Miracle-dz/UniFAR-FTUs huggingface dataset:
QF-Geo: Capacity Aware Geographic Routing using Bounded Regions of Wireless Meshes

Routing in wireless meshes must detour around holes. Extant routing protocols often underperform in minimally connected networks where holes are larger and more frequent. Minimal density networks are common in practice due to deployment cost constraints, mobility dynamics, and/or adversarial jamming. Protocols that use global search to determine optimal paths incur search overhead that limits scaling. Conversely, protocols that use local search tend to find approximately optimal paths at higher densities due to the existence of geometrically direct routes but underperform as the connectivity lowers and regional (or global) information is required to address holes. Designing a routing protocol to achieve high throughput-latency performance across network densities, mobility, and interference dynamics remains challenging. This paper shows that, in a probabilistic setting, bounded exploration can be leveraged to mitigate this challenge. We show, first, that the length of shortest paths in networks with uniform random node distribution can, with high probability (whp), be bounded. Thus, whp a shortest path may be found by limiting exploration to an elliptic region whose size is a function of the network density and the Euclidean distance between the two endpoints. Second, we propose a geographic routing protocol that achieves high reliability and throughput-latency performance by forwarding packets within an ellipse whose size is bounded similarly and by an estimate of the available capacity. Our protocol, QF-Geo, selects forwarding relays within the elliptic region, prioritizing those with sufficient capacity to avoid bottlenecks. Our simulation results show that QF-Geo achieves high goodput efficiency and reliability in both static and mobile networks across both low and high densities, at large scales, with a wide range of concurrent flows, and in the presence of adversarial jamming.
Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression

High-dimensional linear regression under heavy-tailed noise or outlier corruption is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suffer from high computational costs, especially since the robust loss functions are usually non-smooth. More recently, computationally fast non-convex approaches via sub-gradient descent are proposed, which, unfortunately, fail to deliver a statistically consistent estimator even under sub-Gaussian noise. In this paper, we introduce a projected sub-gradient descent algorithm for both the sparse linear regression and low-rank linear regression problems. The algorithm is not only computationally efficient with linear convergence but also statistically optimal, be the noise Gaussian or heavy-tailed with a finite 1 + epsilon moment. The convergence theory is established for a general framework and its specific applications to absolute loss, Huber loss and quantile loss are investigated. Compared with existing non-convex methods, ours reveals a surprising phenomenon of two-phase convergence. In phase one, the algorithm behaves as in typical non-smooth optimization that requires gradually decaying stepsizes. However, phase one only delivers a statistically sub-optimal estimator, which is already observed in the existing literature. Interestingly, during phase two, the algorithm converges linearly as if minimizing a smooth and strongly convex objective function, and thus a constant stepsize suffices. Underlying the phase-two convergence is the smoothing effect of random noise to the non-smooth robust losses in an area close but not too close to the truth. Numerical simulations confirm our theoretical discovery and showcase the superiority of our algorithm over prior methods.
Mobile Image Restoration via Prior Quantization

In digital images, the performance of optical aberration is a multivariate degradation, where the spectral of the scene, the lens imperfections, and the field of view together contribute to the results. Besides eliminating it at the hardware level, the post-processing system, which utilizes various prior information, is significant for correction. However, due to the content differences among priors, the pipeline that aligns these factors shows limited efficiency and unoptimized restoration. Here, we propose a prior quantization model to correct the optical aberrations in image processing systems. To integrate these messages, we encode various priors into a latent space and quantify them by the learnable codebooks. After quantization, the prior codes are fused with the image restoration branch to realize targeted optical aberration correction. Comprehensive experiments demonstrate the flexibility of the proposed method and validate its potential to accomplish targeted restoration for a specific camera. Furthermore, our model promises to analyze the correlation between the various priors and the optical aberration of devices, which is helpful for joint soft-hardware design.
A semi-automatic method for document classification in the shipping industry

In the shipping industry, document classification plays a crucial role in ensuring that the necessary documents are properly identified and processed for customs clearance. OCR technology is being used to automate the process of document classification, which involves identifying important documents such as Commercial Invoices, Packing Lists, Export/Import Customs Declarations, Bills of Lading, Sea Waybills, Certificates, Air or Rail Waybills, Arrival Notices, Certificate of Origin, Importer Security Filings, and Letters of Credit. By using OCR technology, the shipping industry can improve accuracy and efficiency in document classification and streamline the customs clearance process. The aim of this study is to build a robust document classification system based on keyword frequencies. The research is carried out by analyzing Contract-Breach law documents available with IN-D. The documents were collected by scraping the Singapore Government Judiciary website. The database developed has 250 Contract-Breach documents. These documents are splitted to generate 200 training documents and 50 test documents. A semi-automatic approach is used to select keyword vectors for document classification. The accuracy of the reported model is 92.00 %.
Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network

Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments. To tackle this challenge, we investigate a cloud native wireless architecture that employs container-based virtualization to enable flexible service deployment. We then study two representative use cases: network slicing and Multi-Access Edge Computing. To optimize resource allocation in these scenarios, we leverage deep reinforcement learning techniques and introduce two model-free algorithms capable of monitoring the network state and dynamically training allocation policies. We validate the effectiveness of our algorithms in a testbed developed using Free5gc. Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks.
DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects

To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better approximation to human behavior and enable the robot to operate on diverse articulated objects. To this end, we propose a new benchmark called DexArt, which involves Dexterous manipulation with Articulated objects in a physical simulator. In our benchmark, we define multiple complex manipulation tasks, and the robot hand will need to manipulate diverse articulated objects within each task. Our main focus is to evaluate the generalizability of the learned policy on unseen articulated objects. This is very challenging given the high degrees of freedom of both hands and objects. We use Reinforcement Learning with 3D representation learning to achieve generalization. Through extensive studies, we provide new insights into how 3D representation learning affects decision making in RL with 3D point cloud inputs. More details can be found at https://www.chenbao. tech/dexart/.
A variational approach to mapping: an exploration of map representation for SLAM

Simultaneous Localization and Mapping (SLAM) algorithms are used by autonomous robots to build or update maps of an environment while maintaining their position simultaneously. A fundamental open problem in SLAM is the e ective representation of the map in unknown, ambiguous, complex, dynamic environments. Representing such environments in a suitable manner is a complex task. Existing approaches to SLAM use map representations that store individual features (range measurements, image patches, or higher level semantic features) and their locations in the environment. The choice of how the map is represented produces limitations which in many ways are unfavourable for application in real-world scenarios. In this thesis, a new approach to SLAM is explored that rede nes sensing and robot motion as acts of deformation of a di erentiable surface. Distance elds and level set methods are utilized to de ne a parallel to the components of the SLAM estimation process and an algorithm is developed and demonstrated. The variational framework developed is capable of representing complex dynamic scenes and spatially varying uncertainty for sensor and robot models.
QFT with tensorial and local degrees of freedom: Phase structure from functional renormalization

Field theories with combinatorial non-local interactions such as tensor invariants are interesting candidates for describing a phase transition from discrete quantum-gravitational to continuum geometry. In the so-called cyclic-melonic potential approximation of a tensorial field theory on the r-dimensional torus it was recently shown using functional renormalization group techniques that no such phase transition to a condensate phase with a tentative continuum geometric interpretation is possible. Here, keeping the same approximation, we show how to overcome this limitation amending the theory by local degrees freedom on Rd. We find that the effective r − 1 dimensions of the torus part dynamically vanish along the renormalization group flow while the d local dimensions persist up to small momentum scales. Consequently, for d > 2 one can find a phase structure allowing also for phase transitions.
Long‐term observations of increasing snow cover in the western Cairngorms

As part of the UK Environmental Change Network (ECN) long‐term monitoring, an automatic repeat‐photography camera was installed to record changes in landscape phenology in the Allt a'Mharcaidh catchment, Cairngorms National Park, Scotland. For 13 consecutive winters between 2002 and 2015, the date for the onset of continuous winter snow cover, and subsequent melt, was recorded on slopes of north and north‐easterly aspect at altitudes between 450m and 1111m amsl. Results show that the period of time during which snow is continuously present in the catchment has increased significantly by 81 (±21.01) days over the 13‐year period, and that this is largely driven by a significantly later melt date, rather than earlier onset of winter snow cover.
Occurrence and phylogenetic analysis of fowl adenovirus E in broiler flocks from Gaza Strip Palestine.

