Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction

Alleviating the delayed feedback problem is of crucial importance for the conversion rate(CVR) prediction in online advertising. Previous delayed feedback modeling methods using an observation window to balance the trade-off between waiting for accurate labels and consuming fresh feedback. Moreover, to estimate CVR upon the freshly observed but biased distribution with fake negatives, the importance sampling is widely used to reduce the distribution bias. While effective, we argue that previous approaches falsely treat fake negative samples as real negative during the importance weighting and have not fully utilized the observed positive samples, leading to suboptimal performance. In this work, we propose a new method, DElayed Feedback modeling with UnbiaSed Estimation, (DEFUSE), which aim to respectively correct the importance weights of the immediate positive, the fake negative, the real negative, and the delay positive samples at finer granularity. Specifically, we propose a two-step optimization approach that first infers the probability of fake negatives among observed negatives before applying importance sampling. To fully exploit the ground-truth immediate positives from the observed distribution, we further develop a bi-distribution modeling framework to jointly model the unbiased immediate positives and the biased delay conversions. Experimental results on both public and our industrial datasets validate the superiority of DEFUSE. Codes are available at https://github.com/ychen216/DEFUSE.git.
Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach

Vital node identification is the problem of finding nodes of highest importance in complex networks. This problem has crucial applications in various contexts such as viral marketing or controlling the propagation of virus or rumours in real-world networks. Existing approaches for vital node identification mainly focus on capturing the importance of a node through a mathematical expression which directly relates structural properties of the node to its vitality. Although these heuristic approaches have achieved good performance in practice, they have weak adaptability, and their performance is limited to specific settings and certain dynamics. Inspired by the power of machine learning models for efficiently capturing different types of patterns and relations, we propose a machine learning-based, data driven approach for vital node identification. The main idea is to train the model with a small portion of the graph, say 0.5% of the nodes, and do the prediction on the rest of the nodes. The ground-truth vitality for the train data is computed by simulating the SIR diffusion method starting from the train nodes. We use collective feature engineering where each node in the network is represented by incorporating elements of its connectivity, degree and extended coreness. Several machine learning models are trained on the node representations, but the best results are achieved by a Support Vector Regression machine with RBF kernel. The empirical results confirms that the proposed model outperforms state-of-the-art models on a selection of datasets, while it also shows more adaptability to changes in the dynamics parameters. © 2021 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.
Technical inefficiency in wheat production - A socioeconomic analysis*

The study reports the results of an investigation conducted in the Sone canal command area of Bihar to find the level of technical inefficiency in the production of wheat and to asses the effect of farm-specific socio-economic variables on this inefficiency. A stochastic frontier production function has been estimated to determine technical inefficiency of individual farm and variance as well as regression analyses have been carried out to find the influence of socio-economic factors. The farms in the Sone command area are producing wheat with the average technical inefficiency of 18.71%, the maximum being 53.46% and minimum 4.41%. Wheat production in the study area can be increased to an extent of 18.71% with the available technology and resources if they are used judiciously. It has been found that factors like farm size, farmer's education, extension contacts, and experience in production and the percentage of good farm land influence the technical inefficiency negatively while fragmentations of farm land and age of farmer influence it positively. Location of the farm in the canal command, caste of the farmer and number of farm workers in the family have shown no significant relationship with technical inefficiency in wheat production in this area.
I'm Hearing (Different) Voices: Anonymous Voices to Protect User Privacy

In this paper, we present AltVoice -- a system designed to help user's protect their privacy when using remotely accessed voice services. The system allows a user to conceal their true voice identity information with no cooperation from the remote voice service: AltVoice re-synthesizes user's spoken audio to sound as if it has been spoken by a different, private identity. The system converts audio to its textual representation at its midpoint, and thus removes any linkage between the user's voice and the generated private voices. We implement AltVoice and we propose six different methods to generate private voice identities, each is based on a user-known secret. We identify the system's trade-offs, and we investigate them for each of the proposed identity generation methods. Specifically, we investigate generated voices' diversity, word error rate, perceived speech quality and the success of attackers under privacy compromise and authentication compromise attack scenarios. Our results show that AltVoice-generated voices are not easily linked to original users, enabling users to protect themselves from voice data leakages and allowing for the revocability of (generated) voice data; akin to using passwords. However the results also show further work is needed on ensuring that the produced audio is natural, and that identities of private voices are distinct from one another. We discuss the future steps into improving AltVoice and the new implications that its existence has for the creations of remotely accessed voice services.
Evaluating MT Systems: A Theoretical Framework

