The theory of compressed sensing (CS), offering a novel perspective beyond the Nyquist-Shannon sampling theorem, has garnered significant attention from geophysicists. It enables the reconstruction of ideal high-density seismic signals using cost-effective random undersampling, provided that the seismic signals can be sparsely represented. However, the near-surface noise challenges the sparse representation of seismic reflection signals. To suppress near-surface noise and achieve a better sparse representation of seismic reflection signals within the CS framework, this article develops a method for CS-based near-surface noise suppression, centered on 3-D conical Radon transform (CRT) combined with the CS theory. First, this study introduces the 3-D CRT into seismic signal processing, exploiting the “conical surface” characteristic of near-surface noise in 3-D CMP records. A method for extracting seismic signals and suppressing noise in the 3-D conical Radon domain is proposed. Subsequently, an adaptive filtering strategy is integrated with the 3-D CRT to develop a method for adaptive near-surface noise suppression in the 3-D conical Radon domain. Finally, combining CS theory, adaptive filtering strategies, and the 3-D Radon transform, this article proposes a method for adaptive near-surface noise suppression in the 3-D conical Radon domain based on CS. Numerical examples demonstrate that the integration of the CS random undersampling framework, adaptive filtering strategies, and the 3-D CRT significantly mitigates the impact of near-surface noise on seismic reflection signal reconstruction within the CS framework. This effectively addresses the issue of the 3-D CRT’s unsuitability for near-surface noise suppression within the CS framework.

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm. Furthermore, through a meticulous evaluation of various One-hot encoding schemes alongside numerous hybrid neural network models, we discern that encoding and models of moderate complexity ideally balance performance and efficiency. On this foundation, we advance a novel hybrid model, the CRISPR-MCA, which capitalizes on multi-feature extraction to enhance predictive accuracy. The empirical results affirm that the ESB class rebalancing strategy surpasses five conventional methods in addressing extreme dataset imbalances, demonstrating superior efficacy and broader applicability across diverse models. Notably, the CRISPR-MCA model excels in off-target effect prediction across four distinct mismatches-only datasets and significantly outperforms contemporary state-of-the-art models in datasets comprising both mismatches and indels. In summation, the CRISPR-MCA model, coupled with the ESB rebalancing strategy, offers profound insights and a robust framework for future explorations in this field.

Densification of the network deployment, for example by adding new sectors or sites within an existing mobile communication network, has traditionally been an efficient way to improve the system coverage and capacity. That will be the case even for the ultra-reliable low-latency communication (URLLC) services, but the overall situation and the feasibility of the different deployment options will depend on the characteristics of the URLLC service in question. URLLC services with relaxed latency requirements, allowing multiple transmission attempts, can tolerate a decent level of inter-cell interference while still being able to guarantee the desired quality-of-service for all users, which makes it possible to improve the URLLC service capacity by adding new gNodeBs with omnidirectional or directional antennas. However, when the URLLC services require extremely low latency that do not allow for retransmissions, the system performance becomes quite sensitive to the inter-cell interference, which means that in practice the gNodeBs should be equipped with beamformed antennas to reach high levels of URLLC service capacity. Finally, an active distributed antenna system could be an efficient way to secure good coverage throughout the desired service area.

To ensure the high level of automation required in today's industrial applications, next-generation wireless networks must enable real-time control and automation of dynamic processes with the requirements of extreme low-latency and ultra-reliable communications. In this paper, we provide a performance assessment for the co-existence of a public enhanced mobile broadband (eMBB) and a local non-public factory (URLLC) network and evaluate the network conditions under which the stringent latency and reliability requirements of factory automation applications are met. The evaluations consider both an unsynchronized and a synchronized time division duplexing (TDD) deployment between the networks, as well as scenarios both with and without any macro eMBB traffic located inside the factory. The results show that an unsynchronized deployment is possible if the isolation between the networks is sufficiently high, either as a result of a separation distance, wall loss or the use of separate frequencies for the networks. A synchronized deployment will avoid the cross-link interference, but it will not resolve the problems related to the closed access and the near-far interference. If the factory contains eMBB traffic served by the overlaid macro cells, the performance of both networks will suffer due to a high level of cross-link and near-far interference. The problems related to the cross-link interference can be resolved by synchronizing the networks, while the level of the near-far interference can be reduced by allowing the eMBB users to be connected to base stations located inside the factory. Finally, if an unsynchronized deployment is desired, the factory should be deployed on an isolated frequency.

