Miracle-dz/UniFAR-FTUs huggingface dataset:
DP-DNA: A Digital Pattern-Aware DNA Storage System to Improve Encoding Density

With the rapid increase of available digital data, DNA storage is identified as a storage media with high density and capability of long-term preservation, especially for archival storage systems. However, the encoding density (i.e., how many binary bits can be encoded into one nucleotide) and error handling are two major factors intertwined in DNA storage. Considering encoding density, theoretically, one nucleotide can encode two binary bits (upper bound). However, due to biochemical constraints and other necessary information associated with payload, the encoding densities of various DNA storage systems are much less than this upper bound. Additionally, all existing studies of DNA encoding schemes are based on static analysis and really lack the awareness of dynamically changed digital patterns. Therefore, the gap between the static encoding and dynamic binary patterns prevents achieving a higher encoding density for DNA storage systems. In this paper, we propose a new Digital Pattern-Aware DNA storage system, called DP-DNA, which can efficiently store digital data in DNA storage with high encoding density. DP-DNA maintains a set of encoding codes and uses a digital pattern-aware code (DPAC) to analyze the patterns of a binary sequence for a DNA strand and selects an appropriate code for encoding the binary sequence to achieve a high encoding density. An additional encoding field is added to the DNA encoding format, which can distinguish the encoding scheme used for those DNA strands, and thus we can decode DNA data back to its original digital data. Moreover, to further improve the encoding density, a variable-length scheme is proposed to increase the feasibility of the coding scheme with a high encoding density. Finally, the experimental results indicate that the proposed DP-DNA achieves up to 103.5% higher encoding densities than prior work.
Convergence Results in Image Interpolation With the Continuous SSIM

Assessing the similarity of two images is a complex task that attracts significant efforts in the image processing community. The widely used Structural Similarity Index Measure (SSIM) addresses this problem by quantifying a perceptual structural similarity. In this paper we consider a recently introduced continuous SSIM (cSSIM), which allows one to analyze sequences of images of increasingly fine resolutions, and further extend the definition of the index to encompass the locally weighted version that is used in practice. For both the local and the global versions, we prove that the continuous index includes the classical SSIM as a special case, and we provide a precise connection between image similarity measured by the cSSIM and by the $L_2$ norm. Using this connection, we derive bounds on the cSSIM by means of bounds on the $L_2$ error, and we even prove that the two error measures are equivalent in certain circumstances. We exploit these results to obtain precise rates of convergence with respect to the cSSIM for several concrete image interpolation methods, and we further validate these findings by different numerical experiments. This newly established connection paves the way to obtain novel insights into the features and limitations of the SSIM, including on the effect of the local weighted window on the index performances.
TOKCS: Tool for Organizing Key Characteristics of VAM-HRI Systems

Frameworks have begun to emerge to categorize Virtual, Augmented, and Mixed Reality (VAM) technologies that provide immersive, intuitive interfaces to facilitate Human-Robot Interaction. These frameworks, however, fail to capture key characteristics of the growing subﬁeld of VAM-HRI and can be diﬃcult to consistently apply. This work builds upon these prior frameworks through the creation of a Tool for Organizing Key Characteristics of VAM-HRI Systems (TOKCS). TOKCS discretizes the continuous scales used within prior works for more consistent classiﬁcation and adds additional characteristics related to a robot’s internal model, anchor locations, manipulability, and the system’s software and hardware. To showcase the tool’s capability, TOKCS is applied to ﬁnd trends and takeaways from the fourth VAM-HRI workshop. These trends highlight the expressive capability of TOKCS while also helping frame newer trends and future work recommendations for VAM-HRI research.
Effect of stepwise adjustment of Damping factor upon PageRank

The effect of adjusting damping factor α, from a small initial value α0 to the final desired αf value, upon then iterations needed for PageRank computation is observed. Adjustment of the damping factor is done in one or more steps. Results show no improvement in performance over a fixed damping factor based PageRank. Index terms — PageRank algorithm, Step-wise adjustment, Damping factor.
Distilling Transformers for Neural Cross-Domain Search

Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks. Even information retrieval has not been immune to the charm of the transformer, though their large size and cost is generally a barrier to deployment. While there has been much work in streamlining, caching, and modifying transformer architectures for production, here we explore a new direction: distilling a large pre-trained translation model into a lightweight bi-encoder which can be efficiently cached and queried. We argue from a probabilistic perspective that sequence-to-sequence models are a conceptually ideal---albeit highly impractical---retriever. We derive a new distillation objective, implementing it as a data augmentation scheme. Using natural language source code search as a case study for cross-domain search, we demonstrate the validity of this idea by significantly improving upon the current leader of the CodeSearchNet challenge, a recent natural language code search benchmark.
Ancestral sequence reconstruction for co-evolutionary models

The ancestral sequence reconstruction problem is the inference, back in time, of the properties of common sequence ancestors from the measured properties of contemporary populations. Standard algorithms for this problem assume independent (factorized) evolution of the characters of the sequences, which is generally wrong (e.g. proteins and genome sequences). In this work, we have studied this problem for sequences described by global co-evolutionary models, which reproduce the global pattern of cooperative interactions between the elements that compose it. For this, we first modeled the temporal evolution of correlated real valued characters by a multivariate Ornstein–Uhlenbeck process on a finite tree. This represents sequences as Gaussian vectors evolving in a quadratic potential, who describe the selection forces acting on the evolving entities. Under a Bayesian framework, we developed a reconstruction algorithm for these sequences and obtained an analytical expression to quantify the quality of our estimation. We extend this formalism to discrete valued sequences by applying our method to a Potts model. We showed that for both continuous and discrete configurations, there is a wide range of parameters where, to properly reconstruct the ancestral sequences, intra-species correlations must be taken into account. We also demonstrated that, for sequences with discrete elements, our reconstruction algorithm outperforms traditional schemes based on independent site approximations.
The Endokernel: Fast, Secure, and Programmable Subprocess Virtualization

Commodity applications contain more and more combinations of interacting components (user, application, library, and system) and exhibit increasingly diverse tradeoffs between isolation, performance, and programmability. We argue that the challenge of future runtime isolation is best met by embracing the multi-principle nature of applications, rethinking process architecture for fast and extensible intra-process isolation. We present, the Endokernel, a new process model and security architecture that nests an extensible monitor into the standard process for building efficient least-authority abstractions. The Endokernel introduces a new virtual machine abstraction for representing subprocess authority, which is enforced by an efficient self-isolating monitor that maps the abstraction to system level objects (processes, threads, files, and signals). We show how the Endokernel can be used to develop specialized separation abstractions using an exokernel-like organization to provide virtual privilege rings, which we use to reorganize and secure NGINX. Our prototype, includes a new syscall monitor, the nexpoline, and explores the tradeoffs of implementing it with diverse mechanisms, including Intel Control Enhancement Technology. Overall, we believe sub-process isolation is a must and that the Endokernel exposes an essential set of abstractions for realizing this in a simple and feasible way.
Optomechanical strong coupling between a single photon and a single atom

Single atoms coupled to a cavity offer unique opportunities as quantum optomechanical devices because of their small mass and strong interaction with light. A particular regime of interest in optomechanics is that of ‘single-photon strong coupling’, where motional displacements on the order of the zero-point uncertainty are sufficient to shift the cavity resonance frequency by more than its linewidth. In many cavity QED platforms, however, this is unfeasible due to the large cavity linewidth. Here, we propose an alternative route in such systems, which instead relies on the coupling of atomic motion to the much narrower cavity-dressed atomic resonance frequency. We discuss and optimize the conditions in which the scattering properties of single photons from the atom-cavity system become highly entangled with the atomic motional wave function. We also analyze the prominent observable features of this optomechanical strong coupling, which include a per-photon motional heating that is significantly larger than the single-photon recoil energy, as well as mechanically-induced oscillations in time of the second-order correlation function of the emitted light. This physics should be realizable in current experimental setups, such as trapped atoms coupled to photonic crystal cavities, and more broadly opens the door to realizing qualitatively different phenomena beyond what has been observed in optomechanical systems thus far.
Kori: Interactive Synthesis of Text and Charts in Data Documents

Charts go hand in hand with text to communicate complex data and are widely adopted in news articles, online blogs, and academic papers. They provide graphical summaries of the data, while text explains the message and context. However, synthesizing information across text and charts is difficult; it requires readers to frequently shift their attention. We investigated ways to support the tight coupling of text and charts in data documents. To understand their interplay, we analyzed the design space of chart–text references through news articles and scientific papers. Informed by the analysis, we developed a mixed-initiative interface enabling users to construct interactive references between text and charts. It leverages natural language processing to automatically suggest references as well as allows users to manually construct other references effortlessly. A user study complemented with algorithmic evaluation of the system suggests that the interface provides an effective way to compose interactive data documents.
Deep Learning for Embodied Vision Navigation: A Survey

