Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment

Speech Recognition builds a bridge between the multimedia streaming (audio-only, visual-only or audio-visual) and the corresponding text transcription. However, when training the specific model of new domain, it often gets stuck in the lack of new-domain utterances, especially the labeled visual utterances. To break through this restriction, we attempt to achieve zero-shot modality transfer by maintaining the multi-modality alignment in phoneme space learned with unlabeled multimedia utterances in the high resource domain during the pre-training, and propose a training system Open-modality Speech Recognition (OpenSR) that enables the models trained on a single modality (e.g., audio-only) applicable to more modalities (e.g., visual-only and audio-visual). Furthermore, we employ a cluster-based prompt tuning strategy to handle the domain shift for the scenarios with only common words in the new domain utterances. We demonstrate that OpenSR enables modality transfer from one to any in three different settings (zero-, few- and full-shot), and achieves highly competitive zero-shot performance compared to the existing few-shot and full-shot lip-reading methods. To the best of our knowledge, OpenSR achieves the state-of-the-art performance of word error rate in LRS2 on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively.
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model

Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices. However, the deployment of knowledge distillation systems faces great challenges in real-world industrial-strength applications, which require the use of complex distillation methods on even larger-scale PLMs (over 10B), limited by memory on GPUs and the switching of methods. To overcome these challenges, we propose GKD, a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. With GKD, developers can build larger distillation models on memory-limited GPUs and easily switch and combine different distillation methods within a single framework. Experimental results show that GKD can support the distillation of at least 100B-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs.
ARIST: An Effective API Argument Recommendation Approach

Learning and remembering to use APIs are difficult. Several techniques have been proposed to assist developers in using APIs. Most existing techniques focus on recommending the right API methods to call, but very few techniques focus on recommending API arguments. In this paper, we propose ARIST, a novel automated argument recommendation approach which suggests arguments by predicting developers' expectations when they define and use API methods. To implement this idea in the recommendation process, ARIST combines program analysis (PA), language models (LMs), and several features specialized for the recommendation task which consider the functionality of formal parameters and the positional information of code elements (e.g., variables or method calls) in the given context. In ARIST, the LMs and the recommending features are used to suggest the promising candidates identified by PA. Meanwhile, PA navigates the LMs and the features working on the set of the valid candidates which satisfy syntax, accessibility, and type-compatibility constraints defined by the programming language in use. Our evaluation on a large dataset of real-world projects shows that ARIST improves the state-of-the-art approach by 19% and 18% in top-1 precision and recall for recommending arguments of frequently-used libraries. For general argument recommendation task, i.e., recommending arguments for every method call, ARIST outperforms the baseline approaches by up to 125% top-1 accuracy. Moreover, for newly-encountered projects, ARIST achieves more than 60% top-3 accuracy when evaluating on a larger dataset. For working/maintaining projects, with a personalized LM to capture developers' coding practice, ARIST can productively rank the expected arguments at the top-1 position in 7/10 requests.
Generating Language Corrections for Teaching Physical Control Tasks

AI assistance continues to help advance applications in education, from language learning to intelligent tutoring systems, yet current methods for providing students feedback are still quite limited. Most automatic feedback systems either provide binary correctness feedback, which may not help a student understand how to improve, or require hand-coding feedback templates, which may not generalize to new domains. This can be particularly challenging for physical control tasks, where the rich diversity in student behavior and specialized domains make it challenging to leverage general-purpose assistive tools for providing feedback. We design and build CORGI, a model trained to generate language corrections for physical control tasks, such as learning to ride a bike. CORGI takes in as input a pair of student and expert trajectories, and then generates natural language corrections to help the student improve. We collect and train CORGI over data from three diverse physical control tasks (drawing, steering, and joint movement). Through both automatic and human evaluations, we show that CORGI can (i) generate valid feedback for novel student trajectories, (ii) outperform baselines on domains with novel control dynamics, and (iii) improve student learning in an interactive drawing task.
A data storage model for novel partial differential equation descretizations.