This paper outlines a theoretical framework using which different automatic metrics can be designed for evaluation of Machine Translation systems. It introduces the concept of {\em cognitive ease} which depends on {\em adequacy} and {\em lack of fluency}. Thus, cognitive ease becomes the main parameter to be measured rather than comprehensibility. The framework allows the components of cognitive ease to be broken up and computed based on different linguistic levels etc. Independence of dimensions and linearly combining them provides for a highly modular approach. The paper places the existing automatic methods in an overall framework, to understand them better and to improve upon them in future. It can also be used to evaluate the newer types of MT systems, such as speech to speech translation and discourse translation.
The Role of Disk Tearing and Precession in the Observed Variability of Pleione

We acquired Hα spectroscopic observations from 2005 to 2019 showing Pleione has transitioned from a Be phase to a Be-shell phase during this period. Using the radiative transfer code hdust, we created a grid of ∼100,000 disk models for Pleione. We successfully reproduced the observed transition with a disk model that varies in inclination while maintaining an equatorial density of ρ0(r)=3×10−11(r/Req)−2.7gcm−3 , and an Hα-emitting region extending to 15 R eq. We use a precessing disk model to extrapolate the changing disk inclination over 120 yr and follow the variability in archival observations. The best-fit disk model precesses over a line-of-sight inclination between ∼25° and ∼144° with a precessional period of ∼80.5 yr. Our precessing models match some of the observed variability but fail to reproduce all of the historical data available. Therefore, we propose an ad hoc model based on our precessing disk model inspired by recent smoothed particle hydrodynamics simulations of similar systems, where the disk tears due to the tidal influence of a companion star. In this model, a single disk is slowly tilted to an angle of 30° from the stellar equator over 34 yr. Then, the disk is torn by the companion’s tidal torque, with the outer region separating from the innermost disk. The small inner disk returns to the stellar equator as mass injection remains constant. The outer disk precesses for ∼15 yr before gradually dissipating. The process repeats every 34 yr and reproduces all trends in Pleione’s variability.
The discovery of a radio galaxy of at least 5 Mpc

Context. Giant radio galaxies (GRGs, or colloquially ‘giants’) are the Universe’s largest structures generated by individual galaxies. They comprise synchrotron-radiating active galactic nucleus ejecta and attain cosmological (megaparsec-scale) lengths. However, the main mechanisms that drive their exceptional growth remain poorly understood. Aims. To deduce the main mechanisms that drive a phenomenon, it is usually instructive to study extreme examples. If there exist host galaxy characteristics that are an important cause for GRG growth, then the min , 1 = 46 ± 1 pT and B min , 2 = 46 ± 3 pT. Conclusions. We have discovered what is in projection the largest known structure made by a single galaxy – a GRG with a projected proper length l p = 4 . 99 ± 0 . 04 Mpc. The true proper length is at least l min = 5 . 04 ± 0 . 05 Mpc . Beyond geometry, Alcyoneus and its host are suspiciously ordinary: the total low-frequency luminosity density, stellar mass, and super-massive black hole mass are all lower than, though similar to, those of the medial GRG. Thus, very massive galaxies or central black holes are not necessary to grow large giants, and, if the observed state is representative of the source over its lifetime, neither is high radio power. A low-density environment remains a possible explanation. The source resides in a ﬁlament of the Cosmic Web, with which it might have signiﬁcant thermodynamic interaction. The pressures in the lobes are the lowest hitherto found, and Alcyoneus therefore represents the most promising radio galaxy yet to probe the warm–hot inter-galactic medium.
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness

We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversarially robust only on a small fraction $\beta \ll 1$ of the data distribution. Our proposed notion of barely robust learning requires robustness with respect to a"larger"perturbation set; which we show is necessary for strongly robust learning, and that weaker relaxations are not sufficient for strongly robust learning. Our results reveal a qualitative and quantitative equivalence between two seemingly unrelated problems: strongly robust learning and barely robust learning.
Wildfires and Climate Change: What does social media tell us on linkages and public understanding? (Preliminary White Paper, February 11, 2022)