—Navigation is one of the fundamental features of a autonomous robot. And the ability of long-term navigation with semantic instruction is a ‘holy grail’ goals of intelligent robots. The development of 3D simulation technology provide a large scale of data to simulate the real-world environment. The deep learning proves its ability to robustly learn various embodied navigation tasks. However, deep learning on embodied navigation is still in its infancy due to the unique challenges faced by the navigation exploration and learning from partial observed visual input. Recently, deep learning in embodied navigation has become even thriving, with numerous methods have been proposed to tackle different challenges in this area. To give a promising direction for future research, in this paper, we present a comprehensive review of embodied navigation tasks and the recent progress in deep learning based methods. It includes two major tasks: target-oriented navigation and the instruction-oriented navigation.
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation

This paper describes our work in participation of the IWSLT-2021 offline speech translation task. Our system was built in a cascade form, including a speaker diarization module, an Automatic Speech Recognition (ASR) module and a Machine Translation (MT) module. We directly use the LIUM SpkDiarization tool as the diarization module. The ASR module is trained with three ASR datasets from different sources, by multi-source training, using a modified Transformer encoder. The MT module is pretrained on the large-scale WMT news translation dataset and fine-tuned on the TED corpus. Our method achieves 24.6 BLEU score on the 2021 test set.
An Anonymous On-Street Parking Authentication Scheme via Zero-Knowledge Set Membership Proof

The amount of information generated grows as more and more sensor and IoT devices are deployed in smart cities. It is of utmost importance for us to consider the privacy data leakage and compromised identity from both outside adversaries and inside abuse of data access privilege. The security assumption of the system should not solely rely on the fact that permission and access control were being implemented correctly. Quite the contrary, a system can be designed in a way that user's identity data and usage traces are not leaked even if the system had been compromised. Based upon our previous on-street parking system utilizing Bluetooth Low Energy (BLE) beacons, we applied a cryptographic primitive called zero-knowledge proof to our authentication system. A commitment scheme and Merkle tree is combined in the setup to achieve zero-knowledge set membership proof. Doing so, the user is anonymous to the server between authentication sessions, while the server's still able to verify the legitimacy of such user. The on-street parking system is therefore immune to privacy data leakage, as for now one cannot mass-query and profile certain user's traces within the system.
Timing observations of three Galactic millisecond pulsars

We report observed and derived timing parameters for three millisecond pulsars (MSPs) from observations collected with the Parkes 64-m telescope, Murriyang. The pulsars were found during reprocessing of archival survey data by Mickaliger et al. One of the new pulsars (PSR J1546–5925) has a spin period P = 7.8 ms and is isolated. The other two (PSR J0921–5202 with P = 9.7 ms and PSR J1146–6610 with P = 3.7 ms) are in binary systems around low-mass (${\gt}0.2\, {\rm M}_{\odot }$) companions. Their respective orbital periods are 38.2 and 62.8 d. While PSR J0921–5202 has a low orbital eccentricity e = 1.3 × 10−5, in keeping with many other Galactic MSPs, PSR J1146–6610 has a significantly larger eccentricity, e = 7.4 × 10−3. This makes it a likely member of a group of eccentric MSP–helium white dwarf binary systems in the Galactic disc whose formation is poorly understood. Two of the pulsars are co-located with previously unidentified point sources discovered with the Fermi satellite’s Large Area Telescope, but no γ-ray pulsations have been detected, likely due to their low spin-down powers. We also show that, particularly in terms of orbital diversity, the current sample of MSPs is far from complete and is subject to a number of selection biases.
Rauschreduktion in digitalen Niedrigdosis-Röntgenbildern

Rauschreduktion in Niedrigdosis-Rontgenbildern, wie sie beispielsweise in der intraoperativen Fluoroskopie erzeugt werden, stellt fur die Bildverarbeitung der akquirierten Aufnahmen einen entscheidenden Schlusselfaktor dar. Fur eine visuelle Qualitatsverbesserung wird in dieser Arbeit eine auf einem multiskalaren Ansatz beruhende Rauschreduktion vorgestellt, welche auf der zur nichtlinearen, anisotropen Diffusion verwandten bilateralen Filterung basiert. Hierbei werden insbesondere die bestehenden Verfahren auf die Eigenschaften des im Rontgenbild vorhandenen Rauschens angepasst. Die Leistungsfahigkeit der entwickelten Filterung wird an klinischen Fluoroskopiesequenzen demonstriert.
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints

The detection of malware is a critical task for the protection of computing environments. This task often requires extremely low false positive rates (FPR) of 0.01% or even lower, for which modern machine learning has no readily available tools. We introduce the first broad investigation of the use of uncertainty for malware detection across multiple datasets, models, and feature types. We show how ensembling and Bayesian treatments of machine learning methods for static malware detection allow for improved identification of model errors, uncovering of new malware families, and predictive performance under extreme false positive constraints. In particular, we improve the true positive rate (TPR) at an actual realized FPR of 1e-5 from an expected 0.69 for previous methods to 0.80 on the best performing model class on the Sophos industry scale dataset. We additionally demonstrate how previous works have used an evaluation protocol that can lead to misleading results.
El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification

As gravitational-wave interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with gravitational waves does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with kilonovae, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo the only EM counterpart of a binary neutron star merger discovered to date and AT2019npv a supernova that was initially suspected as a counterpart of the gravitationalwave event, GW190814, but was later ruled out after further analysis.
Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading

Despite the advancement in the domain of audio and audio-visual speech recognition, visual speech recognition systems are still quite under-explored due to the visual ambiguity of some phonemes. In this work, we propose a new lip-reading model that combines three contributions. First, the model front-end adopts a spatio-temporal attention mechanism to help extract the informative data from the input visual frames. Second, the model back-end utilizes a sequence-level and frame-level Knowledge Distillation (KD) techniques that allow leveraging audio data during the visual model training. Third, a data preprocessing pipeline is adopted that includes facial landmarks detection-based lip-alignment. On LRW lip-reading dataset benchmark, a noticeable accuracy improvement is demonstrated; the spatio-temporal attention, Knowledge Distillation, and lip-alignment contributions achieved 88.43%, 88.64%, and 88.37% respectively.
Cascaded Refinement Network for Point Cloud Completion With Self-Supervision

Point clouds are often sparse and incomplete, which imposes difficulties for real-world applications. Existing shape completion methods tend to generate rough shapes without fine-grained details. Considering this, we introduce a two-branch network for shape completion. The first branch is a cascaded shape completion sub-network to synthesize complete objects, where we propose to use the partial input together with the coarse output to preserve the object details during the dense point reconstruction. The second branch is an auto-encoder to reconstruct the original partial input. The two branches share a same feature extractor to learn an accurate global feature for shape completion. Furthermore, we propose two strategies to enable the training of our network when ground truth data are not available. This is to mitigate the dependence of existing approaches on large amounts of ground truth training data that are often difficult to obtain in real-world applications. Additionally, our proposed strategies are also able to improve the reconstruction quality for fully supervised learning. We verify our approach in self-supervised, semi-supervised and fully supervised settings with superior performances. Quantitative and qualitative results on different datasets demonstrate that our method achieves more realistic outputs than state-of-the-art approaches on the point cloud completion task.
Syntheses and Energy Storage Applications of MxSy (M = Cu, Ag, Au) and Their Composites: Rechargeable Batteries and Supercapacitors

The development of novel materials to improve energy storage efficiencies is essential to satisfy ever‐increasing energy demands. MxSy (M = Cu, Ag, Au) and their composites offer opportunities and enormous prospects in energy storage due to their extraordinary electrochemical properties, which promote promising energy storage characteristics in terms of stability, energy and power density, lifetime, etc. Recent developments of MxSy (M = Cu, Ag, Au) and their composites with various morphologies have received considerable attention. Multidimensional morphologies of MxSy (M = Cu, Ag, Au) and their composites have enriched charge‐storage and electron‐transport abilities. This review provides a detailed account of the synthetic strategies based on sulfur sources (i.e., inorganic sulfur sources, organosulfur sources, and other sulfur sources), which dictate the morphologies of nanosized MxSy (M = Cu, Ag, Au) and their composites. Notably, nanostructured silver sulfide can be prepared from the bulk to nanoscale. Moreover, the electrochemical applications of these materials for lithium‐ion batteries, sodium‐ion batteries, lithium‐sulfur batteries, and supercapacitors are summarized. Finally, future perspectives on the development challenges and major opportunities for MxSy (M = Cu, Ag, Au) and their composites, which must be overcome to achieve further improvements in electrochemical performance are outlined.