Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting Self-training for Few-shot Learning of Language Model

As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM. Given two views of a text sample via weak and strong augmentation techniques, SFLM generates a pseudo label on the weakly augmented version. Then, the model predicts the same pseudo label when fine-tuned with the strongly augmented version. This simple approach is shown to outperform other state-of-the-art supervised and semi-supervised counterparts on six sentence classification and six sentence-pair classification benchmarking tasks. In addition, SFLM only relies on a few in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate the robustness of our proposed approach under various settings, including augmentation techniques, model scale, and few-shot knowledge transfer across tasks.
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing

Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to express the semantic structure of a text. Such representations generate expressive and adequate graph-based target structures. In this paper, we focus primarily on UCCA graph-based semantic representation. The paper not only presents the existing approaches proposed for UCCA representation, but also proposes a novel self-attentive neural parsing model for the UCCA representation. We present the results for both single-lingual and cross-lingual tasks using zero-shot and few-shot learning for low-resource languages.
Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients

Purpose This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients. Methods We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95<95%) and adaptation (V95<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE10) were set for all Dmax and Dmean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI95). Results RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid Dmean at EOT. Twelve PTVs had V95<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx = 6 (range, 1–16). Seventeen parotids were flagged for exceeding Dmean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, −2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction. Conclusion Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.
JuriBERT: A Masked-Language Model Adaptation for French Legal Text

Language models have proven to be very useful when adapted to specific domains. Nonetheless, little research has been done on the adaptation of domain-specific BERT models in the French language. In this paper, we focus on creating a language model adapted to French legal text with the goal of helping law professionals. We conclude that some specific tasks do not benefit from generic language models pre-trained on large amounts of data. We explore the use of smaller architectures in domain-specific sub-languages and their benefits for French legal text. We prove that domain-specific pre-trained models can perform better than their equivalent generalised ones in the legal domain. Finally, we release JuriBERT, a new set of BERT models adapted to the French legal domain.
Generating and Managing Strong Passwords using Hotel Mnemonic

Weak passwords and availability of supercomputers to password crackers make the financial institutions and businesses at stake. This calls for use of strong passwords and multi factor authentication for secure transactions. Remembering a long and complex password by humans is a daunting task and mnemonic has helped to mitigate this situation to an extent. This paper discusses creating and using long random password and storing them securely using a hybrid strategy of hash-encryption method. The hash function uses a mnemonic password based on the hotel names and other characteristics like room number, floor number and breakfast meal preferences to generate the encryption key. The random strong password can be then encrypted using the key and stored safely. A computer program named Hector is developed which demonstrates these steps and can be used to generate and store the passwords.
A physical model for the broadband energy spectrum of X-ray illuminated accretion discs: fitting the spectral energy distribution of NGC 5548

We develop a new physical model for the broadband spectral energy distribution (SED) of X-ray illuminated accretion discs, that takes into account the mutual interaction of the accretion disc and the X-ray corona, including all relativistic effects. We assume a Keplerian, optically thick and geometrically thin accretion disc and an X-ray source in the lamp-post geometry that emits an isotropic power-law spectrum with a high-energy cut-off. We assume that all the energy that would be released by thermal radiation in the standard disc model in its innermost part, is transported to the corona, effectively cooling the disc in this region. We include the disc heating due to thermalisation of the absorbed part of the disc illumination by X-ray corona. The X-ray reflection from the disc is also included. We compute the X-ray luminosity and the low-energy X-ray cut-off through an iterative process, taking full account of the interplay between the X-ray illumination of the disc and the resulting accretion disc spectrum which enters the corona so that the energy balance is preserved. The corona radius is also computed from the conservation of the photon's number during Comptonization. We discuss the model SEDs and their dependence on system parameters. The disc-corona interaction has profound effects - it constrains the X-ray luminosity and changes the shape and normalisation of the UV/optical blue bump. We use the new code to fit the broad-band SED of a typical Seyfert 1 galaxy, NGC 5548. We infer a high black-hole spin, an intermediate system inclination, and an accretion rate below 10% of Eddington. The X-ray luminosity in this source could be supported by 45-70% of the accretion energy dissipated in the disc. The new model, named KYNSED, is publicly available to be used for fitting AGN SEDs inside the XSPEC spectral analysis tool.
How To Not Drive: Learning Driving Constraints from Demonstration