Wildfires are increasing in frequency and size across the western U.S., with some of the deadliest fires in recorded history occurring in the last few years. The public, as well as elected officials, use social media to convey opinions and knowledge on topics that are impacting their communities. We utilize the platform Twitter to assess connections of wildfire to climate change during recent events and evaluate the differences in knowledge between the public and their government officials. Results show some linkages of wildfire cause and effect, although this relationship was not large (only 5%) and was even lower at the governmental level (2%), suggesting that a broader number of the public and government did not relate climate change to recent extreme wildfires.
The xmuspeech system for multi-channel multi-party meeting transcription challenge

This paper describes the system developed by the XMUSPEECH team for the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT). For the speaker diarization task, we propose a multi-channel speaker diarization system that obtains spatial information of speaker by Difference of Arrival (DOA) technology. Speaker-spatial embedding is generated by x-vector and s-vector derived from Filter-and-Sum Beamforming (FSB) which makes the embedding more robust. Specifically, we propose a novel multi-channel sequence-to-sequence neural network architecture named Discriminative Multi-stream Neural Network (DMSNet) which consists of Attention Filter-and-Sum block (AFSB) and Conformer encoder. We explore DMSNet to address overlapped speech problem on multi-channel audio. Compared with LSTM based OSD module, we achieve a decreases of 10.1% in Detection Error Rate(DetER). By performing DMSNet based OSD module, the DER of cluster-based diarization system decrease significantly form 13.44% to 7.63%. Our best fusion system achieves 7.09% and 9.80% of the diarization error rate (DER) on evaluation set and test set.
Ultrasonic Backscatter Communication for Implantable Medical Devices

This paper proposes an ultrasonic backscatter communication (UsBC) system for passive implantable medical devices (IMDs) that can operate without batteries, enabling versatile revolutionary applications for future healthcare. The proposed UsBC system consists of a reader and a tag. The reader sends interrogation pulses to the tag. The tag backscatters the pulses based on the piezoelectric effect of a piezo transducer. We present several basic modulation schemes for UsBC by impedance matching of the piezo transducer. To mitigate the interference of other scatters in the human body, the tag transmits information bits by codeword mapping, and the reader performs codeword matching before energy detection in the reader. We further derive the theoretical bit-error rate (BER) expression. Monte Carlo simulations verify the theoretical analysis and show that the passive UsBC can achieve low BER and low complexity, which is desirable for size- and energy-constrained IMDs.
Natural Image Stitching Using Depth Maps

Natural image stitching (NIS) aims to create one natural-looking mosaic from two overlapping images that capture the same 3D scene from different viewing positions. Challenges inevitably arise when the scene is non-planar and the camera baseline is wide, since parallax becomes not negligible in such cases. In this paper, we propose a novel NIS method using depth maps, which generates natural-looking mosaics against parallax in both overlapping and non-overlapping regions. Firstly, we construct a robust fitting method to filter out the outliers in feature matches and estimate the epipolar geometry between input images. Then, we draw a triangulation of the target image and estimate multiple local homographies, one per triangle, based on the locations of their vertices, the rectified depth values and the epipolar geometry. Finally, the warping image is rendered by the backward mapping of piece-wise homographies. Panorama is then produced via average blending and image inpainting. Experimental results demonstrate that the proposed method not only provides accurate alignment in the overlapping regions but also virtual naturalness in the non-overlapping region.
Motif-Topology and Reward-Learning Improved Spiking Neural Network for Efficient Multi-Sensory Integration

Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the new-generation artificial networks by incorporating more biological features than ANNs, including dynamic spiking neurons, functionally specified architectures, and efficient learning paradigms. In this paper, we propose a Motiftopology and Reward-learning improved SNN (MR-SNN) for efficient multi-sensory integration. MR-SNN contains 13 types of 3-node Motif topologies which are first extracted from independent single-sensory learning paradigms and then integrated for multi-sensory classification. The experimental results showed higher accuracy and stronger robustness of the proposed MR-SNN than other conventional SNNs without using Motifs. Furthermore, the proposed reward learning paradigm was biologically plausible and can better explain the cognitive McGurk effect caused by incongruent visual and auditory sensory signals.
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes

