Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Matching Point

In this paper, we propose a neural network-based point cloud registration method named Iterative Matching Point (IMP). Our model iteratively matches features of points from two point clouds and solve the rigid body motion by minimizing the distance between the matching points. The idea is similar to Iterative Closest Point (ICP), but our model determines correspondences by comparing geometric features instead of just finding the closest point. Thus it does not suffer from the local minima problem and can handle point clouds with large rotation angles. Furthermore, the robustness of the feature extraction network allows IMP to register partial and noisy point clouds. Experiments on the ModelNet40 dataset show that our method outperforms existing point cloud registration method by a large margin, especially when the initial rotation angle is large. Also, its capability generalizes to real world 2.5D data without training on them.
Convex Relaxation Based Locational Marginal Prices

We propose and analyze semidefinite relaxation based locational marginal prices (RLMPs) for real and reactive power in electricity markets. Our analysis reveals that when the non-convex economic dispatch problem has zero duality gap, the RLMPs exhibit properties similar to locational marginal prices with linearized power flow equations. Otherwise, they behave similar to convex hull prices. Restricted to radial distribution networks, RLMPs reduce to second-order cone relaxation based distribution locational marginal prices. We illustrate our theoretical results on numerical examples.
CNN-Based Analog CSI Feedback in FDD MIMO-OFDM Systems

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral efficiency. Deep Learning (DL)-based CSI feedback compression schemes have received a lot of attention recently as they provide significant improvements in compression efficiency; however, they still require reliable feedback links to convey the compressed CSI information to the BS. Instead, we propose here a Convolutional neural network (CNN)-based analog feedback scheme, called AnalogDeepCMC, which directly maps the downlink CSI to uplink channel input. Corresponding noisy channel outputs are used by another CNN to reconstruct the downlink channel estimate. The proposed analog scheme not only outperforms existing digital CSI feedback schemes in terms of the achievable downlink rate, but also simplifies the feedback transmission as it does not require explicit quantization, coding, and modulation, and provides a low-latency alternative particularly in rapidly changing MIMO channels, where the CSI needs to be estimated and fed back periodically.
Magnetoelectric effect of a conducting sphere near a planar topological insulator

When time-reversal symmetry is broken on its surface, topological insulators exhibit a magnetoelectric response which is described by axion electrodynamics. A direct consequence of this theory is the appearance of a magnetic field that resembles the one produced by a magnetic image monopole when a point-like electric charge is located near the surface of the material. In this paper we investigate the more realistic problem when the point-like charge is replaced by a finite size sphere at constant potential. We calculate the electromagnetic fields using the potential formulation in a particular bispherical coordinate system. We find that the electromagnetic fields can be interpreted in terms of point electric and image magnetic charges as if the medium were the vacuum. As a manifestation of the magnetoelectric effect, we highlight the resulting magnetic field, which we analyze in detail along the symmetry axis, since such estimates could be useful in evaluating the experimental possibility of its measurement via sensible magnetometers. Our numerical estimates show that the proposed setup provides a magnetic field strength in the range of 10-100 mG, which is attainable with present day sensitivities in NV center-diamond magnetometers, for example.
Radial-velocity jitter of stars as a function of observational timescale and stellar age

Context. Stars show various amounts of radial-velocity (RV) jitter due to varying stellar activity levels. The typical amount of RV jitter as a function of stellar age and observational timescale has not yet been systematically quantified, although it is often larger than the instrumental precision of modern high-resolution spectrographs used for Doppler planet detection and characterization. Aims. We aim to empirically determine the intrinsic stellar RV variation for mostly G and K dwarf stars on different timescales and for different stellar ages independently of stellar models. We also focus on young stars (≲30 Myr), where the RV variation is known to be large. Methods. We use archival FEROS and HARPS RV data of stars which were observed at least 30 times spread over at least two years. We then apply the pooled variance (PV) technique to these data sets to identify the periods and amplitudes of underlying, quasiperiodic signals. We show that the PV is a powerful tool to identify quasiperiodic signals in highly irregularly sampled data sets. Results. We derive activity-lag functions for 20 putative single stars, where lag is the timescale on which the stellar jitter is measured. Since the ages of all stars are known, we also use this to formulate an activity–age–lag relation which can be used to predict the expected RV jitter of a star given its age and the timescale to be probed. The maximum RV jitter on timescales of decades decreases from over 500 m s−1 for 5 Myr-old stars to 2.3 m s−1 for stars with ages of around 5 Gyr. The decrease in RV jitter when considering a timescale of only 1 d instead of 1 yr is smaller by roughly a factor of 4 for stars with an age of about 5 Myr, and a factor of 1.5 for stars with an age of 5 Gyr. The rate at which the RV jitter increases with lag strongly depends on stellar age and reaches 99% of the maximum RV jitter over a timescale of a few days for stars that are a few million years old, up to presumably decades or longer for stars with an age of a few gigayears.
The Task Analysis Cell Assembly Perspective

