains to novel domains. Structure Mapping Theory of human analogical reasoning posits that analogical mappings rely on (higher-order) relations and not on the sensory content of the domain. This enables humans to reason systematically about novel domains, a problem with which machine learning (ML) models tend to struggle. We introduce a two-stage neural framework, which we label Neural Structure Mapping (NSM),  to learn visual analogies from Raven's Progressive Matrices, an abstract visual reasoning test of fluid intelligence. Our framework uses (1) a multi-task visual relationship encoder to extract constituent concepts from raw visual input in the source domain, and (2) a neural module net analogy inference engine to reason compositionally about the inferred relation in the target domain. Our NSM approach (a) isolates the relational structure from the source domain with high accuracy, and (b) successfully utilizes this structure for analogical reasoning in the target domain.
In Silico Modelling of Neurodegeneration Using Deep Convolutional Neural Networks
Authors: Jasmine A Moore, Anup Tuladhar, Zahinoor Ismail, Nils Forkert
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2021 12
Tasks: Object Recognition

Although current research aims to use and improve deep learning networks by applying knowledge about the structure and function of the healthy human brain and vice versa, the potential of using such networks to model neurodegenerative diseases remains largely understudied. In this work, we present a novel feasibility study modeling dementia in silico with deep convolutional neural networks. Therefore, deep convolutional neural networks were fully trained to perform visual object recognition, and then progressively injured in two distinct ways. More precisely, damage was progressively inflicted mimicking neuronal as well as synaptic injury. Synaptic injury was applied by randomly deleting weights in the network, while neuronal injury was simulated by removing full nodes or filters in the network. After each iteration of injury, network object recognition accuracy was evaluated. Saliency maps were generated using the uninjured and injured networks and quantitatively compared using the structural similarity index measure for test set images to further investigate the loss of visual cognition. The quantitative evaluation revealed cognitive function of the network progressively decreased with increasing injury load. This effect was more pronounced for synaptic damage. As damage increased, the model focus shifted away from the main objects in the images and became more dispersed. This shift in attention was quantitatively evidenced by a decrease in the structural similarity index measure comparing the saliency maps of corresponding uninjured and injured models, as a function of injury. The results of this study provide a promising foundation to develop in silico models of neurodegenerative diseases using deep learning networks. The effects of neurodegeneration found for the in silico model are especially similar to the loss of visual cognition seen in patients with posterior cortical atrophy.
What Matters In Branch Specialization? Using a Toy Task to Make Predictions
Authors: Chenguang Li, Arturo Deza
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2021 12

What motivates the brain to allocate tasks to different regions and what distinguishes multiple-demand brain regions and the tasks they perform from ones in highly specialized areas? Here we explore these neuroscientific questions using a purely computational framework and theoretical insights. In particular, we focus on how branches of a neural network learn representations contingent on their architecture and optimization task. We train branched neural networks on families of Gabor filters as the input training distribution and optimize them to perform combinations of angle, average color, and size approximation tasks. We find that networks predictably allocate tasks to the branches with appropriate inductive biases. However, this task-to-branch matching is not required for branch specialization, as even identical branches in a network tend to specialize. Finally, we show that branch specialization can be controlled by a curriculum in which tasks are alternated instead of jointly trained. Longer training between alternation corresponds to more even task distribution among branches, providing a possible model for multiple-demand regions in the brain.
A finer mapping of convolutional neural network layers to the visual cortex
Authors: Tom Dupre la Tour, Michael Lu, Michael Eickenberg, Jack L. Gallant
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2021 12

There is increasing interest in understanding similarities and differences between convolutional neural networks (CNNs) and the visual cortex. A common approach is to use features extracted from intermediate CNN layers to fit brain encoding models. Each brain region is then typically associated with the best predicting layer. However, this winner-take-all mapping is non-robust, because consecutive CNN layers are strongly correlated and have similar prediction accuracies. Moreover, the winner-take-all approach ignores potential complementarities between layers to predict brain activity. To address this issue, we propose to fit a joint model on all layers simultaneously. The model is fit with banded ridge regression, grouping features by layer, and learning a separate regularization hyperparameter per feature space. By performing a selection over layers, this model effectively removes non-predictive or redundant layers and disentangles the contributions of each layer on each voxel. This model leads to increased prediction accuracy and to finer mappings of layer selectivity.
Semi-supervised Object Detection via Virtual Category Learning
Authors: Anonymous
Date: 2021-11-25 00:00:00
Tasks: Object, object-detection, Object Detection, Semi-Supervised Object Detection