The purpose of this report is to define a standard interface for storing and retrieving novel, non-traditional partial differential equation (PDE) discretizations. Although it focuses specifically on finite elements where state is associated with edges and faces of volumetric elements rather than nodes and the elements themselves (as implemented in ALEGRA), the proposed interface should be general enough to accomodate most discretizations, including hp-adaptive finite elements and even mimetic techniques that define fields over arbitrary polyhedra. This report reviews the representation of edge and face elements as implemented by ALEGRA. It then specifies a convention for storing these elements in EXODUS files by extending the EXODUS API to include edge and face blocks in addition to element blocks. Finally, it presents several techniques for rendering edge and face elements using VTK and ParaView, including the use of VTK’s generic dataset interface for interpolating values interior to edges and faces.
Does preoperative imaging for scalp non‐melanocytic skin cancer accurately predict invasion of the cranial vault? A systematic review and meta‐analysis

This study aimed to determine the diagnostic accuracy of CT and MRI in the preoperative detection of bone involvement for non‐melanoma skin cancers (NMSCs) located on the scalp. This study further aimed to evaluate the predictive value of these imaging modalities in determining the need for craniectomy and to identify gaps in the existing literature.
Detection and Quantification of Klebsiella pneumoniae in Fecal Samples Using Digital Droplet PCR in Comparison with Real-Time PCR

Klebsiella pneumoniae can cause a range of illnesses and has a high colonization rate in the human gut, making it crucial to develop an efficient method for detecting K. pneumoniae in fecal samples. ABSTRACT This study aimed to develop a rapid and sensitive droplet digital PCR (ddPCR) assay for the specific detection of Klebsiella pneumoniae in fecal samples, and to evaluate its application in the clinic by comparison with real-time PCR assay and conventional microbial culture. Specific primers and a probe targeting the K. pneumoniae hemolysin (khe) gene were designed. Thirteen other pathogens were used to evaluate the specificity of the primers and probe. A recombinant plasmid containing the khe gene was constructed and used to assess the sensitivity, repeatability, and reproducibility of the ddPCR. Clinical fecal samples (n = 103) were collected and tested by the ddPCR, real-time PCR, and conventional microbial culture methods. The detection limit of ddPCR for K. pneumoniae was 1.1 copies/μL, about a 10-fold increase in sensitivity compared with real-time PCR. The ddPCR was negative for the 13 pathogens other than K. pneumoniae, confirming its high specificity. Clinical fecal samples gave a higher rate of positivity in the K. pneumoniae ddPCR assay than in analysis by real-time PCR or conventional culture. ddPCR also showed less inhibition by the inhibitor in fecal sample than real-time PCR. Thus, we established a sensitive and effective ddPCR-based assay method for K. pneumoniae. It could be a useful tool for K. pneumoniae detection in feces and may serve as a reliable method to identify causal pathogens and help guide treatment decisions. IMPORTANCE Klebsiella pneumoniae can cause a range of illnesses and has a high colonization rate in the human gut, making it crucial to develop an efficient method for detecting K. pneumoniae in fecal samples.
Combining multiplex gene editing and doubled haploid technology in maize

Summary A major advantage of using CRISPR/Cas9 for gene editing is multiplexing, i.e. the simultaneous targeting of many genes. However, primary transformants typically contain hetero-allelic mutations or are genetic mosaic, while genetically stable lines that are homozygous are desired for functional analysis. Currently, a dedicated and labor-intensive effort is required to obtain such higher-order mutants through several generations of genetic crosses and genotyping. We describe the design and validation of a rapid and efficient strategy to produce lines of genetically identical plants carrying various combinations of homozygous edits, suitable for replicated analysis of phenotypical differences. This approach was achieved by combining highly multiplex gene editing in Zea mays (maize) with in vivo haploid induction, and efficient in vitro generation of doubled haploid plants using embryo rescue doubling. By combining three CRISPR/Cas9 constructs that target in total 36 genes potentially involved in leaf growth, we generated an array of homozygous lines with various combinations of edits within three generations. Several genotypes show a reproducible 10% increase in leaf size, including a septuple mutant combination. We anticipate that our strategy will facilitate the study of gene families via multiplex CRISPR mutagenesis and the identification of allele combinations to improve quantitative crop traits.
Threshold definitions for significant change on the timed 25‐foot walk and nine‐hole peg test in primary progressive multiple sclerosis

The timed 25‐foot walk (T25FW) and nine‐hole peg test (NHPT) exhibit random variability in the short term. A threshold of ≥20% change from baseline has been used to indicate true disability change, but other threshold definitions may be better suited to exclude false and include true change events. The aim of this study was to use patient‐level original trial data to investigate the short‐term variation in T25FW and NHPT, and to compare its extent with disability change at 12‐month follow‐up in people with primary progressive multiple sclerosis (PPMS).
Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space

Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces have been proposed in the literature. These algorithms mainly use either hyperplanes or geodesics for decision boundaries in a large margin classifiers setting leading to a non-convex optimization problem. In this paper, we propose a novel large margin classifier based on horospherical decision boundaries that leads to a geodesically convex optimization problem that can be optimized using any Riemannian gradient descent technique guaranteeing a globally optimal solution. We present several experiments depicting the competitive performance of our classifier in comparison to SOTA.
Reliability Check: An Analysis of GPT-3’s Response to Sensitive Topics and Prompt Wording

Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance. Despite the countless out-of-the-box applications, LLMs are still not reliable. A lot of work is being done to improve the factual accuracy, consistency, and ethical standards of these models through fine-tuning, prompting, and Reinforcement Learning with Human Feedback (RLHF), but no systematic analysis of the responses of these models to different categories of statements, or on their potential vulnerabilities to simple prompting changes is available. In this work, we analyze what confuses GPT-3: how the model responds to certain sensitive topics and what effects the prompt wording has on the model response. We find that GPT-3 correctly disagrees with obvious Conspiracies and Stereotypes but makes mistakes with common Misconceptions and Controversies. The model responses are inconsistent across prompts and settings, highlighting GPT-3’s unreliability.
Online Learning with Set-Valued Feedback

We study a variant of online multiclass classification where the learner predicts a single label but receives a \textit{set of labels} as feedback. In this model, the learner is penalized for not outputting a label contained in the revealed set. We show that unlike online multiclass learning with single-label feedback, deterministic and randomized online learnability are \textit{not equivalent} in the realizable setting under set-valued feedback. In addition, we show that deterministic and randomized realizable learnability are equivalent if the Helly number of the collection of sets that can be revealed as feedback is finite. In light of this separation, we give two new combinatorial dimensions, named the Set Littlestone and Measure Shattering dimension, whose finiteness characterizes deterministic and randomized realizable learnability respectively. Additionally, these dimensions lower- and upper bound the deterministic and randomized minimax regret in the realizable setting. Going beyond the realizable setting, we prove that the Measure shattering dimension continues to characterize learnability and quantify minimax regret in the agnostic setting. Finally, we use our results to establish bounds on the minimax regret for three practical learning settings: online multilabel ranking, online multilabel classification, and real-valued prediction with interval-valued response.
Role of high-order anharmonicity and off-diagonal terms in thermal conductivity: A case study of multiphase 
CsPbBr3

We investigate the influence of three- and four-phonon scattering, perturbative anharmonic phonon renormalization, and off-diagonal terms of coherent phonons on the thermal conductivity of CsPbBr3 phase change perovskite, by using advanced implementations and first-principles simulations. Our study spans a wide temperature range covering the entire structural spectrum. Notably, we demonstrate that the interactions between acoustic and optical phonons result in contrasting trends of phonon frequency shifts for the high-lying optical phonons in orthorhombic and cubic CsPbBr3 as temperature varies. Our findings highlight the significance of wave-like tunneling of coherent phonons in ultralow and glass-like thermal conductivity in halide perovskites.
Learning zeros of Fokker-Planck operators

In this paper we devise a deep learning algorithm to find non-trivial zeros of Fokker-Planck operators when the drift is non-solenoidal. We demonstrate the efficacy of our algorithm for problem dimensions ranging from 2 to 10. Our method scales linearly with dimension in memory usage. We show that this method produces better approximations compared to Monte Carlo methods, for the same overall sample sizes, even in low dimensions. Unlike the Monte Carlo methods, our method gives a functional form of the solution. We also demonstrate that the associated loss function is strongly correlated with the distance from the true solution, thus providing a strong numerical justification for the algorithm. Moreover, this relation seems to be linear asymptotically for small values of the loss function.
Low frequency azimuthal stability of the ionization region of the Hall thruster discharge. II. Global analysis

The linear stability of the Hall thruster discharge is analysed against axial-azimuthal perturbations in the low frequency range using a time-dependent 2D code of the discharge. This azimuthal stability analysis is spatially global, as opposed to the more common local stability analyses, already afforded previously (D. Escobar and E. Ahedo, Phys. Plasmas 21(4), 043505 (2014)). The study covers both axial and axial-azimuthal oscillations, known as breathing mode and spoke, respectively. The influence on the spoke instability of different operation parameters such as discharge voltage, mass flow, and thruster size is assessed by means of different parametric variations and compared against experimental results. Additionally, simplified models are used to unveil and characterize the mechanisms driving the spoke. The results indicate that the spoke is linked to azimuthal oscillations of the ionization process and to the Bohm condition in the transition to the anode sheath. Finally, results obtained from local and global stability analyses are compared in order to explain the discrepancies between both methods.
Augmenting Greybox Fuzzing with Generative AI

