Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Single-Index Models in Gaussian Space

We consider regression problems where the response is a smooth but non-linear function of a kdimensional projection of p normally-distributed covariates, contaminated with additive Gaussian noise. The goal is to recover the range of the k-dimensional projection, i.e., the index space. This model is called the multi-index model, and the k = 1 case is called the single-index model. For the single-index model, we characterize the population landscape of a natural semi-parametric maximum likelihood objective in terms of the link function and prove that it has no spurious local minima. We also propose and analyze an efficient iterative procedure that recovers the index space up to error using a sample size Õ(p 2/μ)+p/ ), whereR and μ, respectively, parameterize the smoothness of the link function and the signal strength. When a multi-index model is incorrectly specified as a single-index model, we prove that essentially the same procedure, with sample size Õ(p /μ) + p/ ), returns a vector that is -close to being completely in the index space.
Von Willebrand disease: Clinical conundrums

In this short review, current clinical conundrums on von Willebrand disease (VWD) will be addressed. Despite more knowledge on the pathophysiology of VWD, improvement of diagnostic strategies and advances in clinical care for patients with VWD still several problems and dilemmas remain. In this review, the latest findings regarding the pathology, including controversial borders for definition of the disease, diagnostic dilemma when von Willebrand factor (VWF) levels evolve with age, laboratory issues rendering difficult the classification of the different types of VWD and the current principles of care, including prophylaxis, related to these issues will be addressed. In addition, the importance of the loss of high molecular weight (HMW) VWF multimers in a subset of patients with congenital or acquired von VWD, which can be associated with severe bleeding related to vascular malformations in the gastrointestinal tract or other tissues will be reviewed. Also, the therapeutic options that can be proposed for these severe and difficult to control bleeding episodes in patients with VWD will be discussed.
Comparative analysis of silage fermentation and in vitro digestibility of tropical grass prepared with Acremonium and Tricoderma species producing cellulases

Objective To find out ways of improving fermentation quality of silage, the comparative analysis of fermentation characteristics and in vitro digestibility of tropical grasses silage applied with cellulases produced from Acremonium or Tricoderma species were studied in Thailand. Methods Fresh and wilted Guinea grass and Napier grass silages were prepared with cellulases from Acremonium (AC) or Trichoderma (TC) at 0.0025%, 0.005%, and 0.01% on a fresh matter (FM), and their fermentation quality, chemical composition and in vitro digestibility were analyzed. Results All silages of fresh Napier grass were good quality with lower pH, butyric acid, and ammonia nitrogen, but higher lactic acid content than wilted Napier grass and Guinea grass silage. Silages treated with AC 0.01% had the best result in terms of fermentation quality. They also had higher in vitro dry matter digestibility and in vitro organic matter digestibility at 6 and 48 h after incubation than other silages. Silages treated with lower levels at 0.005% or 0.0025% of AC and all levels of TC did not improve silage fermentation. Conclusion The AC could improve silage fermentation and in vitro degradation of Guinea grass and Napier grass silages, and the suitable addition ration is 0.01% (73.5 U) of FM for tropical silage preparation.
Thalamocortical Connections and Executive Function in Pediatric Temporal and Frontal Lobe Epilepsy

