Miracle-dz/UniFAR-FTUs huggingface dataset:
Prototype-Anchored Learning for Learning with Imperfect Annotations

The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resulting labels may be class imbalanced, noisy or human biased. It is challenging to learn unbiased classification models from imperfectly annotated datasets, on which we usually suffer from overfitting or underfitting. In this work, we thoroughly investigate the popular softmax loss and margin-based loss, and offer a feasible approach to tighten the generalization error bound by maximizing the minimal sample margin. We further derive the optimality condition for this purpose, which indicates how the class prototypes should be anchored. Motivated by theoretical analysis, we propose a simple yet effective method, namely prototype-anchored learning (PAL), which can be easily incorporated into various learning-based classification schemes to handle imperfect annotation. We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
Fluctuating kinetic theory and fluctuating hydrodynamics of aligning active particles: the dilute limit

Kinetic and hydrodynamic theories are widely employed for describing the collective behavior of active matter systems. At the fluctuating level, these have been obtained from explicit coarse-graining procedures in the limit where each particle interacts weakly with many others, so that the total forces and torques exerted on each of them is of order unity at all times. Such limit is however not relevant for dilute systems that mostly interact via alignment; there, collisions are rare and make the self-propulsion direction to change abruptly. We derive a fluctuating kinetic theory, and the corresponding fluctuating hydrodynamics, for aligning self-propelled particles in the limit of dilute systems. We discover that fluctuations at kinetic level are not Gaussian and depend on the interactions among particles, but that only their Gaussian part survives in the hydrodynamic limit. At variance with fluctuating hydrodynamics for weakly interacting particles, we find that the noise variance at hydrodynamic level depends on the interaction rules among particles and is proportional to the square of the density, reflecting the binary nature of the aligning process. The results of this paper, which are derived for polar self-propelled particles with polar alignment, could be straightforwardly extended to polar particles with nematic alignment or to fully nematic systems.
A Review of Published Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging

Abstract Machine learning (ML) is a subfield of Artificial intelligence (AI), and its applications in radiology are growing at an ever-accelerating rate. The most studied ML application is the automated interpretation of images. However, natural language processing (NLP), which can be combined with ML for text interpretation tasks, also has many potential applications in radiology. One such application is automation of radiology protocolling, which involves interpreting a clinical radiology referral and selecting the appropriate imaging technique. It is an essential task which ensures that the correct imaging is performed. However, the time that a radiologist must dedicate to protocolling could otherwise be spent reporting, communicating with referrers, or teaching. To date, there have been few publications in which ML models were developed that use clinical text to automate protocol selection. This article reviews the existing literature in this field. A systematic assessment of the published models is performed with reference to best practices suggested by machine learning convention. Progress towards implementing automated protocolling in a clinical setting is discussed.
Proposing Dynamic Model of Functional Interactions of IoT Technological Innovation System by Using System Dynamics and Fuzzy DEMATEL

1 یارا ه‌ ‌ی ناروانف‌یروآون‌ماظن‌یدرکراک‌تلاماعت‌یایوپ‌لدم ه‌ ‌ی ‌ایشا‌تنرتنیا ییایوپ‌زا‌هدافتسا‌اب ‌ اه ید‌و‌متسیس‌ی یزاف‌لتام ‌ ‌ دمحم ‌ یسوم ‌ یناخ 1 * ، ‌ همطاف ‌ یفقث 2 ‌، دمحم ‌ نسح ‌ هداز 3 ‌، یقداص‌میهاربا‌دمحم 4 ‌ 1 ،رايشناد هاگشناد هدکشناد ،نارهت ناریا ،نارهت ،تاعلاطا یروانف تیریدم هورگ ،تیریدم ی 2 ،رايشناد هاگشناد هدکشناد ،نارهت ناریا ،نارهت ،يتعنص تیریدم هورگ ،تیریدم ی 3 داتسا ، هاگشناد تيبرت هدکشناد ،سردم تیریدم ی و ،يسانش شناد و تاعلاطا ملع هورگ ،داصتقا ناریا ،نارهت
A Stable Implementation of a Well-posed 2-D Curvilinear Shallow Water Equations with No-Penetration Wall and Far-Field Boundary conditions

