Miracle-dz/UniFAR-FTUs huggingface dataset:
Projective Transformation Rectification for Camera-captured Chest X-ray Photograph Interpretation with Synthetic Data

Automatic interpretation on smartphone-captured chest X-ray (CXR) photographs is challenging due to the geometric distortion (projective transformation) caused by the non-ideal camera position. In this paper, we proposed an innovative deep learning-based P rojective T ransformation R ectification N etwork (PTRN) to automatically rectify such distortions by predicting the projective transformation matrix. PTRN is trained on synthetic data to avoid the expensive collection of natural data. Therefore, we proposed an innovative synthetic data framework that accounts for the visual attributes of natural photographs including screen, background, illuminations, and visual artifacts, and generate synthetic CXR photographs and projective transformation matrices as the ground-truth labels for training PTRN. Finally, smartphone-captured CXR photographs are automatically rectified by trained PTRN and interpreted by a classifier trained on high-quality digital CXRs to produce final interpretation results. In the CheXphoto CXR photograph interpretation competition released by the Stanford
Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional, contact-rich behaviors often require esoteric teleoperation tools. In this work, we present Holo − Dex, a framework for dexter-ous manipulation that places a teacher in an immersive mixed reality through commodity VR headsets. The high-fidelity hand pose estimator onboard the headset is used to teleoperate the robot and collect demonstrations for a variety of general-purpose dexterous tasks. Given these demonstrations, we use powerful feature learning combined with non-parametric imi-tation to train dexterous skills. Our experiments on six common dexterous tasks, including in-hand rotation, spinning, and bottle opening, indicate that HOLO-DEX can both collect high-quality demonstration data and train skills in a matter of hours. Finally, we find that our trained skills can exhibit generalization on objects not seen in training. Videos of HOLO − DEX are available on {https://holo-dex.github. io/. }
Towards Optimal Primary- and Secondary-control Design for Networks with Generators and Inverters

For power grids predominantly featuring large synchronous generators (SGs), there exists a significant body of work bridging optimization and control tasks. A generic workflow in such efforts entails: characterizing the steady state of control algorithms and SG dynamics; assessing the optimality of the resulting operating point with respect to an optimal dispatch task; and prescribing control parameters to ensure that (under reasonable ambient perturbations) the considered control nudges the system steady state to optimality. Well studied instances of the aforementioned approach include designing: i) automatic generation control (AGC) participation factors to ensure economic optimality, and ii) governor frequency-droop slopes to ensure power sharing. Recognizing that future power grids will feature a diverse mix of SGs and inverter-based resources (IBRs) with varying control structures, this work examines the different steps of the optimization-control workflow for this context. Considering a representative model of active power-frequency dynamics of IBRs and SGs, a characterization of steady state is put forth (with and without secondary frequency control). Conditions on active-power droop slopes and AGC participation factors are then derived to ascertain desired power sharing and ensure economically optimal operation under varying power demands.
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors

The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs. Re-cent works have achieved success on this task by utilizing facial priors such as facial landmarks. Most existing methods pay more attention to global shape and structure information, but less to local texture information, which makes them cannot recover local details well. In this pa-per, we propose a novel recurrent convolutional network based framework for face super-resolution, which progres-sively introduces both global shape and local texture infor-mation. We take full advantage of the intermediate outputs of the recurrent network, and landmarks information and facial action units (AUs) information are extracted in the output of the first and second steps respectively, rather than low-resolution input. Moreover, we introduced AU clas-sification results as a novel quantitative metric for facial details restoration. Extensive experiments show that our proposed method significantly outperforms state-of-the-art FSR methods in terms of image quality and facial details restoration.
Simple single-field inflation models with arbitrarily small tensor/scalar ratio

We construct a family of simple single-field inflation models consistent with Planck / BICEP Keck bounds which have a parametrically small tensor amplitude and no running of the scalar spectral index. The construction consists of a constant-roll hilltop inflaton potential with the end of inflation left as a free parameter induced by higher-order operators which become dominant late in inflation. This construction directly demonstrates that there is no lower bound on the tensor/scalar ratio for simple single-field inflation models.
Clustering Embedding Tables, Without First Learning Them