The possibility of regulating the behavior of live animals using biologically-inspired robots has attracted the interest of biologists and engineers for over twenty-five years. From early work on insects to recent endeavors on mammals, we have witnessed fascinating applications that have pushed forward our understanding of animal behavior along new directions. Despite significant progress, most of the research has focused on open-loop control systems, in which robots execute predetermined actions, independent of the animal behavior. We integrate mathematical modeling of social behavior toward the design of realistic feedback laws for robots to interact with a live animal. In particular, we leverage recent advancements in data-driven modeling of zebrafish behavior. Ultimately, we establish a novel robotic platform that allows real-time actuation of a biologically-inspired 3D-printed zebrafish replica to implement model-based control of animal behavior. We demonstrate our approach through a series of experiments, designed to elucidate the appraisal of the replica by live subjects with respect to conspecifics and to quantify the biological value of closed-loop control.

Abstract Traditional designs for functional languages (such as Haskell or ML) have separate sorts of syntax for terms and types. In contrast, many dependently typed languages use a unified syntax that accounts for both terms and types. Unified syntax has some interesting advantages over separate syntax, including less duplication of concepts, and added expressiveness. However, integrating unrestricted general recursion in calculi with unified syntax is challenging when some level of type-level computation is present, since properties such as decidable type-checking are easily lost. This paper presents a family of calculi called pure iso-type systems (PITSs), which employs unified syntax, supports general recursion and preserves decidable type-checking. PITS is comparable in simplicity to pure type systems (PTSs), and is useful to serve as a foundation for functional languages that stand in-between traditional ML-like languages and fully blown dependently typed languages. In PITS, recursion and recursive types are completely unrestricted and type equality is simply based on alpha-equality, just like traditional ML-style languages. However, like most dependently typed languages, PITS uses unified syntax, naturally supporting many advanced type system features. Instead of implicit type conversion, PITS provides a generalization of iso-recursive types called iso-types . Iso-types replace the conversion rule typically used in dependently typed calculus and make every type-level computation explicit via cast operators. Iso-types avoid the complexity of explicit equality proofs employed in other approaches with casts. We study three variants of PITS that differ on the reduction strategy employed by the cast operators: call-by-name , call-by-value and parallel reduction . One key finding is that while using call-by-value or call-by-name reduction in casts loses some expressive power, it allows those variants of PITS to have simple and direct operational semantics and proofs. In contrast, the variant of PITS with parallel reduction retains the expressive power of PTS conversion, at the cost of a more complex metatheory.

In recent years dependent types have become a hot topic in programming language research. A key reason why dependent types are interesting is that they allow unifying types and terms, which enables both additional expressiveness and economy of concepts . Unfortunately there has been much less work on dependently typed calculi for object-oriented programming. This is partly because it is widely acknowledged that the combination between dependent types and subtyping is particularly challenging. This paper presents λ I ≤ , which is a dependently typed generalization of System F ≤ . The resulting calculus follows the style of Pure Type Systems, and contains a single unified syntactic sort that accounts for expressions, types and kinds. To address the challenges posed by the combination of dependent types and subtyping, λ I ≤ employs a novel technique that unifies typing and subtyping . In λ I ≤ there is only a judgement that is akin to a typed version of subtyping. Both the typing relation, as well as type well-formedness are just special cases of the subtyping relation. The resulting calculus has a rich metatheory and enjoys of several standard and desirable properties, such as subject reduction , transitivity of subtyping , narrowing as well as standard substitution lemmas . All the metatheory of λ I ≤ is mechanically proved in the Coq theorem prover. Furthermore, (and as far as we are aware) λ I ≤ is the first dependently typed calculus that completely subsumes System F ≤ , while preserving various desirable properties.

Dense deployment which brings small base stations (BS) closer to mobile devices is considered as a promising solution to the booming traffic demand. Meanwhile, the utilization of new frequency bands and spectrum aggregation techniques provide more options for spectrum choice.Whether to increase BS density or to acquire more spectrum is a key strategic question for mobile operators. In this paper, we investigate the relationship between BS density and spectrum with regard to individual user throughput target. Our work takes into account load-dependent interference model and various traffic demands. Numerical results show that densification is more effective in sparse networks than in already dense networks. In sparse networks, doubling BS density results in almost twofold throughput increase. However, in dense networks where BSs outnumber users, more than 10 times of BS density is needed to double user throughput. Meanwhile, spectrum has a linear relationship with user throughput for a given BS density. The impact of traffic types is also discussed. Even with the same area throughput requirement, different combination of user density and individual traffic amount leads to different needs for BS density and spectrum.