This paper presents a more stable implementation and a highly accurate numerical tool for predicting ﬂooding in urban areas. We started with the (linearised) well-posedness analysis by [1], where far-ﬁeld boundary conditions were proposed but extended their analysis to include wall boundaries. Speciﬁcally, high-order Summation-by-parts (SBP) ﬁnite-diﬀerence operators were employed to construct a scheme for the Shallow Water Equations. Subsequently, a stable SBP scheme with Simultaneous Approximation Terms that imposes both far-ﬁeld and wall boundaries was developed. Finally, we extended the schemes and their stability proofs to non-cartesian domains. To demonstrate the strength of the schemes, computations for problems with exact solutions were performed and a theoretical design-order with second-, third- and fourth (2,3,4) convergence rates obtained. Finally, we apply the 4 th -order scheme to steady river channel, canal (or ﬂood control channel simulations), and dam-break problems. The results show that the imposition of the boundary conditions are stable, and that they cause no visible reﬂections at the boundaries. The analysis adequately becomes nec-∗ essary in ﬂood risk management decisions since they can conﬁrm stable and accurate future predictions of ﬂoodplain variables. 2000 MSC: 65M06, 65M12, 35L65, 35F61
Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles

Due to the pivotal role of recommender systems (RS) in guiding customers toward the purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this article, we study shilling attacks where an adversarial party injects a number of fake user profiles for improper purposes. Conventional Shilling Attack approaches lack attack transferability (i.e., attacks are not effective on some victim RS models) and/or attack invisibility (i.e., injected profiles can be easily detected). To overcome these issues, we present learning to generate fake user profiles (Leg-UP), a novel attack model based on the generative adversarial network. Leg-UP learns user behavior patterns from real users in the sampled “templates” and constructs fake user profiles. To simulate real users, the generator in Leg-UP directly outputs discrete ratings. To enhance attack transferability, the parameters of the generator are optimized by maximizing the attack performance on a surrogate RS model. To improve attack invisibility, Leg-UP adopts a discriminator to guide the generator to generate undetectable fake user profiles. Experiments on benchmarks have shown that Leg-UP exceeds state-of-the-art shilling attack methods on a wide range of victim RS models. The source code of our work is available at: https://github.com/XMUDM/ShillingAttack.
Towards Green ASR: Lossless 4-bit Quantization of a Hybrid TDNN System on the 300-hr Switchboard Corpus

State-of-the-art time automatic speech recognition (ASR) systems are becoming increasingly complex and expensive for practical applications. This paper presents the development of a high performance and low-footprint 4-bit quantized LF-MMI trained factored time delay neural networks (TDNNs) based ASR system on the 300-hr Switchboard corpus. A key feature of the overall system design is to account for the fine-grained, varying performance sensitivity at different model components to quantization errors. To this end, a set of neural architectural compression and mixed precision quantization approaches were used to facilitate hidden layer level auto-configuration of optimal factored TDNN weight matrix subspace dimensionality and quantization bit-widths. The proposed techniques were also used to produce 2-bit mixed precision quantized Transformer language models. Experiments conducted on the Switchboard data suggest that the proposed neural architectural compression and mixed precision quantization techniques consistently outperform the uniform precision quantised baseline systems of comparable bit-widths in terms of word error rate (WER). An overall “lossless” compression ratio of 13.6 was obtained over the baseline full precision system including both the TDNN and Transformer components while incurring no statistically significant WER increase.
Communication by Means of Thermal Noise: Toward Networks With Extremely Low Power Consumption

In this paper, the paradigm of thermal noise communication (TherCom) is put forward for future wired/wireless networks with extremely low power consumption. Taking backscatter communication (BackCom) and reconfigurable intelligent surface (RIS)-based radio frequency chain-free transmitters one step further, a thermal noise-driven transmitter might enable zero-signal-power transmission by simply indexing resistors or other noise sources according to information bits. This preliminary paper aims to shed light on the theoretical foundations, transceiver designs, and error performance derivations as well as optimizations of two emerging TherCom solutions: Kirchhoff-law-Johnson-noise (KLJN) secure bit exchange and wireless thermal noise modulation (TherMod) schemes. Our theoretical and computer simulation findings reveal that noise variance detection, supported by sample variance estimation with carefully optimized decision thresholds, is a reliable way of extracting the embedded information from noise modulated signals, even with limited number of noise samples.
Feasibility Analysis and Proof of Model Predictive Control

Sequential and terminal constraint feasibility of the model predictive control (MPC) play important roles in ensuring MPC control continuity. This study thus investigates these two properties theoretically using an MPC model for vehicle platooning and eco-driving strategy at signalized intersections. This study is served as a supplement material for our paper coming soon.
Adversarial Multi-Task Learning for Disentangling Timbre and Pitch in Singing Voice Synthesis

Recently, deep learning-based generative models have been in-troduced to generate singing voices. One approach is to predict the parametric vocoder features consisting of explicit speech parameters. This approach has the advantage that the mean-ing of each feature is explicitly distinguished. Another approach is to predict mel-spectrograms for a neural vocoder. However, parametric vocoders have limitations of voice quality and the mel-spectrogram features are difficult to model because the timbre and pitch information are entangled. In this study, we propose a singing voice synthesis model with multi-task learning to use both approaches – acoustic features for a parametric vocoder and mel-spectrograms for a neural vocoder. By using the parametric vocoder features as auxiliary features, the proposed model can efficiently disentangle and control the timbre and pitch components of the mel-spectrogram. Moreover, a generative adversarial network framework is applied to improve the quality of singing voices in a multi-singer model. Experimental results demonstrate that our proposed model can generate more natural singing voices than the single-task models, while per-forming better than the conventional parametric vocoder-based model.
Financing Transportation in California: Strategies for Change (Final Draft) - eScholarship