BACKGROUND AND PURPOSE: Largely accepted in the literature is the role the interconnections between the thalamus and cortex play in generalized epilepsy. However, thalamocortical involvement is less understood in focal epilepsy in terms of the effect of seizures on thalamocortical circuitry in the developing brain and subsequent cognitive outcome. We investigated thalamocortical pathway microstructure in pediatric frontal lobe epilepsy and temporal lobe epilepsy and examined the associations between pathway microstructure and measures of executive function. MATERIALS AND METHODS: We examined thalamocortical connections in 24 children with frontal lobe epilepsy, 17 patients with temporal lobe epilepsy, and 25 healthy children using DTI. We investigated several executive function measures in patients and controls, which were distilled into latent executive function components to compare among groups, and the associations between measures of thalamocortical microstructure and executive function. RESULTS: We found no differences in thalamocortical pathway microstructure between the groups, but aspects of executive function (mental flexibility/inhibition/shifting) were impaired in the frontal lobe epilepsy group compared with controls. In patients with frontal lobe epilepsy, younger age at seizure onset and a greater number of antiepileptic drugs were associated with DTI indices indicative of damaged/less developed thalamocortical pathways. In patients with temporal lobe epilepsy, poorer performance on all measures of executive function was associated with DTI indices reflective of damaged/less developed pathways. CONCLUSIONS: Our results give insight into vulnerable neural networks in pediatric focal epilepsy and suggest thalamocortical pathway damage as a potential mechanism of executive function impairment in temporal lobe epilepsy but not frontal lobe epilepsy. Identifying structure-function relations can help inform how we measure functional and cognitive/behavioral outcomes in these populations.
Advances on a null-space-based approach to range-only underwater steering and positioning

This paper addresses the problem of optimal AUV (Autonomous Underwater Vehicle) motion generation to maximise the range-related information available for vehicle positioning subject to the condition that the overall trajectory will drive the vehicle from an initial to a final target point. To this end we resort to a task prioritisation technique using a null-space-based projection approach. This allows decoupling the motion objectives from those of maximising an observability based criterion. Preliminary results on this topic have been presented in previous work. Here, a careful singularity analysis of the null space projector operator is formalized and discussed. A saturation management algorithm that copes with vehicle velocity command saturation by properly assigning the velocity vector to the two tasks is also proposed. The stability of the resulting control law is analysed using a Lyapunov-based approach. Robustness against uncertainty in the initial position of the vehicle is also handled by comparing two alternative procedures: the first aims at optimising the worst case scenario, that is, maximising the minimum of the observability metric inside the uncertainty region; the second, a novel approach, aims at optimising the most frequent observability metric inside the uncertainty region. The work is completed by combining the guidance algorithm with an observer for the estimation of the vehicle's position and its associated uncertainty. Results of numerical simulations show the effectiveness of the proposed strategy.
First-order Meta-Learned Initialization for Faster Adaptation in Deep Reinforcement Learning

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. Recent works have proposed training a meta-learner on a distribution of similar tasks, in the hope of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. The hypothesis is that is should be possible to learn something about the optimization process while learning on a speciﬁc task that generalizes across tasks. However, recent meta-learning approaches are hand-designed, either using architectures specialized to a particular application, or hard-coding parts of the meta-learner to solve the meta-optimization in a speciﬁc manner. While there do exist model-agnostic meta-learning methods, they often contain gradient-of-gradient (second order derivative) terms in the parameter update rules. We characterize the effect of ﬁrst-order approximations on recently proposed meta-learning algorithms which use the gradient updates of the learner itself. We also evaluate these ﬁrst-order methods (which have hitherto only been tested in supervised settings) for reinforcement learning problems. We show that these methods perform just as well as their second-order counterparts while being 1 . 5 × faster on average.
High-performance NTT architecture for large integer multiplication

This paper presents an efficient architecture of number Theoretical transform (NTT), targeting at fulfilling large integer multiplication for fully homomorphic encryption applications. A systematic memory management scheme is proposed for the pipelined shared-memory NTT architecture implemented with mixed-radix multi-path delay commutators (MDCs). The presented data relocation scheme along with the MDC can be applied to merge multiple banks with single-port memory for further reducing the area requirement. Experimental results show that a 1,179,648-bit multiplier implemented by the proposed solution, including seamless data transfer among the building blocks, can lead to more than 39.8% area reduction with even a lower computational time as compared with the related works.
Hidden pursuits: evaluating gaze-selection via pursuits when the stimuli's trajectory is partially hidden