To work with categorical features, machine learning systems employ embedding tables. These tables can become exceedingly large in modern recommendation systems, necessitating the development of new methods for fitting them in memory, even during training. Some of the most successful methods for table compression are Productand Residual Vector Quantization (Gray & Neuhoff, 1998). These methods replace table rows with references to k-means clustered “codewords. ” Unfortunately, this means they must first know the table before compressing it, so they can only save memory during inference, not training. Recent work has used hashing-based approaches to minimize memory usage during training, but the compression obtained is inferior to that obtained by “post-training” quantization. We show that the best of both worlds may be obtained by combining techniques based on hashing and clustering. By first training a hashing-based “sketch”, then clustering it, and then training the clustered quantization, our method achieves compression ratios close to those of post-training quantization with the training time memory reductions of hashing-based methods. We show experimentally that our method provides better compression and/or accuracy that previous methods, and we prove that our method always converges to the optimal embedding table for least-squares training.
Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing

The application of machine learning (ML) techniques in wireless communication domain has seen a tremendous growth over the years especially in the wireless sensing domain. However, the questions surrounding the ML model's inference reliability, and uncertainty associated with its predictions are never answered or communicated properly. This itself raises a lot of questions on the transparency of these ML systems. Developing ML systems with probabilistic modeling can solve this problem easily, where one can quantify uncertainty whether it is arising from the data (irreducible error or aleotoric uncertainty) or from the model itself (reducible or epistemic uncertainty). This paper describes the idea behind these types of uncertainty quantification in detail and uses a real example of WiFi channel state information (CSI) based sensing for motion/no-motion cases to demonstrate the uncertainty modeling. This work will serve as a template to model uncertainty in predictions not only for WiFi sensing but for most wireless sensing applications ranging from WiFi to millimeter wave radar based sensing that utilizes AI/ML models.
Streaming Punctuation for Long-form Dictation with Transformers

While speech recognition Word Error Rate (WER) has reached human parity for English, longform dictation scenarios still suffer from segmentation and punctuation problems resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEU-score improvement of 0.66 for the downstream task of Machine Translation (MT).
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork

Deep neural networks (DNNs) are vulnerable to backdoor attacks. Previous works have shown it extremely challenging to unlearn the undesired backdoor behavior from the network, since the entire network can be affected by the backdoor samples. In this paper, we propose a brand-new backdoor defense strategy, which makes it much easier to remove the harmful influence of backdoor samples from the model. Our defense strategy, Trap and Replace, consists of two stages. In the first stage, we bait and trap the backdoors in a small and easy-to-replace subnetwork. Specifically, we add an auxiliary image reconstruction head on top of the stem network shared with a light-weighted classification head. The intuition is that the auxiliary image reconstruction task encourages the stem network to keep sufficient low-level visual features that are hard to learn but semantically correct, instead of overfitting to the easy-to-learn but semantically incorrect backdoor correlations. As a result, when trained on backdoored datasets, the backdoors are easily baited towards the unprotected classification head, since it is much more vulnerable than the shared stem, leaving the stem network hardly poisoned. In the second stage, we replace the poisoned light-weighted classification head with an untainted one, by re-training it from scratch only on a small holdout dataset with clean samples, while fixing the stem network. As a result, both the stem and the classification head in the final network are hardly affected by backdoor training samples. We evaluate our method against ten different backdoor attacks. Our method outperforms previous state-of-the-art methods by up to 20.57%, 9.80%, and 13.72% attack success rate and on-average 3.14%, 1.80%, and 1.21% clean classification accuracy on CIFAR10, GTSRB, and ImageNet-12, respectively. Code is available at https://github.com/VITA-Group/Trap-and-Replace-Backdoor-Defense.
Non-Axiomatic Term Logic: A Computational Theory of Cognitive Symbolic Reasoning

This paper 1 presents Non-Axiomatic Term Logic (NATL) as a theoretical computational framework of humanlike symbolic reasoning in artiﬁcial intelligence. NATL unites a discrete syntactic system inspired from Aristotle’s term logic and a continuous semantic system based on the modern idea of distributed representations, or embeddings. This paper positions the proposed approach in the phylogeny and the literature of logic, and explains the framework. As it is yet no more than a theory and it requires much further elaboration to implement it, no quantitative evaluation is presented. Instead, qualitative analyses of arguments using NATL, some applications to possible cognitive science/robotics-related research, and remaining issues towards a machinery implementation are discussed.
Effects of Safety State Augmentation on Safe Exploration