Quality of life in California depends in many ways on the freedom of people and goods to move safely from place to place when they want to do so, in a timely manner, at reasonable cost, and with reasonable choices among modes and routes of travel. However, our desire for mobility must be tempered by due concern for the natural and built environment, and like all aspects of public policy, the provision of mobility must be framed by realistic consideration of available financial resources. This report examines the system by which California raises money for transportation and assesses its adequacy and appropriateness for current and future needs.
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer

In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are expressed linearly, and the agent has previously learned a set of policies for different tasks, successor features (SFs) can be exploited to combine such policies and identify reasonable solutions for new problems. However, the identiﬁed solutions are not guaranteed to be optimal. We introduce a novel algorithm that addresses this limitation. It allows RL agents to combine existing policies and directly identify optimal policies for arbitrary new problems, without requiring any further interac-tions with the environment. We ﬁrst show (under mild assumptions) that the transfer learning problem tackled by SFs is equivalent to the problem of learning to optimize multiple objectives in RL. We then introduce an SF-based extension of the Optimistic Linear Support algorithm to learn a set of policies whose SFs form a convex coverage set. We prove that policies in this set can be combined via generalized policy improvement to construct optimal behaviors for any new linearly-expressible tasks, without requiring any additional training samples. We empirically show that our method outperforms state-of-the-art competing algorithms both in discrete and continuous domains under value function approximation.
Improved description of e^+e^- \to p\bar{p}{\pi}^0 cross section line shape and more stringent constraints on {\psi}(3770) and {\psi}(4230) \to p\bar{p}{\pi}^0

By using the cross sections of e + e − → p ¯ pπ 0 measured at the center-of-mass energies from 3.65 to 4.60 GeV, we ﬁnd that the line shape well agrees with pure continuum production parametrized by a power law function and the signiﬁcance of both ψ (3770) and ψ (4230) → p ¯ pπ 0 is less than 0 . 4 σ . We set more stringent constraints on the charmless decays of the charmonium state, ψ (3770) , and the charmoniumlike state, ψ (4230) , to p ¯ pπ 0 than in previous measurements. The data are also used to estimate the cross sections of p ¯ p → π 0 ψ (3770) and p ¯ p → π 0 ψ (4230) that are essential for planning the data taking of the PANDA experiment. The charmonium state ψ (3770) , lying about 40 MeV above the open charm threshold, is expected to decay dominantly into the Okubo-Zweig-Iizuka allowed ﬁnal state, D ¯ D , together with a small fraction of hadronic and radiative transitions into ﬁnal states with a lower mass charmonium state. However, the total cross section σ ( e + e − → ψ (3770)) measured by counting the total number of inclusive hadronic events is not saturated by D ¯ D
The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). II. The Spatially Resolved Recent Star Formation History of M33

We measure the spatially resolved recent star formation history (SFH) of M33 using optical images taken with the Hubble Space Telescope as part of the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey. The area covered by the observations used in this analysis covers a de-projected area of ∼38 kpc2 and extends to ∼3.5 and ∼2 kpc from the center of M33 along the major and semimajor axes, respectively. We divide the PHATTER optical survey into 2005 regions that measure 24 arcsec, ∼100 pc, on a side and fit color–magnitude diagrams for each region individually to measure the spatially resolved SFH of M33 within the PHATTER footprint. There are significant fluctuations in the SFH on small spatial scales and also galaxy-wide scales that we measure back to about 630 Myr ago. We observe a more flocculent spiral structure in stellar populations younger than about 80 Myr, while the structure of the older stellar populations is dominated by two spiral arms. We also observe a bar in the center of M33, which dominates at ages older than about 80 Myr. Finally, we find that the mean star formation rate (SFR) over the last 100 Myr within the PHATTER footprint is 0.32 ± 0.02 M⊙ yr−1. We measure a current SFR (over the last 10 Myr) of 0.20 ± 0.03 M⊙ yr−1. This SFR is slightly higher than previous measurements from broadband estimates, when scaled to account for the fraction of the D25 area covered by the PHATTER survey footprint.
Inferring Tie Strength in Temporal Networks