The idea behind gaze interaction using Pursuits is to leverage the human's smooth pursuit eye movements performed when following moving targets. However, humans can also anticipate where a moving target would reappear if it temporarily hides from their view. In this work, we investigate how well users can select targets using Pursuits in cases where the target's trajectory is partially invisible (HiddenPursuits): e.g., can users select a moving target that temporarily hides behind another object? Although HiddenPursuits was not studied in the context of interaction before, understanding how well users can perform HiddenPursuits presents numerous opportunities, particularly for small interfaces where a target's trajectory can cover area outside of the screen. We found that users can still select targets quickly via Pursuits even if their trajectory is up to 50% hidden, and at the expense of longer selection times when the hidden portion is larger. We discuss how gaze-based interfaces can leverage HiddenPursuits for an improved user experience.
Low-C ESD Protection Design With Dual Resistor-Triggered SCRs in CMOS Technology

The electrostatic discharge (ESD) protection design with low parasitic capacitance seen at I/O pad is needed for high-frequency applications. Conventional ESD protection designs with dual diodes or dual stacked diodes have been used for gigahertz applications. To further reduce the parasitic capacitance, the ESD protection design by using complementary resistor-triggered silicon-controlled rectifiers (RTSCRs) is proposed in this paper. The proposed design includes a P-type RTSCR between I/O and <inline-formula> <tex-math notation="LaTeX">${{V}}_{{DD}}$ </tex-math></inline-formula>, an N-type RTSCR between I/O and <inline-formula> <tex-math notation="LaTeX">${{V}}_{{SS}}$ </tex-math></inline-formula>, and a power clamp circuit between <inline-formula> <tex-math notation="LaTeX">${{V}}_{{DD}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${{V}}_{{SS}}$ </tex-math></inline-formula> to achieve whole-chip ESD protection. Verified in silicon chip, the RTSCRs have the advantages, such as the sufficient low clamping voltage and lower parasitic capacitance, as compared with the conventional ESD protection designs. Therefore, the proposed design is suitable for low-C ESD protection in CMOS technology.
A British Society for haematology good practice paper on the diagnosis and investigation of patients with mantle cell lymphoma

Pamela McKay, Mike Leach, Bob Jackson, Stephen Robinson and Simon Rule Department of Haematology, Beatson West of Scotland Cancer Centre, Gartnavel Hospital, Department of Pathology, Queen Elizabeth University Hospital, Glasgow, Department of Haematology, University Hospitals Bristol, Bristol, and Department of Haematology, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
Metabolic vulnerability of cisplatin‐resistant cancers

Cisplatin is the most widely used chemotherapeutic agent, and resistance of neoplastic cells against this cytoxicant poses a major problem in clinical oncology. Here, we explored potential metabolic vulnerabilities of cisplatin‐resistant non‐small human cell lung cancer and ovarian cancer cell lines. Cisplatin‐resistant clones were more sensitive to killing by nutrient deprivation in vitro and in vivo than their parental cisplatin‐sensitive controls. The susceptibility of cisplatin‐resistant cells to starvation could be explained by a particularly strong dependence on glutamine. Glutamine depletion was sufficient to restore cisplatin responses of initially cisplatin‐resistant clones, and glutamine supplementation rescued cisplatin‐resistant clones from starvation‐induced death. Mass spectrometric metabolomics and specific interventions on glutamine metabolism revealed that, in cisplatin‐resistant cells, glutamine is mostly required for nucleotide biosynthesis rather than for anaplerotic, bioenergetic or redox reactions. As a result, cisplatin‐resistant cancers became exquisitely sensitive to treatment with antimetabolites that target nucleoside metabolism.
Controllable Variation Synthesis for Surface Motion Capture