Due to the lack of large amounts of labelled data to learn rich-expressive features of objects, semi-supervised detectors powered by pseudo labelling techniques usually make a tentative decision for the pseudo labels of confusing samples. When dealing with confusing training samples, neither of the two recently adopted strategies, i.e., discarding or retaining, is optimal. Arbitrarily discarding the valuable confusing samples would compromise the generalisation ability of the model, while using them for model training would exacerbate the confirmation bias issue caused by inevitable mislabelling. To remedy this situation, this paper, for the first time, proposes to make use of these confusing samples without label correction, instead of discarding them. To this end, an alternative virtual category (VC), which is safe for optimisation, is provided for each confusing sample such that they can still contribute to better decision-boundary finding even without a concrete label. It is attributed to a new VC loss formulating the embedding distance between the training sample and the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow the high-quality bounding boxes to be used for location regression training. Extensive experimental results demonstrate that the proposed semi-supervised detector underpinned by the VC learning surpasses the current state-of-the-art, when extremely small amounts of annotated labels are available for training (e.g., 0.5\% and 1\% label ratios).
Learning an airway atlas from lung CT using semantic inter-patient deformable registration
Authors: Anonymous
Date: 2021-11-15 00:00:00
Tasks: Anatomy, Diagnostic, Image Registration

Pulmonary image analysis for diagnostic and interventions often relies on a canonical geometric representation of lung anatomy across a patient cohort. Bronchoscopy can benefit from simulating an appearance atlas of airway cross- sections, intra-patient deformable image registration could be initialised using a shared lung atlas. The diagnosis of pneumonia, COPD and other respiratory diseases can benefit from a well defined anatomical reference space. Previous work to create lung atlases either relied on tedious and often ambiguous manual landmark correspondences and/or image features to perform deformable inter- patient registration. In this work, we overcome these limitations by guiding the registration with semantic airway features that can be obtained straightforwardly with an nnUNet and dilated training labels. We demonstrate that accurate and robust registration results across patients can be achieved in few seconds leading to high agreement of small airways of later generations. Incorporating the semantic cost function improves segmentation overlap and landmark accuracy.
TheEyeCorpus: Experiments in Reducing NLP Bias and Identifiability for Large LMs
Authors: Anonymous
Date: 2021-11-03 00:00:00

NLP is a constantly changing and evolving subset of artificial intelligence, due to constantly increasing data requirements for SOTA (state of the art) machine learning models such as GPT-3, we often neglect the safety and privacy of our data producers. Using pure raw data often presents challenges, such as legalities and identifiability, solving these issues requires taking a machine-to-machine corpus approach, using source data to synthesize novel data. However, machine-to-machine approaches often involve a prolonged data gathering period, while also being computationally costly to produce. Rather than building from the ground up I believe publicly available datasets are needed. Building off of previous work from Gretel.AI (https://gretel.ai/), I propose TheEyeCorpus, a machine synthesized publicly available text corpus obtained from the public The-Eye Discord server and preprocessed and synthesised by Simp Labs utilizing Gretel AI’s APIS and utilities. The data is hosted at github.com/puffy310/TheEyeCorpus
A General “Power-of-d” Dispatching Framework for Heterogeneous Systems
Authors: Anonymous
Date: 2021-10-20 00:00:00

Intelligent  dispatching  is  crucial  to  obtaining  low  response  times  in  large-scale  systems.  One common  scalable  dispatching paradigm  is  the  “power-of-d,”  in  which  the  dispatcher  queries d servers  at random and assigns the job to a server based only on the state of the queried servers. The bulk of power-of-d policies studied in the literature assume that the system is homogeneous, meaning that all servers have the same speed; meanwhile real-world systems often exhibit server speed heterogeneity. This paper introduces a general framework for describing and analyzing heterogeneity-aware power-of-d policies.  The  key  idea  behind  our  framework  is  that  dispatching  policies  can  make  use  of  server  speed information at two decision points: when choosing which d servers to query, and when assigning a job to one of those servers. Our framework explicitly separates the dispatching policy into a querying rule and an assignment rule; we consider general families of both rule types. While  the  strongest  assignment  rules  incorporate  both  detailed  queue length  information  and  server speed information, these rules typically are difficult to analyze. We overcome this difficulty by focusing on heterogeneity-aware assignment rules that ignore queue length information beyond idleness status. In this setting, we analyze mean response time and formulate novel optimization problems for the joint optimization of querying and assignment. We build upon our optimized policies to develop heuristic queue length-aware dispatching policies. Our heuristic policies perform well in simulation, relative to policies that have appeared in the literature.
The Solar System: Nature and Mechanics
Authors: Anonymous
Date: 2021-10-16 00:00:00

Origin, mechanics and properties of the Solar System are analyzed in the framework of Complete Relativity.