INTRODUCTION Inclusion-body hepatitis (IBH) and hydropericardium syndrome (HPS) are highly infectious diseases caused by fowl adenoviruses (FAdVs). IBH and HPS cause major economic losses in poultry production. IBH is caused by multiple FAdV serotypes such as FAdV-11, FAdV8a, and FAdV8b; while HPS is mainly caused by the FAdV-4 serotype. In 2018, we detected FAdVs in West Bank - Palestine for the first time. This study aims to monitor the emergence of new FAdVs in broiler farms in Gaza Strip-Palestine in 2022. METHODOLOGY The clinical signs, necropsy, and histopathological findings associated with IBH in the suspected birds were recorded. Polymerase chain reaction (PCR) was performed using primers matching the virus-encoded L1 loop of the hexon gene. The sequences of the L1 loop were analyzed and a phylogenetic tree was constructed and compared with the related FAdV field isolates and reference strains from different regions of the world available in GenBank. RESULTS The infected broiler displayed FAdVs-induced clinical symptoms and pathological lesions with mortality rates ranging from 20-46%. The L1 loop sequences from the infected flocks were submitted to GenBank with accession numbers ON638995, ON872150, and ON872151. The identified L1 loop gene has high nucleotide homology (96.7-97.9%) to the highly pathogenic FAdV E serotype 8b strain FAdV_isolate_04-53357-122_Canada_2007 (GenBank: EF685489) and 94.5-94.6% to FAdV_10_Belgium_2010 isolate 11-15941 (GenBank: AF339924.1). Furthermore, the phylogenetic analysis indicated that they belong to FAdV-E serotype 8b. CONCLUSIONS Our study reports the emergence of FAdV-E causing IBH disease in broiler chickens for the first time in Gaza in Palestine.
The Impact of Randomized Family-Centered Interventions on Family-Centered Outcomes in the Adult Intensive Care Unit: A Systematic Review

Objective: To review the literature for randomized family-centered interventions with family-centered outcomes in the adult intensive care unit (ICU). Data Sources: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and the Cochrane Library database from inception until February 2023. Study Selection: We included articles involving randomized controlled trials (RCTs) in the adult critical care setting evaluating family-centered interventions and reporting family-centered outcomes. Data Extraction: We extracted data on author, year of publication, setting, number of participants, intervention category, intervention, and family-centered outcomes. Data Synthesis: There were 52 RCTs included in the analysis, mostly involving communication and receiving information (38%) and receiving care and meeting family member needs (38%). Nearly two-thirds of studies (N = 35; 67.3%) found improvements in at least 1 family-centered outcome. Most studies (N = 24/40; 60%) exploring the impact of family-centered interventions on mental health outcomes showed improvement. Improvements in patient-centered outcomes (N = 7/17; 41%) and healthcare worker outcomes (N = 1/5; 20%) were less commonly found. Conclusions: Family-centered interventions improve family-centered outcomes in the adult ICU and may be beneficial to patients and healthcare workers.
What do seller manipulations of online product reviews mean to consumers

There is growing evidence that consumers are influenced by online product reviews when making a variety of purchase decisions. Firms are therefore tempted to monitor and manipulate online product reviews on the company’s website or forum to influence consumer perceptions by anonymously posting positive reviews, hiding or deleting unfavorable reviews, or offering rewards to consumers who post favorable reviews. Our review of the literature has revealed a surprising shortage of work directed at the development of an integrative theoretical framework or rigorous empirical studies on the effectiveness and the exact impact of such activities on the payoffs to various parties. This study fills a void in the online marketing and information manipulation literature by studying consumers’ suspicion, awareness and evaluation of specific manipulation tactics through in-depth interviews with 16 experienced online shoppers in China. We adopt a grounded theory approach to analyze the qualitative data and end up with a series of research propositions (research framework) for further testing and verification. The findings about consumers’ views of online manipulations would provide valuable insights to industry associations and policy makers on whether and how to regulate online manipulation activities to ensure the healthy development of the e-commerce.
Discharge destinations for young people with a first episode of psychosis after attending an early intervention for psychosis service

Objective: Early intervention for psychosis services result in superior outcomes in the domains of symptomatic and functional recovery, hospitalisation and employment compared to standard services; however, the optimal duration of care with these services is unknown. Knowledge on the discharge destinations, specifically the proportion discharged to high- and low-intensity services, could provide insights into the proportion of who may require a longer tenure of care. This study aimed to determine (1) the discharge destinations from early intervention for psychosis services and (2) baseline and intra-episode factors associated with discharge to the secondary care/adult mental health service. Methodology: This study was conducted at the Early Psychosis Prevention and Intervention Centre in Melbourne and included all young people treated by the service with a first episode of psychosis over a 6-year period. Discharge destinations were categorised according to high-intensity services, namely, secondary mental health care, or lower intensity services, such as private practitioners or primary care. Results: A total of 1101 young people with a first episode of psychosis were included in the study, of whom 58.8% were male and the median age was 20.0 years (interquartile range: 17–22). After a median of 95.4 weeks (interquartile range: 66.7–105.7), 36.6% were discharged to the adult mental health services, which was associated with being not in employment, education or training at presentation (odds ratio = 1.71, 95% confidence interval [1.23, 2.37]); experiencing a relapse (odds ratio = 1.76, 95% confidence interval [1.24, 2.49]); and being admitted to a mental health unit (odds ratio = 3.98, 95% confidence interval [2.61, 6.09]). Young people who lived with their parents were less likely to be discharged to secondary care services (odds ratio = 0.52, 95% confidence interval [0.37, 0.73]), as were those who were achieving symptomatic remission within 12 weeks (odds ratio = 0.60, 95% confidence interval [0.43, 0.83]). Migrant status and the duration of untreated psychosis were not associated with discharge destination. Conclusion: These findings indicate that there is a sizable, identifiable minority who may benefit from a longer episode of care with early intervention for psychosis services.
Tropifexor plus cenicriviroc combination versus monotherapy in nonalcoholic steatohepatitis: Results from the phase 2b TANDEM study