An entirely novel synthesis combines the applied cognitive psychology of a task analytic approach with a neural cell assembly perspective that models both brain and mind function during task performance; similar cell assemblies could be implemented as an artificially intelligent neural network. A simplified cell assembly model is introduced and this leads to several new representational formats that, in combination, are demonstrated as suitable for analysing tasks. The advantages of using neural models are exposed and compared with previous research that has used symbolic artificial intelligence production systems, which make no attempt to model neurophysiology. For cognitive scientists, the approach provides an easy and practical introduction to thinking about brains, minds and artificial intelligence in terms of cell assemblies. In the future, subsequent developments have the potential to lead to a new, general theory of psychology and neurophysiology, supported by cell assembly based artificial intelligences.
Lucene for Approximate Nearest-Neighbors Search on Arbitrary Dense Vectors

We demonstrate three approaches for adapting the open-source Lucene search library to perform approximate nearest-neighbor search on arbitrary dense vectors, using similarity search on word embeddings as a case study. At its core, Lucene is built around inverted indexes of a document collection's (sparse) term-document matrix, which is incompatible with the lower-dimensional dense vectors that are common in deep learning applications. We evaluate three techniques to overcome these challenges that can all be natively integrated into Lucene: the creation of documents populated with fake words, LSH applied to lexical realizations of dense vectors, and k-d trees coupled with dimensionality reduction. Experiments show that the "fake words" approach represents the best balance between effectiveness and efficiency. These techniques are integrated into the Anserini open-source toolkit and made available to the community.
Memory-Augmented Recurrent Networks for Dialogue Coherence

Recent dialogue approaches operate by reading each word in a conversation history, and aggregating accrued dialogue information into a single state. This fixed-size vector is not expandable and must maintain a consistent format over time. Other recent approaches exploit an attention mechanism to extract useful information from past conversational utterances, but this introduces an increased computational complexity. In this work, we explore the use of the Neural Turing Machine (NTM) to provide a more permanent and flexible storage mechanism for maintaining dialogue coherence. Specifically, we introduce two separate dialogue architectures based on this NTM design. The first design features a sequence-to-sequence architecture with two separate NTM modules, one for each participant in the conversation. The second memory architecture incorporates a single NTM module, which stores parallel context information for both speakers. This second design also replaces the sequence-to-sequence architecture with a neural language model, to allow for longer context of the NTM and greater understanding of the dialogue history. We report perplexity performance for both models, and compare them to existing baselines.
Selectivity properties of spaces

This paper addresses several questions of Feng, Gruenhage, and Shen which arose from Michael's theory of continuous selections from countable spaces. We construct an example of a space which is $L$-selective but not $\mathbb{Q}$-selective from $\mathfrak{d}=\omega_1$, and an $L$-selective space which is not selective for a $P$-point ultrafilter from the assumption of $\mathsf{CH}$. We also produce $\mathsf{ZFC}$ examples of Frechet spaces where countable subsets are first countable which are not $L$-selective.
Modeling and simulation for varying die gap fluid extrusion

