Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Framework For Deep Learning based CSI Feedback

—Deep learning (DL) based channel state informa- tion (CSI) feedback in multiple-input multiple-output (MIMO) systems recently has attracted lots of attention from both academia and industrial. From a practical point of views, it is huge burden to train, transfer and deploy a DL model for each parameter conﬁguration of the base station (BS). In this paper, we propose a scalable and ﬂexible framework for DL based CSI feedback referred as scalable CsiNet (SCsiNet) to adapt a family of conﬁgured parameters such as feedback payloads, MIMO channel ranks, antenna numbers. To reduce model size and training complexity, the core block with pre- processing and post-processing in SCsiNet is reused among different parameter conﬁgurations as much as possible which is totally different from conﬁguration-orienting design. The pre-processing and post-processing are trainable neural network layers introduced for matching input/output dimensions and probability distributions. The proposed SCsiNet is evaluated by metrics of squared generalized cosine similarity (SGCS) and user throughput (UPT) in system level simulations. Compared to existing schemes (conﬁguration-orienting DL schemes and 3GPP Rel-16 Type-II codebook based schemes), the proposed scheme can signiﬁcantly reduce mode size and achieve 2% ∼ 10% UPT improvement for all parameter conﬁgurations.
Reconstruction Attack on Differential Private Trajectory Protection Mechanisms

Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due to their strong theoretical privacy guarantees, differential private publication mechanisms receive much attention. However, the large amount of noise required to achieve differential privacy yields structural differences, e.g., ship trajectories passing over land. We propose a deep learning-based Reconstruction Attack on Protected Trajectories (RAoPT), that leverages the mentioned differences to partly reconstruct the original trajectory from a differential private release. The evaluation shows that our RAoPT model can reduce the Euclidean and Hausdorff distances between the released and original trajectories by over 68 % on two real-world datasets under protection with ε ≤ 1. In this setting, the attack increases the average Jaccard index of the trajectories’ convex hulls, representing a user’s activity space, by over 180 %. Trained on the GeoLife dataset, the model still reduces the Euclidean and Hausdorff distances by over 60 % for T-Drive trajectories protected with a state-of-the-art mechanism (ε = 0.1). This work highlights shortcomings of current trajectory publication mechanisms, and thus motivates further research on privacy-preserving publication schemes.
Moving gravitational wave sources at cosmological distances: Impact on the measurement of the Hubble constant

Standard sirens - GW sources with an EM counterpart - can be used to measure H_0 directly which should help to ease the existing Hubble tension. However, if the source has a relative velocity to the expanding universe on top of its motion due to the Hubble flow, a relativistic redshift affects the redshift of the EM counterpart and the apparent distance of the GW source, and thus it needs to be corrected to obtain accurate measurements. We study the effect of such a relative velocity on GWs for a source in an expanding universe showing that the total redshift of the wave is equal to the product of the relativistic redshift and the cosmological redshift. We, further, find that a relative velocity of the source changes its apparent distance by a factor (1+z_rel)^2 in contrast to a linear factor for the cosmological redshift. We discuss that the additional factor for the relativistic redshift is a consequence of a velocity-dependent amplitude for GWs. We consider the effect of the relative velocity on the chirp mass and the apparent distance of the source an observer would infer when ignoring this velocity. We find that for different astrophysical scenarios the error can range between 0.1 % and 7 % for the chirp mass while the error in the apparent distance can be between 0.25 % and 15 %. Furthermore, we consider the error introduced in the measurement of H_0 using standard sirens for two cases: (i) when the effect of velocity on the redshift of the EM counterpart is considered but not on the apparent distance obtained from GWs and (ii) when the effect of the relative velocity is ignored completely. We find that in the first case the error can reach 1 % for a source moving due to the peculiar velocity of its host galaxy and that in the second case the error can be more than 5 % for a source at the distance of GW150914 with the same velocity.
A note on the Hurwitz problem and cone spherical metrics