The modeling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of meters vs a pipe diameter of just a few inches. Approximating CFD models in a low-dimensional space, reduced-order models have been shown to produce accurate results with a speed-up of orders of magnitude. In this paper, we present a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM), which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by (i) using a domain decomposition approach; (ii) using dimensionality reduction to obtain a low-dimensional space in which to approximate the CFD model; (iii) training a neural network to make predictions for a single subdomain; and (iv) using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we compare Proper Orthogonal Decomposition with several types of autoencoder networks, known for their ability to compress information accurately and compactly. The comparison is assessed with two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network, which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce visually realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1 and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Inspection of the predicted liquid volume fractions shows a good match with the high fidelity model as shown in the results. Statistics of the flows obtained from the CFD simulations are compared to those of the AI-DDNIROM predictions to demonstrate the accuracy of our approach.
Toward Decentralized Cloud Storage With IPFS: Opportunities, Challenges, and Future Considerations

The InterPlanetary File System (IPFS) is a novel decentralized storage architecture, which provides decentralized cloud storage by building on founding principles of P2P networking and content addressing. IPFS is used by more than 230k peers per week and serves tens of millions of requests per day, which makes it an interesting large-scale operational network to study. While it is used as a building block in several projects and studies, its inner workings, properties, and implications have only been marginally explored in research. Thus, we provide an overview of the IPFS design and its core features, along with the opportunities that it opens, as well as the challenges that it faces because of its properties. Overall, IPFS presents an interesting set of characteristics and offers lessons that can help build decentralized systems of the future.
Speech Analysis for Automatic Mania Assessment in Bipolar Disorder

Bipolar disorder is a mental disorder that causes periods of manic and depressive episodes. In this work, we classify recordings from Bipolar Disorder corpus that contain 7 different tasks, into hypomania, mania, and remission classes using only speech features. We perform our experiments on splitted tasks from the interviews. Best results achieved on the model trained with 6th and 7th tasks together gives 0.53 UAR (unweighted average recall) result which is higher than the baseline results of the corpus.
Minimal Steklov eigenvalues on combinatorial graphs

In this paper, we study extremal problems of Steklov eigenvalues on combinatorial graphs by extending Friedman’s theory [Duke Math. J. 69 (1993), no. 3, 487–525] of nodal domains for Laplacian eigenfunctions to Steklov eigenfunctions, and solve an extremal problem for Steklov eigenvalues on combinatorial graphs that is an analogue of the extremal problem solved by Friedman [Duke Math. J. 83 (1996), no. 1, 1–18. ] for Laplacian eigenvalues. More precisely, we mainly show that the minimum of the i th Steklov eigenvalue on a connected combinatorial graph with n vertices is essentially attained by a star with each arm a minimal broom when i 6 | n , and attained by a regular comb with each tooth a minimal broom when i | n .
Deep Signatures - Learning Invariants of Planar Curves

We propose a learning paradigm for numerical approximation of differential invariants of planar curves. Deep neural-networks' (DNNs) universal approximation properties are utilized to estimate geometric measures. The proposed framework is shown to be a preferable alternative to axiomatic constructions. Specifically, we show that DNNs can learn to overcome instabilities and sampling artifacts and produce numerically-stable signatures for curves subject to a given group of transformations in the plane. We compare the proposed schemes to alternative state-of-the-art axiomatic constructions of group invariant arc-lengths and curvatures.
Awe Versus Aww: The Effectiveness of Two Kinds of Positive Emotional Stimulation on Stress Reduction for Online Content Moderators

When people have the freedom to create and post content on the internet, particularly anonymously, they do not always respect the rules and regulations of the websites on which they post, leaving other unsuspecting users vulnerable to sexism, racism, threats, and other unacceptable content in their daily cyberspace diet. However, content moderators witness the worst of humanity on a daily basis in place of the average netizen. This takes its toll on moderators, causing stress, fatigue, and emotional distress akin to the symptomology of post-traumatic stress disorder (PTSD). The goal of the present study was to explore whether adding positive stimuli to breaktimes-images of baby animals or beautiful, aweinspiring landscapes-could help reduce the negative side-effects of being a content moderator. To test this, we had over 300 experienced content moderators read and decide whether 200 fake text-based social media posts were acceptable or not for public consumption. Although we set out to test positive emotional stimulation, however, we actually found that it is the cumulative nature of the negative emotions that likely negates most of the effects of the intervention: the longer the person had practiced content moderation, the stronger their negative experience. Connections to compassion fatigue and how best to spend work breaks as a content moderator are discussed.