We address the problem of generating variations of captured 4D models automatically, and we particularly focus on dynamic human shapes as observed from multi-view videos. Variation is an essential component of motion realism, however recent mesh animation datasets and tools lack such richness. Given a few 4D models representing movements of the same type, our method builds a probabilistic low dimensional embedding of shape poses using Gaussian Process Dynamical Models, and novel variants of motions are obtained by sampling trajectories from this manifold using Monte Carlo Markov Chain. We can synthesise an unlimited number of variations of any of the input movements, and also any blended version of them, without costly non-linear interpolation of input movement variations in mesh domain. The output variations are statistically similar to the input movements but yet slightly different in poses and timings. As we show through our results, the generated mesh sequences match the training examples in realism, which facilitates 4D model dataset augmentation.
Robust Sampling Based Model Predictive Control with Sparse Objective Information

—We present an algorithmic framework for stochastic model predictive control that is able to optimize non-linear systems with cost functions that have sparse, discontinuous gradient information. The proposed framework combines the beneﬁts of sampling-based model predictive control with linearization-based trajectory optimization methods. The resulting algorithm consists of a novel utilization of Tube-based model predictive control. We demonstrate robust algorithmic performance on a variety of simulated tasks
Modeling Neurological Diseases With Human Brain Organoids

The complexity and delicacy of human brain make it challenging to recapitulate its development, function and disorders. Brain organoids derived from human pluripotent stem cells (PSCs) provide a new tool to model both normal and pathological human brain, and greatly enhance our ability to study brain biology and diseases. Currently, human brain organoids are increasingly used in modeling neurological disorders and relative therapeutic discovery. This review article focuses on recent advances in human brain organoid system and its application in disease modeling. It also discusses the limitations and future perspective of human brain organoids in modeling neurological diseases.
The Extremal Function and Colin de Verdière Graph Parameter

We study the maximum number of edges in an $n$ vertex graph with Colin de Verdiere parameter no more than $t$. We conjecture that for every integer $t$, if $G$ is a graph with at least $t$ vertices and Colin de Verdiere parameter at most $t$, then $|E(G)| \leq t|V(G)|-\binom{t+1}{2}$. We observe a relation to the graph complement conjecture for the Colin de Verdiere parameter and prove the conjectured edge upper bound for graphs $G$ such that either $\mu(G) \leq 7$, or $\mu(G) \geq |V(G)|-6$, or the complement of $G$ is chordal, or $G$ is chordal.
A type 2 adaptive fuzzy inferencing system

Type 2 fuzzy sets allow for linguistic grades of membership and, therefore, present a better representation of the 'fuzziness', when applied to a particular problem, than type 1 fuzzy sets. However, the associated cost is that the fuzzy membership grades and rules have somehow to be determined and no recognised approach yet exists. For type 1 systems a number of approaches have been adopted. One in particular is the adaptive network based fuzzy inferencing system (ANFIS) which has successfully been applied to a variety of applications. ANFIS takes domain data and learns the membership functions and rules for a type 1 fuzzy inferencing system. Our work aims to extend this approach for type 2 systems. Our Type 2 adaptive fuzzy inferencing system has inputs that are linguistic variables and the membership functions for these fuzzy grades are learnt from the relationship between these inputs and the given output. The paper describes the algorithm developed highlighting the theoretical and computational issues involved.
A Power Reconfigurable High-Efficiency  $X$ -Band Power Amplifier MMIC Using the Load Modulated Balanced Amplifier Technique

The load modulated balanced amplifier (LMBA) technique uses a control signal, injected at the output coupler, to modulate the impedance of the balanced amplifier transistors. A 14.1-W X-band LMBA is reported, integrating for the first time the balanced, driver, and control signal amplifiers in a single microwave monolithic integrated circuit. Load modulation and bias settings are used to demonstrate that high circuit efficiency can be achieved as the LMBA is adjusted for operation in three RF-power regimes; 1.5, 5.6, and 14.1 W for a constant input power of 22 dBm. Power-added efficiencies above 37% are observed in all power regimes from 8 to 9 GHz under saturated conditions.
RAPD analysis for genetic diversity of germplasm resources of Strobilanthes