We are motivated by cone spherical metrics on compact Riemann surfaces of positive genus to solve a special case of the Hurwitz problem. Precisely speaking, letting $d,\,g$ and $\ell$ be three positive integers and $\Lambda$ be the following collection of $(\ell+2)$ partitions of a positive integer $d$: \[(a_1,\cdots, a_p),\,(b_1,\cdots, b_q),\,(m_1+1,1,\cdots,1),\cdots, (m_{\ell}+1,1,\cdots,1),\] where $(m_1,\cdots, m_{\ell})$ is a partition of $p+q-2+2g$, we prove that there exists a branched cover from some compact Riemann surface of genus $g$ to the Riemann sphere ${\Bbb P}^1$ with branch data $\Lambda$. An analogue for the genus-zero case was found by the first two authors ({\it Algebra Colloq.} {\bf 27} (2020), no. 2, 231-246), who were stimulated by such metrics on ${\Bbb P}^1$ and conjectured the veracity of the above statement there.
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs

As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators (e.g., NVIDIA GPUs and Google TPUs) and the rapidly growing number of operators. Deep learning compilers, such as Apache TVM, adopt declarative scheduling primitives to lower the bar of developing tensor programs. However, we show that this approach is insufficient to cover state-of-the-art tensor program optimizations (e.g., double buffering). In this paper, we propose to embed the scheduling process into tensor programs and use dedicated mappings, called task mappings, to define the computation assignment and ordering directly in the tensor programs. This new approach greatly enriches the expressible optimizations by allowing developers to manipulate tensor programs at a much finer granularity (e.g., allowing program-statement-level optimizations). We call the proposed method the task-mapping programming paradigm. In addition, we propose a new post-scheduling fusion optimization that allows developers to focus on scheduling every single operator and automates the fusion after scheduling. It greatly reduces the engineering efforts for operator fusion. Our proposed paradigm also constructs an efficient hardware-centric schedule space, which is agnostic to the program input size and greatly reduces the tuning time. With the proposed paradigm, we implement a deep learning compiler Hidet. Extensive experiments on modern convolution and transformer models show that Hidet outperforms state-of-the-art DNN inference framework, ONNX Runtime, and compiler, TVM equipped with scheduler AutoTVM and Ansor, by up to 1.48x (1.22x on average). It also reduces the tuning time by 20x and 11x compared with AutoTVM and Ansor, respectively. We open-sourced hidet at https://www.github. com/hidet-org/hidet.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction

Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/Jlfj345wf/RAPO.
Vision paper: causal inference for interpretable and robust machine learning in mobility analysis

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems.
Efficient Evaluation of Arbitrary Relational Calculus Queries

The relational calculus (RC) is a concise, declarative query language. However, existing RC query evaluation approaches are inefficient and often deviate from established algorithms based on finite tables used in database management systems. We devise a new translation of an arbitrary RC query into two safe-range queries, for which the finiteness of the query's evaluation result is guaranteed. Assuming an infinite domain, the two queries have the following meaning: The first is closed and characterizes the original query's relative safety, i.e., whether given a fixed database, the original query evaluates to a finite relation. The second safe-range query is equivalent to the original query, if the latter is relatively safe. We compose our translation with other, more standard ones to ultimately obtain two SQL queries. This allows us to use standard database management systems to evaluate arbitrary RC queries. We show that our translation improves the time complexity over existing approaches, which we also empirically confirm in both realistic and synthetic experiments.
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL using a hierarchical model to enable generalization to arbitrary goal routes, and measure performance using a closed-loop evaluation framework with simulated interactive agents. We train policies from expert trajectories collected from real vehicles driving over 100,000 miles in San Francisco, and demonstrate a steerable policy that can navigate robustly even in a zero-shot setting, generalizing to synthetic scenarios with novel goals that never occurred in real-world driving. We also demonstrate the importance of mixing closed-loop MGAIL losses with open-loop behavior cloning losses, and show our best policy approaches the performance of the expert. We evaluate our imitative model in both average and challenging scenarios, and show how it can serve as a useful prior to plan successful trajectories.
Connected (n, m)-point functions of diagonal 2-BKP tau-functions and spin double Hurwitz numbers

We derive an explicit formula for connected ( n, m)-point functions associated with an arbitrary diagonal tau-function of the 2-BKP hierarchy using the computation of neutral fermions and boson–fermion correspondence of type B and then apply this formula to the computation of connected spin double Hurwitz numbers. This is the type B analog of Wang and Yang [arXiv:2210.08712 (2022)].
Generalizing in the Real World with Representation Learning