Background and Aims: With distinct mechanisms of action, the combination of tropifexor (TXR) and cenicriviroc (CVC) may provide an effective treatment for NASH. This randomized, multicenter, double-blind, phase 2b study assessed the safety and efficacy of TXR and CVC combination, compared with respective monotherapies. Approach and Results: Patients (N = 193) were randomized 1:1:1:1 to once-daily TXR 140 μg (TXR140), CVC 150 mg (CVC), TXR 140 μg + CVC 150 mg (TXR140 + CVC), or TXR 90 μg + CVC 150 mg (TXR90 + CVC) for 48 weeks. The primary and secondary end points were safety and histological improvement, respectively. Rates of adverse events (AEs) were similar across treatment groups. Pruritus was the most frequently experienced AE, with highest incidence in the TXR140 group (40.0%). In TXR and combination groups, alanine aminotransferase (ALT) decreased from baseline to 48 weeks (geometric mean change: −21%, TXR140; −16%, TXR140 + CVC; −13%, TXR90 + CVC; and +17%, CVC). Reductions in body weight observed at week 24 (mean changes from baseline: TXR140, −2.5 kg; TXR140 + CVC, −1.7 kg; TXR90 + CVC, −1.0 kg; and CVC, −0.1 kg) were sustained to week 48. At least 1-point improvement in fibrosis stage/steatohepatitis resolution without worsening of fibrosis was observed in 32.3%/25.8%, 31.6%/15.8%, 29.7%/13.5%, and 32.5%/22.5% of patients in the TXR140, CVC, TXR140 + CVC, and TXR90 + CVC groups, respectively. Conclusions: The safety profile of TXR + CVC combination was similar to respective monotherapies, with no new signals. TXR monotherapy showed sustained ALT and body weight decreases. No substantial incremental efficacy was observed with TXR + CVC combination on ALT, body weight, or in histological end points compared with monotherapy.
Mining discriminative class codes for multi-class classification based on minimizing generalization errors

Error Correcting Output Code (ECOC) has emerged as one of promising techniques for solving multi-class classification. In the ECOC framework, a multi-class problem is decomposed into several binary ones with a coding design scheme. Despite this, the suitable multi-class decomposition scheme is still ongoing research in machine learning. In this work, we propose a novel multi-class coding design method to mine the effective and compact class codes for multi-class classification. For a given n-class problem, this method decomposes the classes into subsets by embedding a structure of binary trees. We put forward a novel splitting criterion based on minimizing generalization errors across the classes. Then, a greedy search procedure is applied to explore the optimal tree structure for the problem domain. We run experiments on many multi-class UCI datasets. The experimental results show that our proposed method can achieve better classification performance than the common ECOC design methods.
Genetic portrait of the Amazonian communities of Peru and Bolivia: The legacy of the Takanan‐speaking people

During the colonial period in South America, many autochthonous populations were affected by relocation by European missionary reductions and other factors that impacted and reconfigured their genetic makeup. Presently, the descendants of some “reduced” and other isolated groups are distributed in the Amazonian areas of Peru, Bolivia, and Brazil, and among them, speakers of Takanan and Panoan languages. Based on linguistics, these peoples should be closely related, but so far no DNA comparison studies have been conducted to corroborate a genetic relationship. To clarify these questions, we used a set of 15 short tandem repeats of the non‐recombining part of the Y‐chromosome (Y‐STRs) and mitochondrial DNA (mtDNA) control region sequence data. Paternal line comparisons showed the Takanan‐speaking peoples from Peru and Bolivia descended from recent common ancestors; one group was related to Arawakan, Jivaroan, and Cocama and the other to Panoan speakers, consistent with linguistics. Also, a genetic affinity for maternal lines was observed between some Takanan speakers and individuals who spoke different Amazonian languages. Our results supported a shared ancestry of Takanan, Panoan, Cocama, and Jivaroan‐speaking communities who appeared to be related to each other and came likely from an early Arawak expansion in the western Amazonia of South America.