The 18 samples representing 18 populations of Strobilanthes cusia (Nees) O. Ktze in Fujian Province of China were analyzed with RAPD markers. Eleven primers were used, a total of 106 bands were scored and 88 of them were polymorphic. The percentage of polymorphic loci was 77.36%, Nei’s gene diversity was 0.2420, and Shannon’s index was 0.3700. The 18 populations were classified into 2 groups based on the RAPD data by the method of hierarchical cluster analysis. Most of the populations from Fujian were clustered into a group, other populations were clustered into the other group. There was a high level of genetic diversity among the populations, and the genetic differentiation was obvious among the populations from Fujian.
Evaluation of the vascular anatomy of the left‐sided colon focused on the accessory middle colic artery: a single‐centre study of 734 patients

Surgery for colorectal cancer located in the splenic flexure is difficult to perform because of the complex anatomy. Recently, in addition to the middle colic artery and left colic artery (LCA), the accessory middle colic artery (AMCA) has been recognized as a feeding artery for the left‐sided colon. This study aimed to evaluate the vascular anatomy of the splenic flexure focusing on the AMCA in a large number of patients.
Design Exploration of IoT centric Neural Inference Accelerators

Neural networks have been successfully deployed in a variety of fields like computer vision, natural language processing, pattern recognition, etc. However most of their current deployments are suitable for cloud-based high-performance computing systems. As the computation of neural networks is not suited to traditional Von-Neumann CPU architectures, many novel hardware accelerator designs have been proposed in literature. In this paper we present the design of a novel, simplified and extensible neural inference engine for IoT systems. We present a detailed analysis on the impact of various design choices like technology node, computation block size, etc on overall performance of the neural inference engine. The paper demonstrates the first design instance of a power-optimized ELM neural network using ReLU activation. Comparison between learning performance of simulated hardware against the software model of the neural network shows a variation of ~ 1% in testing accuracy due to quantization. The accelerator compute blocks manage to achieve a performance per Watt of ~ 290 MSPS/W (Million samples per second per Watt) with a network structure of size: 8 x 32 x 2. Minimum energy of 40 pJ is acheived per sample processed for a block size of 16. Further, we show through simulations that an added power-saving of ~ 30 % can be acheived if SRAM based main memory is replaced with emerging STT-MRAM technology.
An environment recognition method for MAVs using a smartphone

In the field of micro-aerial-vehicles, Positioning and navigation depends on the environment where they are located in. Inherently different techniques and different sensors combination are used in different environments for MAVs. MAVs often go through different environments in a flight task, such as indoor and outdoor, which cause a problem that what navigation method MAVs should adopt and where the different navigation methods MAVs should change. In order to ensure the continuity and reliability of autonomous mission, MAVs must have ability to detect the environment. This paper aims to propose an environment recognition method for MAVs' adaptive navigation. MAVs have multiple navigation sensors, which are sensitive to environment, can provide environment facts to MAVs. However, the compute capability of MAVs' hardware has been occupied by flight control. In order to achieve the requirement of adaptive navigation and ensure the safety of experiment, a two-level environment recognition method for MAVs using a smartphone has been proposed. Some sensors on smartphone are also sensitive to environment, such as position sensor (GPS), environment sensors (magnetometers, the barometer, the light intensity sensor) and image sensor (camera), which can be used for environment features extraction. Sensors' measurements are preprocessed and fed into deep learning framework to get classification results including indoor phase, intermediate phase and outdoor phase of the MAV flight. The first level of recognition system uses position sensor and environment sensors as input of the framework, which is constructed by the integration of convolutional neural networks (CNN) and recurrent neural networks (RNN). It gives a high update rate of the environment recognition result to MAVs. The second level uses image sensor as input of the framework, which is constructed by convolutional neural networks. The second level is triggered when the first level cannot give satisfied result. A MAV-Smartphone combined (MSC) platform and an android app have developed in order to collect data from multiple sensors in different environments during the MAV flight. Furthermore, the two-level recognition system has been validated stepwise. The effectiveness of the proposed environment recognition method has been demonstrated.