Machine learning (ML) formalizes the problem of getting computers to learn from experience as optimization of performance according to some metric(s) on a set of data examples. This is in contrast to requiring behaviour specified in advance (e.g. by hard-coded rules). Formalization of this problem has enabled great progress in many applications with large real-world impact, including translation, speech recognition, self-driving cars, and drug discovery. But practical instantiations of this formalism make many assumptions - for example, that data are i.i. d. : independent and identically distributed - whose soundness is seldom investigated. And in making great progress in such a short time, the field has developed many norms and ad-hoc standards, focused on a relatively small range of problem settings. As applications of ML, particularly in artificial intelligence (AI) systems, become more pervasive in the real world, we need to critically examine these assumptions, norms, and problem settings, as well as the methods that have become de-facto standards. There is much we still do not understand about how and why deep networks trained with stochastic gradient descent are able to generalize as well as they do, why they fail when they do, and how they will perform on out-of-distribution data. In this thesis I cover some of my work towards better understanding deep net generalization, identify several ways assumptions and problem settings fail to generalize to the real world, and propose ways to address those failures in practice.
Towards Domain-Independent Supervised Discourse Parsing Through Gradient Boosting

Discourse analysis and discourse parsing have shown great impact on many important problems in the field of Natural Language Processing (NLP) (e.g., Ji and Smith (2017); Bhatia et al. (2015); Nejat et al. (2017); Gerani et al. (2014)). Given the direct impact of discourse annotations on model performance and interpretability, robustly extracting discourse structures from arbitrary documents is a key task to further improve computational models in NLP. To this end, a variety of complementary discourse theories have been proposed in the past, such as the lexicalized discourse framework (Webber et al., 2003), the Segmented Discourse Representation Theory (SDRT) (Asher, 1993; Asher et al., 2003), and the Rhetorical Structure Theory (RST) (Mann and Thompson, 1988), with RST focusing on the semantic and pragmatic structure of complete monologue documents, as used in this work. Despite the importance of discourse analysis and discourse parsing for the field of NLP and the obvious value of the RST discourse theory for many downstream applications, one major limitation for a wider application of discourse information is the severe data sparsity issue (for instance, the popular RST-DT (Carlson et al., 2002) and GUM (Zeldes, 2017) treebanks do not exceed a minuscule number of 400 documents). Furthermore, while the data sparsity issue has been a long-standing problem, modern, dataintensive machine learning approaches further reinforce its severity. In general, three modelling alternatives have been established in the current landscape: (i) Supervised approaches (e.g., Ji and Eisenstein (2014); Feng and Hirst (2014); Joty et al. (2015); Li et al. (2016); Wang et al. (2017); Guz et al. (2020)), performing well in the domain in which they are trained, however, obtain severely reduced performance if a domain shift is present, as shown in Huber and Carenini (2019, 2020). (ii) Distantly supervised models (e.g., Huber and Carenini (2019, 2020); Nishida and Nakayama (2020); Karimi and Tang (2019); Liu et al. (2019); Huber et al. (2021); Xiao et al. (2020)), aiming to overcome the domain adaptation problem by exploiting large-scale supervised datasets from context-sensitive auxiliary tasks (e.g., sentiment analysis). (iii) Selfsupervised/unsupervised methods (e.g., Zhu et al. (2020); Koto et al. (2021); Wu et al. (2020); Kobayashi et al. (2019); Huber and Carenini (2021, 2022)), predicting discourse from either pre-trained language models, autoencoder style frameworks, or by recursively computing dissimilarity scores.
Methods To Ensure Privacy Regarding Medical Data -- Including an examination of the differential privacy algorithm RAPPOR and its implementation in"Cryptool 2"

This document examines several applicable methods to ensure privacy of data gathered in the health care sector. To ensure a common understanding of the topic, the introduction explains the need for anonymization methods based on an example. Next, reasons for data collection are introduced in connection to the purpose to protect mentioned data, as well as currently applicable privacy laws to enforce this privacy. The question"What kind of privacy we are talking about and what conditions have to be fulfilled? "is dealt with in the subsequent chapter"Differential Privacy". Thus being established, common anonymization methods are explained and reviewed for their use in the healthcare sector. The RAPPOR algorithm and its differential privacy is dealt with in more detail before coming to a conclusion.
Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion

An attached arm can significantly increase the applicability of legged robots to several mobile manipulation tasks that are not possible for the wheeled or tracked counterparts. The standard hierarchical control pipeline for such legged manipulators is to decouple the controller into that of manipulation and locomotion. However, this is ineffective. It requires immense engineering to support coordination between the arm and legs, and error can propagate across modules causing non-smooth unnatural motions. It is also biological implausible given evidence for strong motor synergies across limbs. In this work, we propose to learn a unified policy for whole-body control of a legged manipulator using reinforcement learning. We propose Regularized Online Adaptation to bridge the Sim2Real gap for high-DoF control, and Advantage Mixing exploiting the causal dependency in the action space to overcome local minima during training the whole-body system. We also present a simple design for a low-cost legged manipulator, and find that our unified policy can demonstrate dynamic and agile behaviors across several task setups. Videos are at https://maniploco.github. io
Simultaneous Translation for Unsegmented Input: A Sliding Window Approach

In the cascaded approach to spoken language translation (SLT), the ASR output is typically punctuated and segmented into sentences before being passed to MT, since the latter is typically trained on written text. However, erroneous segmentation, due to poor sentence-final punctuation by the ASR system, leads to degradation in translation quality, especially in the simultaneous (online) setting where the input is continuously updated. To reduce the influence of automatic segmentation, we present a sliding window approach to translate raw ASR outputs (online or offline) without needing to rely on an automatic segmenter. We train translation models using parallel windows (instead of parallel sentences) extracted from the original training data. At test time, we translate at the window level and join the translated windows using a simple approach to generate the final translation. Experiments on English-to-German and English-to-Czech show that our approach improves 1.3--2.0 BLEU points over the usual ASR-segmenter pipeline, and the fixed-length window considerably reduces flicker compared to a baseline retranslation-based online SLT system.
MVP: Detection of motif-making and -breaking mutations

Background: DNA, RNA, and protein sequence motifs can be recognition sites for biological functions such as regulation, DNA base modification, and molecular binding in general. The gain and loss of such motifs can carry important consequences. When comparing sequences, the analysis of individual variants does not impart an understanding of the impact on these sites. Rather, only when these variants considered together with their neighbors and the original sequence context does this become possible. Results: The motif-variant probe ( mvp ) makes this consideration, counting instances of user-specified sequence motifs before and after mutation and reports those that result in motif gain or loss. mvp can perform a similar analysis for proteins with amino acid variant data. The software is freely available at https://lpcdrp.gitlab. io/mvp and also installable with the conda package manager. Conclusions: The ability to easily search for variants affecting any motif, together with the simultaneous consideration of neighboring variants makes mvp a versatile tool to aid in a less-frequented dimension of comparative genomics. ,
Critical Metallicity of Cool Supergiant Formation. I. Effects on Stellar-mass Loss and Feedback

This paper systematically studies the relation between metallicity and mass loss of massive stars. We perform one-dimensional stellar evolution simulations and build a grid of ∼2000 models with initial masses ranging between 11 and 60 M ⊙ and absolute metallicities Z between 0.00001 and 0.02. Steady-state winds, comprising hot main-sequence winds and cool supergiant winds, are the main drivers of the mass loss of massive stars in our models. We calculate the total mass loss over the stellar lifetime for each model. Our results reveal the existence of a critical metallicity Z c at Z ∼ 10−3, where the mass loss exhibits a dramatic jump. If Z > Z c, massive stars tend to evolve into cool supergiants, and a robust cool wind is operational. In contrast, if Z < Z c, massive stars usually remain as blue supergiants, wherein the cool wind is not activated and the mass loss is generally weak. Moreover, we calculate the wind feedback in a 105 M ⊙ star cluster with the Salpeter initial mass function. The kinetic energy released by winds does not exhibit any significant transition at Z c because the wind velocity of a cool supergiant wind is low and contributes little to the kinetic energy. The effects of critical metallicity provide implications for the fates of metal-poor stars in the early universe.