The analysis confirms the postulates and hypotheses of the theory with a high degree of confidence.

During the analysis, some new hypotheses have emerged. These are discussed and confirmed with various degrees of confidence. To increase confidence or refute some hypotheses, experimental verification is necessary.

Main conclusions are:

    Solar System is a scaled Carbon isotope with a nucleus in a condensed (bosonic) state and components in various vertically excited states,
    Earth is a living being of extremely introverted intelligence,
    life is common everywhere, albeit extroverted complex forms are present on planetary surfaces only during planetary neurogenesis,
    anthropogenic climate change is only a part (trigger from one perspective) of bigger global changes on Earth and in the Solar System during planetary neurogenesis,
    major extinction events are relative extinctions, a regular part of transformation and transfer of life in the process of planetary neurogenesis.
Complete Relativity: Nature of observables
Authors: Anonymous
Date: 2021-10-16 00:00:00
Tasks: Articles

A complete relativity of all observables with scale invariance in physical laws is postulated and discussed.

Along with definitions of new terms which may be used in follow-up articles written in the context of the theory, in order to conform to this physics, a redefinition and generalization of some terms and factors already in use has also been suggested.

In conclusion, the theory suggests that everything must be relative in order to exist and everything evolves in order to conserve this, existence conserving, relativity.
Detecting Concealed Language Knowledge via Response Times
Authors: Gáspár Lukács, Claudia Kawai, Ulrich Ansorge, Anna Fekete
Date: 2021-02-21 00:00:00

In the present study, we introduce a response-time-based test that can be used to detect concealed language knowledge, for various potential applications (e.g., espionage, border control, counter-terrorism). In this test, the examinees are asked to respond to repeatedly presented items, including a real word in the language tested (suspected to be known by the examinee) and several pseudowords. A person who understands the tested language recognizes the real word and tends to have slower responses to it as compared to the pseudowords, and, thereby, can be distinguished from those who do not understand the language. This was demonstrated in a series of experiments including diverse participants tested for their native language (German, Hungarian, Polish, Russian; n = 312), for second language (English, German; n = 66), and several control groups (n = 192).
Question Generation for Generating Textbook Flashcards
Authors: Anonymous
Date: 2021-09-16 00:00:00
Tasks: Question Generation, Question-Generation

One of the most effective ways of retaining the meaning of important concepts in learning materials is to review them in spaced intervals. Millions of students around the world are trying to do exactly that with the help of flash cards. In this short paper, we present a new, transformer-based application for education that automates the process of creating flash cards by automatically generating questions and answers for textbooks. As a  proof-of-concept, we report two studies: a) generating questions for textbook summaries written by humans and b)  testing a fully-automated pipeline. Several aspects of the quality of the resulting question-answer pairs are evaluated by three annotators. Finally, we describe and make available for review the deployed prototype for the flash card application.
Academic Expert Finding via $(k,\mathcal{P})$-Core based Embedding over Heterogeneous Graphs
Authors: Anonymous
Date: 2021-07-26 00:00:00
Tasks: Document Embedding