Safe exploration is a challenging and important problem in model-free reinforcement learning (RL). Often the safety cost is sparse and unknown, which unavoidably leads to constraint violations -- a phenomenon ideally to be avoided in safety-critical applications. We tackle this problem by augmenting the state-space with a safety state, which is nonnegative if and only if the constraint is satisfied. The value of this state also serves as a distance toward constraint violation, while its initial value indicates the available safety budget. This idea allows us to derive policies for scheduling the safety budget during training. We call our approach Simmer (Safe policy IMproveMEnt for RL) to reflect the careful nature of these schedules. We apply this idea to two safe RL problems: RL with constraints imposed on an average cost, and RL with constraints imposed on a cost with probability one. Our experiments suggest that"simmering, a safe algorithm can improve safety during training for both settings. We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
Generalised correlated batched bandits via the ARC algorithm with application to dynamic pricing

The Asymptotic Randomised Control (ARC) algorithm provides a rigorous approximation to the optimal strategy for a wide class of Bayesian bandits, while retaining low computational complexity. In particular, the ARC approach provides nearly optimal choices even when the payoﬀs are correlated or more than the reward is observed. The algorithm is guaranteed to asymptotically optimise the expected discounted payoﬀ, with error depending on the initial uncertainty of the bandit. In this paper, we extend the ARC framework to consider a batched bandit problem where observations arrive from a generalised linear model. In particular, we develop a large sample approximation to allow correlated and generally distributed observation. We apply this to a classic dynamic pricing problem based on a Bayesian hierarchical model and demonstrate that the ARC algorithm outperforms alternative approaches.
Learning by Asking Questions for Knowledge-based Novel Object Recognition

In real-world object recognition, there are numerous object classes to be recognized. Traditional image recognition methods based on supervised learning can only recognize object classes present in the training data, and have limited applicability in the real world. In contrast, humans can recognize novel objects by questioning and acquiring knowledge about them. Inspired by this, we propose a framework for acquiring external knowledge by generating questions that enable the model to instantly recognize novel objects. Our framework comprises three components: the object classifier (OC), which performs knowledge-based object recognition, the question generator (QG), which generates knowledge-aware questions to acquire novel knowledge, and the policy decision (PD) Model, which determines the “policy” of questions to be asked. The PD model utilizes two strategies, namely “confirmation” and “exploration”—the former confirms candidate knowledge while the latter explores completely new knowledge. Our experiments demonstrate that the proposed pipeline effectively acquires knowledge about novel objects compared to several baselines, and realizes novel object recognition utilizing the obtained knowledge. We also performed a real-world evaluation in which humans responded to the generated questions, and the model used the acquired knowledge to retrain the OC, which is a fundamental step toward a real-world human-in-the-loop learning-by-asking framework. We plan to release the dataset immediately upon acceptance of our work.
An Improved Method to Measure the Cosmic Curvature

In this paper, we propose an improved model-independent method to constrain the cosmic curvature by combining the most recent Hubble parameter H(z) and supernovae Ia (SNe Ia) data. Based on the H(z) data, we first use the model-independent smoothing technique, Gaussian processes, to construct a distance modulus μH(z), which is susceptible to the cosmic curvature parameter Ωk. In contrary to previous studies, the light-curve-fitting parameters, which account for the distance estimation of SN (μSN(z)), are set free to investigate whether Ωk has a dependence on them. By comparing μH(z) to μSN(z), we put limits on Ωk. Our results confirm that Ωk is independent of the SN light-curve parameters. Moreover, we show that the measured Ωk is in good agreement with zero cosmic curvature, implying that there is no significant deviation from a flat universe at the current observational data level. We also test the influence of different H(z) samples and different Hubble constant H0 values, finding that different H(z) samples do not have a significant impact on the constraints. However, different H0 priors can affect the constraints of Ωk to some degree. The prior of H0 = 73.24 ± 1.74 km s−1 Mpc−1 gives a value of Ωk, a little bit above the 1σ confidence level away from 0, but H0 = 69.6 ± 0.7 km s−1 Mpc−1 gives it below 1σ.
Approximate methods for the generation of dark matter halo catalogs in the age of precision cosmology