Inferring tie strengths in social networks is an essential task in social network analysis. Common approaches classify the ties as wea} and strong ties based on the strong triadic closure (STC). The STC states that if for three nodes, $A$, $B$, and $C$, there are strong ties between $A$ and $B$, as well as $A$ and $C$, there has to be a (weak or strong) tie between $B$ and $C$. A variant of the STC called STC+ allows adding a few new weak edges to obtain improved solutions. So far, most works discuss the STC or STC+ in static networks. However, modern large-scale social networks are usually highly dynamic, providing user contacts and communications as streams of edge updates. Temporal networks capture these dynamics. To apply the STC to temporal networks, we first generalize the STC and introduce a weighted version such that empirical a priori knowledge given in the form of edge weights is respected by the STC. Similarly, we introduce a generalized weighted version of the STC+. The weighted STC is hard to compute, and our main contribution is an efficient 2-approximation (resp. 3-approximation) streaming algorithm for the weighted STC (resp. STC+) in temporal networks. As a technical contribution, we introduce a fully dynamic $k$-approximation for the minimum weighted vertex cover problem in hypergraphs with edges of size $k$, which is a crucial component of our streaming algorithms. An empirical evaluation shows that the weighted STC leads to solutions that better capture the a priori knowledge given by the edge weights than the non-weighted STC. Moreover, we show that our streaming algorithm efficiently approximates the weighted STC in real-world large-scale social networks.
AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models

The objective of pre-trained language models is to learn contextual representations of textual data. Pre-trained language models have become mainstream in natural language processing and code modeling. Using probes, a technique to study the linguistic properties of hidden vector spaces, previous works have shown that these pre-trained language models encode simple linguistic properties in their hidden representations. However, none of the previous work assessed whether these models encode the whole grammatical structure of a programming language. In this paper, we prove the existence of a syntactic subspace, lying in the hidden representations of pre-trained language models, which contain the syntactic information of the programming language. We show that this subspace can be extracted from the models’ representations and define a novel probing method, the AST-Probe, that enables recovering the whole abstract syntax tree (AST) of an input code snippet. In our experimentations, we show that this syntactic subspace exists in five state-of-the-art pre-trained language models. In addition, we highlight that the middle layers of the models are the ones that encode most of the AST information. Finally, we estimate the optimal size of this syntactic subspace and show that its dimension is substantially lower than those of the models’ representation spaces. This suggests that pre-trained language models use a small part of their representation spaces to encode syntactic information of the programming languages.
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model

We study the two-stage vertex-weighted online bipartite matching problem of Feng, Niazadeh, and Saberi (SODA 2021) in a setting where the algorithm has access to a suggested matching that is recommended in the first stage. We evaluate an algorithm by its robustness $R$, which is its performance relative to that of the optimal offline matching, and its consistency $C$, which is its performance when the advice or the prediction given is correct. We characterize for this problem the Pareto-efficient frontier between robustness and consistency, which is rare in the literature on advice-augmented algorithms, yet necessary for quantifying such an algorithm to be optimal. Specifically, we propose an algorithm that is $R$-robust and $C$-consistent for any $(R,C)$ with $0 \leq R \leq \frac{3}{4}$ and $\sqrt{1-R} + \sqrt{1-C} = 1$, and prove that no other algorithm can achieve a better tradeoff.
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation

We present PriFit, a semi‐supervised approach for label‐efficient learning of 3D point cloud segmentation networks. PriFit combines geometric primitive fitting with point‐based representation learning. Its key idea is to learn point representations whose clustering reveals shape regions that can be approximated well by basic geometric primitives, such as cuboids and ellipsoids. The learned point representations can then be re‐used in existing network architectures for 3D point cloud segmentation, and improves their performance in the few‐shot setting. According to our experiments on the widely used ShapeNet and PartNet benchmarks, PriFit outperforms several state‐of‐the‐art methods in this setting, suggesting that decomposability into primitives is a useful prior for learning representations predictive of semantic parts. We present a number of ablative experiments varying the choice of geometric primitives and downstream tasks to demonstrate the effectiveness of the method.
On Random Graph Properties

We consider 15 properties of labeled random graphs that are of interest in the graph-theoretical and the graph mining literature, such as clustering coeﬃcients, centrality measures, spectral radius, degree assortativity, treedepth, treewidth, etc. We the analyze relationships and correlations between these properties. Whereas for graphs on small number of vertices we can exactly compute the average values and range for each property of interest, this becomes infeasible for larger graphs. We show that graphs generated by the Erd˝os-R´enyi graph generator with p = 1 / 2 model well the underlying space of all labeled graphs with ﬁxed number of vertices. The later observation allows us to analyze properties and correlations between these properties for larger graphs. We then use linear and non-linear models to predict a given property based on the others and for each property, we ﬁnd the most predictive subset. We experimentally show that pairs and triples of properties have high predictive power, making it possible to estimate computationally expensive to compute properties with ones for which there are eﬃcient algorithms.