The analysis and the simulation of fluid extruded out of a die opening are of great importance in different industrial applications. Mathematical modeling and experimental studies of extrusion out of a constant die gap has been the subject of numerous publications in the literature. Extrusion Blow Molding is a polymer forming technique used to manufacture a wide range of large hollow parts such as fuel tanks used in the automotive industry. The die gap is varied during extrusion to adjust the manufactured product's final thickness and to compensate for uneven stretching during molding. In this work, mathematical modeling of varying die gap extrusion is introduced. Two models using different approaches are presented to model the time dependent effects of varying the die gap on the extrudate. In the first approach, physics based partial differential equations are used to simulate extrusion. The Navier Stokes equations are used as governing equations to simulate molten polymers. The governing equations are solved using Arbitrary Lagrangian Eulerian based Finite Element Method. A novel numerical method for defining the free surface of the extrudate is proposed. In addition, the effects of varying the die gap on the material velocity, pressure and extrudate shape are reported. In the second approach, a parameter identification scheme is used to obtain the parameters of a novel model with a predefined structure. The purpose of the proposed model is to replicate time dependent extrudate thickness of the physics based model.
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output

Deep neural networks achieve superior performance in challenging tasks such as image classification. However, deep classifiers tend to incorrectly classify out-of-distribution (OOD) inputs, which are inputs that do not belong to the classifier training distribution. Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge. In this paper, we propose a new OOD detection approach that can be easily applied to an existing classifier and does not need to have access to OOD samples. The detector is a one-class classifier trained on the output of an early layer of the original classifier fed with its original training set. We apply our approach to several low- and high-dimensional datasets and compare it to the state-of-the-art detection approaches. Our approach achieves substantially better results over multiple metrics.
MANUAL OF MOLECULAR AND CLINICAL LABORATORY IMMUNOLOGY

The work of medical practitioners is divided curiously, based on body parts, age, physiological processes, treatments and attitudes of society, into specialities such as cardiology, paediatrics, obstetrics, orthopaedics and sexual health. This haphazard 'assemblage of elements interacting with one another in ad hoc ways'1 is a legacy of the 19th century, when division of labour was all the rage, and was motivated not only by rational application of principles of pathology and physiology but also the hope among practitioners of mercantile advantage. Despite this dubious provenance, the medical specialties are now soundly institutionalized within teaching hospitals, medical faculties, specialist societies and colleges.
Fully Quantized Transformer for Improved Translation

State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsuccessful. To the best of our knowledge, we are the first to propose a quantization strategy inclusive of all components of the Transformer. We are also the first to show that it is possible to avoid any loss in translation quality with a fully quantized network. Indeed, our 8-bit models consistently score equal or higher BLEU than the full-precision variant on multiple translation datasets. Comparing ourselves to all previously proposed methods, we achieve state-of-the-art quantization results.
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Neural Network based controllers hold enormous potential to learn complex, high-dimensional functions. However, they are prone to overfitting and unwarranted extrapolations. PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations. However, optimizing to learn such a function and a bound is intractable for complex tasks. In this work, we propose a method to simultaneously learn such a function and estimate performance bounds that scale organically to high-dimensions, non-linear environments without making any explicit assumptions about the environment. We build our approach on a parallel that we draw between the formulations called ELBO and PAC Bayes when the risk metric is negative log likelihood. Through our experiments on multiple high dimensional MuJoCo locomotion tasks, we validate the correctness of our theory, show its ability to generalize better, and investigate the factors that are important for its learning. The code for all the experiments is available at this https URL.
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control

Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every different goal or do not infer the specific target in the presence of multiple goals from visual data. We introduce an end-to-end method that represents targetable visuomotor skills as a goal-parameterized neural network policy. By training on an informative subset of available goals with the associated target parameters, we are able to learn a policy that can zero-shot generalize to previously unseen goals. We evaluate our method in a representative 2D simulation of a button-grid and on both button-pressing and peg-insertion tasks on two different physical arms. We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments. We also successfully learn a mapping from target pixel coordinates to a robot policy to complete a specified goal.
GCI Detection from Raw Speech Using a Fully-Convolutional Network

Glottal Closure Instants (GCI) detection consists in automatically detecting temporal locations of most significant excitation of the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithms have been proposed for this purpose. Recently, new approaches using convo-lutional neural networks have emerged, with encouraging results. Following this trend, we propose a simple approach that performs a mapping from the speech waveform to a target signal from which the GCIs are obtained by peak-picking. However, the ground truth GCIs used for training and evaluation are usually extracted from EGG signals, which are not perfectly reliable and often not available. To overcome this problem, we propose to train our network on high-quality synthetic speech with perfect ground truth. The performances of the proposed algorithm are compared with three other state-of-the-art approaches using publicly available datasets, and the impact of using controlled synthetic or real speech signals in the training stage is investigated. The experimental results demonstrate that the proposed method obtains similar or better results than other state-of-the-art algorithms and that using large synthetic datasets with many speakers offers a better generalization ability than using a smaller database of real speech and EGG signals.
USTAR: Online Multimodal Embedding for Modeling User-Guided Spatiotemporal Activity