We propose a new scheme to learn motion planning constraints from human driving trajectories. Behavioral and motion planning are the key components in an autonomous driving system. The behavioral planning is responsible for high-level decision making required to follow traffic rules and interact with other road participants. The motion planner role is to generate feasible, safe trajectories for a self-driving vehicle to follow. The trajectories are generated through an optimization scheme to optimize a cost function based on metrics related to smoothness, movability, and comfort, and subject to a set of constraints derived from the planned behavior, safety considerations, and feasibility. A common practice is to manually design the cost function and constraints. Recent work has investigated learning the cost function from human driving demonstrations. While effective, the practical application of such approaches is still questionable in autonomous driving. In contrast, this paper focuses on learning driving constraints, which can be used as an add-on module to existing autonomous driving solutions. To learn the constraint, the planning problem is formulated as a constrained Markov Decision Process, whose elements are assumed to be known except the constraints. The constraints are then learned by learning the distribution of expert trajectories and estimating the probability of optimal trajectories belonging to the learned distribution. The proposed scheme is evaluated using NGSIM dataset, yielding less than 1% collision rate and out of road maneuvers when the learned constraints is used in an optimization-based motion planner.
Numerical Computation of Neumann Controls for the Heat Equation on a Finite Interval

This article presents a new numerical method, which approximates Neumann type null controls for the heat equation and is based on the Fokas method. This is a direct method for solving problems originating from the control theory, which allows the realization of an efficient numerical algorithm that requires small computational effort for determining the null control with exponentially small error. Furthermore, the unified character of the Fokas method makes the extension of the numerical algorithm to a wide range of other linear partial differential equations and different type of boundary conditions straightforward.
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

Laws and their interpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
FICGAN: Facial Identity Controllable GAN for De-identification

In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility. We tackle the less-explored yet desired functionality in face de-identification based on the two factors. First, we focus on the challenging issue to obtain a high level of privacy protection in the de-identification task while uncompromising the image quality. Second, we analyze the facial attributes related to identity and non-identity and explore the trade-off between the degree of face de-identification and preservation of the source attributes for enhanced data utility. Based on the analysis, we develop Facial Identity Controllable GAN (FICGAN), an autoencoder-based conditional generative model that learns to disentangle the identity attributes from non-identity attributes on a face image. By applying the manifold k-same algorithm to satisfy k-anonymity for strengthened security, our method achieves enhanced privacy protection in de-identified face images. Numerous experiments demonstrate that our model outperforms others in various scenarios of face de-identification.
Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp

Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational resources to perform the required control tasks. Neuromorphic computing stands for a promising research direction for addressing such problem. By mimicking the biological process for transferring information between neurons using spikes or impulses, spiking neural networks (SNNs) allow for low power consumption and asynchronous event-driven processing. In this paper, we propose an evolved altitude controller based on an SNN for a robotic airship which relies solely on the sensory feedback provided by an airborne radar. Starting from the design of a lightweight, low-cost, open-source airship, we also present an SNN-based controller architecture, an evolutionary framework for training the network in a simulated environment, and a control strategy for ameliorating the gap with reality. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network and a linear controller. The results show an accurate tracking of the altitude command with an efficient control effort.
BLEnD: Improving NDN Performance Over Wireless Links Using Interest Bundling

Named Data Networking (NDN) employs small-sized Interest packets to retrieve large-sized Data packets. Given the half-duplex nature of wireless links, Interest packets frequently contend for the channel with Data packets, leading to throughput degradation. In this work, we present a novel idea called BLEnD, an Interest-bundling technique that encodes multiple Interests into one at the sender and decodes at the receiver. The major design challenges are to reduce the number of Interest transmissions without impacting the one-Interest one-Data principle embedded everywhere in NDN architecture and implementation, and support flow/congestion control mechanisms that usually use Interest packets as signals. BLEnD achieves these by bundling/unbundling Interests at the link adaptation layer, keeping all NDN components unaware and unaffected. Over a one-hop Wi-Fi link, BLEnD improves application throughput by 30%. It may also be used over multiple hops and be improved in a number of ways.
Control Barrier Function Meets Interval Analysis: Safety-Critical Control with Measurement and Actuation Uncertainties

This paper presents a framework for designing provably safe feedback controllers for sampled-data control affine systems with measurement and actuation uncertainties. Based on the interval Taylor model of nonlinear functions, a sampled-data control barrier function (CBF) condition is proposed which ensures the forward invariance of a safe set for sampled-data systems. ℝeachable set overapproximation and Lasserre’s hierarchy of polynomial optimization are used for finding a margin term in the sampled-data CBF condition. Sufficient conditions for a safe controller in the presence of measurement and actuation uncertainties are proposed. The effectiveness of the proposed method is illustrated by a numerical example and an experimental example that implements the proposed controller on the Crazyflie quadcopter in real-time.
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized model learning, e.g., federated learning, how to retain regret minimization while reducing communication cost becomes an open challenge. In this paper, we study linear contextual bandit in a federated learning setting. We propose a general framework with asynchronous model update and communication for a collection of homogeneous clients and heterogeneous clients, respectively. Rigorous theoretical analysis is provided about the regret and communication cost under this distributed learning framework; and extensive empirical evaluations demonstrate the effectiveness of our solution.
ML4C: Seeing Causality Through Latent Vicinity