Finding relevant experts in specified areas is often crucial for a wide range of applications in both academia and industry. Given a user input query and a large amount of academic knowledge (e.g., academic papers), expert finding aims to find and rank the experts who are most relevant to the given query, from the academic knowledge. Existing studies mainly focus on the embedding-based solutions that (1) consider academic papers' textual semantic similarities to a given query through document representation models and (2) extract the top-$n$ experts with the greatest similarities. Beyond the implicit textual semantics of papers, however, the papers’ explicit relationships (e.g., co-authorship, citation, and same-topic relationship) in a heterogeneous academic graph (e.g., DBLP) are critical for document representation, insofar as they help improve the expert finding quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this paper, we study the academic expert finding on heterogeneous graphs by considering the explicit relationships besides the implicit textual semantics of papers in one representation model. Specifically, we first define the  $(k,\mathcal{P})$-core to denote a cohesive community of papers that are closely connected via a meta-path $\mathcal{P}$ ($\mathcal{P}$ indicates the different relationships of papers). We then propose an offline $(k,\mathcal{P})$-core based document embedding model to capture papers' various explicit relationships for representation. Moreover, by using papers' embeddings, we present an online threshold algorithm (TA)-based method to efficiently return top-$n$ experts via a carefully designed proximity graph-based index (PG-Index). We extend our approach to support multiple relationships simultaneously for representation. Extensive experiments over real-world datasets demonstrate the effectiveness and efficiency of our approach.
Autonomous Vehicles for Ride-Hailing
Authors: Anonymous
Date: 2021-09-10 00:00:00
Tasks: Autonomous Vehicles

Problem definition: We consider a setting in which a ride-hailing platform operates a mixed fleet of conventional vehicles (CVs) and autonomous vehicles (AVs) over locations distributed spatially.  The CVs are operated by human drivers who make independent decisions about whether to work for the platform and where to position themselves when they become idle. The AVs are under the control of the platform. The platform decides on the wage it pays the drivers, the size of the AV fleet and how AVs are positioned spatially when they are idle. The platform can also make decisions on how much demand to accept for each pair of origins and destinations and whether to prioritize AVs or CVs in assigning vehicles to customer requests. Methodology: We use a fluid model to characterize the optimal decisions of the platform in equilibrium and contrast those with the optimal decisions in the absence of AVs. We study outcomes in equilibrium in terms of platform profit, customer service level, driver welfare, and driver productivity. Results: Among our findings, we show that the platform, whenever possible, would deploy the AVs in such a way as to reduce repositioning by CVs from the low demand location to the high demand location (inducing such repositioning by drivers is costly for the platform). The presence of AVs can also eliminate driver incentives that would otherwise force the platform to reject demand from the low demand location to force drivers to relocate to the high demand location. Even though demand is no longer exclusively fulfilled by CVs, we show that the income of drivers may not necessarily be harmed, and that drivers may not necessarily experience more idleness or empty travel. Moreover, we find that drivers can be strictly better off if the platform prioritizes AVs in assigning customer requests to vehicles.  Managerial implications: Our results uncover important ways the introduction of AVs affects the operation of a ride-hailing platform and highlight the nuanced impact of AVs on human drivers and customers. Our results are potentially useful to  policy makers in deciding on regulatory interventions that can induce more socially desirable outcomes with the introduction of AVs.
Directing the violence or admonishing it? A survey of contronymy and androcentrism in Google Translate and some recommendations
Authors: Anonymous
Date: 2021-08-18 00:00:00
Tasks: Data Augmentation, Machine Translation, Translation

The recent raft of high-profile gaffes involving neural machine translation technologies has brought to light the unreliability of this evolving technology. A worrisome
facet of the ubiquity of this technology is that it largely operates in a use-it-at-yourown-peril mode where the user is often unaware of either the idiosyncratic brittleness of the underlying neural translation model or when it is, that the translations
be deemed trustworthy and when they wouldn’t. These revelations have worryingly
coincided with other developments such as the emergence of large language models
that now produce biased and erroneous results, albeit with human-like fluency, the
use of back-translation as a data-augmentation strategy in so termed ’low-resource’
settings and the emergence of ’AI-enhanced legal-tech’ as a panacea that promises
’disruptive democratization’ of access to legal services. In the backdrop of these
quandaries, we present this cautionary tale where we shed light on the specifics
of the risks surrounding cavalier deployment of this technology by exploring two
specific failings: Androcentrism and Enantiosemy. In this regard, we empirically
investigate the fate of the pronouns and a list of contronyms when subjected to
back-translation using Google Translate. Through this, we seek to highlight the
prevalence of ’defaulting-to-the-masculine’ phenomenon in the context of engendered profession-related translations and also empirically demonstrate the scale and
nature of threats pertaining to contronymous phrases covering both current-affairs
and legal issues. Based on these observations, we have collected a series of recommendations that constitute the latter half of this paper. All of the code and datasets
generated in this paper have been open-sourced for the community to build on here:
https://github.com/rteehas/GT_study_recommendations.