Building spatiotemporal activity models for people’s activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics. With the emergence of Geo-Tagged Social Media (GTSM) records, previous studies demonstrate the potential of GTSM records for spatiotemporal activity modeling. State-of-the-art methods for this task embed different modalities (location, time, and text) of GTSM records into a single embedding space. However, they ignore Non-GeoTagged Social Media (NGTSM) records, which generally account for the majority of posts (e.g., more than 95% in Twitter), and could represent a great source of information to alleviate the sparsity of GTSM records. Furthermore, in the current spatiotemporal embedding techniques, less focus has been given to the users, who exhibit spatially motivated behaviors. To bridge this research gap, this work proposes USTAR, a novel online learning method for User-guided SpatioTemporal Activity Representation, which (1) embeds locations, time, and text along with users into the same embedding space to capture their correlations; (2) uses a novel collaborative filtering approach to incorporate both NGTSM and GTSM records in learning; and (3) introduces a novel sampling technique to learn spatiotemporal representations in an online fashion to accommodate recent information into the embedding space, while avoiding overfitting to recent records and frequently appearing units in social media streams. Our results show that USTAR substantially improves the state-of-the-art for region retrieval and keyword retrieval and its potential to be applied to other downstream applications such as local event detection.
Contrastive Representation Distillation

Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation. Code: this http URL
Continuous-Time Principal-Agent Problem in Degenerate Systems

In this paper we present a variational calculus approach to Principal-Agent problem with a lump-sum payment on finite horizon in degenerate stochastic systems, such as filtered partially observed linear systems. Our work extends the existing methodologies in the Principal-Agent literature using dynamic programming and BSDE representation of the contracts in the non-degenerate controlled stochastic systems. We first solve the Principal's problem in an enlarged set of contracts defined by a forward-backward SDE system given by the first order condition of the Agent's problem using variational calculus. Then we use the sufficient condition of the Agent's problem to verify that the optimal contract that we obtain by solving the Principal's problem is indeed implementable (i.e. belonging to the admissible contract set). Importantly we consider the control problem in a weak formulation. Finally, we give explicit solution of the Principal-Agent problem in partially observed linear systems and extend our results to some mean field interacting Agents case.
A GMRT 150 MHz search for variables and transients in Stripe 82

We have carried out a dedicated transient survey of 300 deg2 of the SDSS Stripe 82 region using the Giant Metrewave Radio Telescope (GMRT) at 150 MHz. Our multi-epoch observations, together with the TGSS survey, allow us to probe variability and transient activity on four different time-scales, beginning with 4 h and up to 4 yr. Data calibration, RFI flagging, source finding, and transient search were carried out in a semi-automated pipeline incorporating the SPAM recipe. This has enabled us to produce superior-quality images and carry out reliable transient search over the entire survey region in under 48 h post-observation. Among the few thousand unique point sources found in our 5σ single-epoch catalogues (flux density thresholds of about 24, 20, 16, and 18 mJy on the respective time-scales), we find <0.08 per cent, 0.01 per cent, <0.06 per cent, and 0.05 per cent to be variable (beyond a significance of 4σ and fractional variability of 30 per cent) on time-scales of 4 h, 1 d, 1 month, and 4 yr, respectively. This is substantially lower than that in the GHz sky, where ∼1 per cent of the persistent point sources are found to be variable. Although our survey was designed to probe a superior part of the transient phase space, our transient search did not yield any significant candidates. The transient (preferentially extragalactic) rate at 150 MHz is therefore <0.005 on time-scales of 1 month and 4 yr, and <0.002 on time-scales of 1 d and 4 h, beyond 7σ detection threshold. We put these results in perspective with the previous studies and give recommendations for future low-frequency transient surveys.