Supervised Causal Learning (SCL) aims to learn causal relations from observational data by accessing previously seen datasets associated with ground truth causal relations. This paper presents a first attempt at addressing a fundamental question: What are the benefits from supervision and how does it benefit? Starting from seeing that SCL is not better than random guessing if the learning target is non-identifiable a priori, we propose a two-phase paradigm for SCL by explicitly considering structure identifiability. Following this paradigm, we tackle the problem of SCL on discrete data and propose ML4C. The core of ML4C is a binary classifier with a novel learning target: it classifies whether an Unshielded Triple (UT) is a v-structure or not. Specifically, starting from an input dataset with the corresponding skeleton provided, ML4C orients each UT once it is classified as a v-structure. These v-structures are together used to construct the final output. To address the fundamental question of SCL, we propose a principled method for ML4C featurization: we exploit the vicinity of a given UT (i.e., the neighbors of UT in skeleton), and derive features by considering the conditional dependencies and structural entanglement within the vicinity. We further prove that ML4C is asymptotically correct. Last but foremost, thorough experiments conducted on benchmark datasets demonstrate that ML4C remarkably outperforms other state-of-the-art algorithms in terms of accuracy, reliability, robustness and tolerance. In summary, ML4C shows promising results on validating the effectiveness of supervision for causal learning. Our codes are publicly available at https://github.com/microsoft/ML4C.
Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems

This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multicarrier transmission. The conventional precoding design involves a two-step process of first estimating the high-dimensional channel, then designing the precoders based on such estimate. This two-step process is, however, not necessarily optimal. This paper shows that by using a learning approach to design the analog sensing and the hybrid downlink precoders directly from the received pilots without the intermediate high-dimensional channel estimation, the overall system performance can be significantly improved. Training a neural network to design the analog and digital precoders simultaneously is, however, difficult. Further, such an approach is not generalizable to systems with different number of users. In this paper, we develop a simplified and generalizable approach that learns the uplink sensing matrix and downlink analog precoder using a deep neural network that decomposes on a per-user basis, then designs the digital precoder based on the estimated low-dimensional equivalent channel. Numerical comparisons show that the proposed methodology results in significantly less training overhead and leads to an architecture that generalizes to various system settings.
Enabling End-to-End Simulation for Host Networking Evaluation using SimBricks

Full system "end-to-end" measurements in physical testbeds are the gold standard for evaluation of network systems but are fraught with challenges. Adequate testbeds are often not available, as projects target next generation devices, propose new hardware, or require larger scale. Further, evaluations in testbeds limit what we can observe without affecting sys-tem behavior, are frequently hard to reproduce, and are only available to groups with sufﬁcient funding. Yet, we lack an accepted alternative, leaving us with ad-hoc non-end-to-end evaluations that do not form a solid basis for future work. We argue that full system simulations enable comparable end-to-end evaluation and are the next best alternative when a physical testbed is not available. To this end, we present SimBricks, a modular full system simulation framework for network systems. SimBricks combines multiple existing simulators for individual components, including processor and memory, NIC, and network, into full virtual testbeds running unmodiﬁed software. The architecture combines well-deﬁned component interfaces for extensibility with new simulators, efﬁcient communication channels for local and distributed simulators, and a novel efﬁcient synchronization protocol for accurate timing across different simulators. We demonstrate that SimBricks simulations reproduce key ﬁndings from prior work in congestion control, NIC architecture, and in-network computing, and show scalability to 1000 simulated hosts.
One-Bit Matrix Completion with Differential Privacy

As a prevailing collaborative ﬁltering method for recommendation systems, one-bit matrix completion requires data collected by users to provide personalized service. Due to insidious attacks and unexpected inference, the release of users’ data often raises serious privacy concerns. To address this issue, diﬀerential privacy (DP) has been widely used in standard matrix completion models. To date, however, little has been known about how to apply DP to achieve privacy protection in one-bit matrix completion. In this paper, we propose a uniﬁed framework for ensuring a strong privacy guarantee of one-bit matrix completion with DP. In our framework, we develop four diﬀerent private perturbation mechanisms corresponding to diﬀerent stages of one-bit matrix completion. For each mechanism, we design a privacy-preserving algorithm and provide a theoretical recovery error bound under the proper conditions. Numerical experiments on synthetic and real-world datasets demonstrate the eﬀectiveness of our proposal. Compared to the one-bit matrix completion without privacy protection, our proposed mechanisms can maintain high-level privacy protection with marginal loss of completion accuracy.