Real-world programs expecting structured inputs often has a format-parsing stage gating the deeper program space. Neither a mutation-based approach nor a generative approach can provide a solution that is effective and scalable. Large language models (LLM) pre-trained with an enormous amount of natural language corpus have proved to be effective for understanding the implicit format syntax and generating format-conforming inputs. In this paper, propose ChatFuzz, a greybox fuzzer augmented by generative AI. More specifically, we pick a seed in the fuzzer's seed pool and prompt ChatGPT generative models to variations, which are more likely to be format-conforming and thus of high quality. We conduct extensive experiments to explore the best practice for harvesting the power of generative LLM models. The experiment results show that our approach improves the edge coverage by 12.77\% over the SOTA greybox fuzzer (AFL++) on 12 target programs from three well-tested benchmarks. As for vulnerability detection, \sys is able to perform similar to or better than AFL++ for programs with explicit syntax rules but not for programs with non-trivial syntax.
A Robust Optimization Model for Nonlinear Support Vector Machine

In this paper we present new optimization models for Support Vector Machine (SVM), with the aim of separating data points in two classes by means of a nonlinear classifier. Traditionally, in the nonlinear context data points are firstly mapped to a higher dimensional space and then classified through a SVM-type model. In order to increase the predictive power of SVM, within our approach we include a final linear search procedure aiming to minimize the overall number of misclassified points. Along with a deterministic model in which data are assumed to be perfectly known, we formulate a robust optimization model with bounded-by-lp-norm uncertainty sets. Indeed, when data are real-world observations, measurement errors or noise may corrupt the quality of input values. For this reason, facing uncertainty in the model is a way to robustify the approach. All formulations reduce to linear models with advantages in terms of efficiency compared to other approaches in the literature. Extensive numerical results on real-world datasets show the benefits in terms of accuracy when considering nonlinear decision classifier and protecting the model against uncertainties. Finally, managerial insights to guide the final user are provided.
Data Dimmensioning Algorithms for Delay Differentiation Services in Regular Plans for Mobile Clients

The broadcast problem including the plan design is considered. The data is inserted and numbered into customized size relations at a predefined order. The server's ability to create a full, regular Broadcast Plan (RBP) with a single and multiple channels after some data transformations are performed is studied. The Basic Regular Algorithm (BRA) prepares an RBP and enables users to catch their items while avoiding wasting energy due to their devices. In the case of multiple channels a dynamic grouping solution is proposed, called the Partition Value Algorithm with Less Dimension (PVALD), under a multiplicity constraint. In order to provide an RBP under relative delays, two Dimensioning Algorithms (DA) are developed. The DA1, with the criterion of ratio, offers the desired differentiation of service using the available number of channels. The Extended DA (EXDA) discovers the attainable differentiation of services using the minimum number of channels. This last property, in addition to self-monitoring, and self-organizing, can be offered by servers today providing channel availability and lower energy consumption by using a smaller number of channels of equal bandwidth. Conditions for having the same delay ratio for RBPs and the unification of RBPs are also examined. Simulation results are provided.
Vibrations and Stability of Plates Under Initial Stress

Equilibrium of plates, under initial stresses uniformly distributed across plate thickness; equations derived by B.de SAINT-VENANT by considering isolated plate element; paper presents generalization of Saint-Venant theory and establishes more inclusive theory of plates under initial stress, analogous to R.D. MINDLIN plate theory, by retaining in equations of motion effects of transverse shear deformation and rotatory inertia.
PWR-Align: Leveraging Part-Whole Relationships for Part-wise Rigid Point Cloud Registration in Mixed Reality Applications

We present an efficient and robust point cloud registration (PCR) workflow for part-wise rigid point cloud alignment using the Microsoft HoloLens 2. Point Cloud Registration (PCR) is an important problem in Augmented and Mixed Reality use cases, and we present a study for a special class of non-rigid transformations. Many commonly encountered objects are composed of rigid parts that move relative to one another about joints resulting in non-rigid deformation of the whole object such as robots with manipulators, and machines with hinges. The workflow presented allows us to register the point cloud with various configurations of the point cloud.
Risk stratification of malignant melanoma using neural networks

In order to improve the detection and classification of malignant melanoma, this paper describes an image-based method that can achieve AUROC values of up to 0.78 without additional clinical information. Furthermore, the importance of the domain gap between two different image sources is considered, as it is important to create usability independent of hardware components such as the high-resolution scanner used. Since for the application of machine learning methods, alterations of scanner-specific properties such as brightness, contrast or sharpness can have strong (negative) effects on the quality of the prediction methods, two ways to overcome this domain gap are discussed in this paper.