Precision cosmology has recently triggered new attention on the topic of approximate methods for the clustering of matter on large scales, whose foundations date back to the period from the late 1960s to early 1990s. Indeed, although the prospect of reaching sub-percent accuracy in the measurement of clustering poses a challenge even to full N-body simulations, an accurate estimation of the covariance matrix of clustering statistics, not to mention the sampling of parameter space, requires usage of a large number (hundreds in the most favourable cases) of simulated (mock) galaxy catalogs. Combination of few N-body simulations with a large number of realizations performed with approximate methods gives the most promising approach to solve these problems with a reasonable amount of resources. In this paper I review this topic, starting from the foundations of the methods, then going through the pioneering efforts of the 1990s, and finally presenting the latest extensions and a few codes that are now being used in present-generation surveys and thoroughly tested to assess their performance in the context of future surveys.
Engulfing a radio pulsar: the case of PSR J1023+0038

The binary millisecond radio pulsar PSR J1023+0038 has been recently the subject of multi-wavelength monitoring campaigns which revealed that an accretion disc has formed around the neutron star (since 2013 June). We present here the results of Xray and UV observations carried out by the Swift satellite between 2013 October and 2014 May, and of optical and NIR observations performed with the REM telescope, the Liverpool Telescope, the 2.1-m telescope at the San Pedro M artir Observatory and the 1.52-m telescope at the Loiano observing station. The X-ray spectrum is well described by an absorbed power law, which is softer than the previous quiescent epoch (up to 2013 June). The strong correlation between the X-ray and the UV emissions indicates that the same mechanism should be responsible for part of the emission in these bands. Optical and infrared photometric observations show that the companion star is strongly irradiated. Double-peaked emission lines in the optical spectra provide compelling evidence for the presence of an outer accretion disc too. The spectral energy distribution from IR to X-rays is well modelled if the contributions from the companion, the disc and the intra-binary shock emission are all considered. Our extensive data set can be interpreted in terms of an engulfed radio pulsar: the radio pulsar is still active, but undetectable in the radio band due to a large amount of ionized material surrounding the compact object. X-rays and gamma-rays are produced in an intra-binary shock front between the relativistic pulsar wind and matter from the companion and an outer accretion disc. The intense spin-down power irradiates the disc and the companion star, accounting for the UV and optical emissions.
How to Reveal Students’ Conceptions of Programming and Designing Digital Games

Digital games are an important leisure activity for teenagers. In Germany, 72% of 12- to 19-year-olds play every day or several times a week in the year 2021. This poster abstract presents a research design to survey students’ conceptions of programming and designing digital games and their functionality. The research results will advise computing education in programming digital games. In the pilot study with seven secondary school students we used the methods of brainstorming, sorting, structural mapping technique and miracle question to figure out which methods are suitable for our research purpose. First results show that some relevant computing-related phenomena in the context of game design can be identified. For instance, we found the following categories: bugs and errors, server functionality, object behavior and game execution.
CorefPM: Coreference Resolution as Linguistic Prompt Prediction and Machine Reading Comprehension

Coreference resolution is a key challenge in natural language processing and is considered as a good test of machine intelligence. Recent coreference resolution prefers to supervised methods due to its superior performance. But supervised methods suffer from data scarcity and limited generalizability. In this paper, we present CorefPM, an unsupervised approach for the coreference resolution task. We formulate the task as following steps: (1) We generate the candidate mention set by linguistic knowledge and score them by prompt prediction method. (2) We obtain another mention with its confidence score by machine reading comprehension(MRC) model. (3) We output final resolution result by comparing candidate mention and MRC mention. Experiments demonstrate that our approach significantly outperforms the baseline of few-shot and zero-shot in ZeroCLUE, although performs worse on CLUEWSC 2020 dataset than supervised learning baselines. Our experiment results suggested that linguistic knowledge is valuable and can improve the result of coreference resolution task.
Computational methods for Cahn-Hilliard variational inequalities

We consider the non-standard fourth order parabolic Cahn-Hilliard variational inequality with constant as well as non-constant diffusional mobility. We propose a primal-dual active set method as solution technique for the discrete variational inequality given by a (semi-)implicit Euler discretization in time and linear finite elements in space. We show local convergence of the method by reinterpretation as a semi-smooth Newton method. The discrete saddle point system arising in each iteration step is handled by either a Gauss-Seidel type method, the application of a multi-frontal direct solver or a preconditioned conjugate gradient method applied to the Schur complement. Finally we show the efficiency of the method and the preconditioning with several numerical simulations.