Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Machine Learning for Malware Detection over Time

The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more effective detectors that can block the majority of these malicious programs. However, these algorithms have not been developed to pursue the natural evolution of malware, and their performances significantly degrade over time because of such concept-drift. Currently, state-of-the-art techniques only focus on detecting the presence of such drift, or they address it by relying on frequent updates of models. Hence, there is a lack of knowledge regarding the cause of the concept drift, and ad-hoc solutions that can counter the passing of time are still under-investigated. In this work, we commence to address these issues as we propose (i) a drift-analysis framework to identify which characteristics of data are causing the drift, and (ii) SVM-CB, a time-aware classifier that leverages the drift-analysis information to slow down the performance drop. We highlight the efficacy of our contribution by comparing its degradation over time with a state-of-the-art classifier, and we show that SVM-CB better withstands the distribution changes that naturally characterize the malware domain. We conclude by discussing the limitations of our approach and how our contribution can be taken as a first step towards more time-resistant classifiers that not only tackle, but also understand the concept drift that affects data.
ELECTRON CLOUD MEASUREMENTS IN FERMILAB BOOSTER

Fermilab Booster synchrotron requires an intensity upgrade from 4.5x1012 to 6.5x1012 protons per pulse as a part of Fermilabs Proton Improvement Plan-II (PIP-II). One of the factors which may limit the high-intensity performance is the fast transverse instabilities caused by electron cloud effects. According to the experience in the Recycler, the electron cloud gradually builds up over multiple turns in the combined function magnets and can reach final intensities orders of magnitude greater than in a pure dipole. Since the Booster synchrotron also incorporates combined function magnets, it is essential to discover any existence of an electron cloud. And if it does, its effects on the PIP-II era Booster and its mitigating techniques. As the first step, the presence or absence of the electron cloud was investigated using the clearing bunch technique. This paper presents experimental details and observations of the bunch-by-bunch tune shifts of beams with various bunch train structures at low and high intensities and simulation results conducted using PyECLOUD.
Early Stage Sparse Retrieval with Entity Linking

Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval performance is restricted by semantic discrepancies and vocabulary gaps. On the other hand, transformer-based dense retrievers introduce significant improvements in information retrieval tasks by exploiting low-dimensional contextualized representations of the corpus. While dense retrievers are known for their relative effectiveness, they suffer from lower efficiency and lack of generalization issues, when compared to sparse retrievers. For a lightweight retrieval task, high computational resources and time consumption are major barriers encouraging the renunciation of dense models despite potential gains. In this work, we propose boosting the performance of sparse retrievers by expanding both the queries and the documents with linked entities in two formats for the entity names: 1) explicit and 2) hashed. We employ a zero-shot end-to-end dense entity linking system for entity recognition and disambiguation to augment the corpus. By leveraging the advanced entity linking methods, we believe that the effectiveness gap between sparse and dense retrievers can be narrowed. We conduct our experiments on the MS MARCO passage dataset. Since we are concerned with the early stage retrieval in cascaded ranking architectures of large information retrieval systems, we evaluate our results using recall@1000. Our approach is also capable of retrieving documents for query subsets judged to be particularly difficult in prior work. We further demonstrate that the non-expanded and the expanded runs with both explicit and hashed entities retrieve complementary results. Consequently, we adopt a run fusion approach to maximize the benefits of entity linking.
Extending GCC-PHAT using Shift Equivariant Neural Networks

Speaker localization using microphone arrays depends on accurate time delay estimation techniques. For decades, methods based on the generalized cross correlation with phase transform (GCC-PHAT) have been widely adopted for this purpose. Recently, the GCC-PHAT has also been used to provide input features to neural networks in order to remove the effects of noise and reverberation, but at the cost of losing theoretical guarantees in noise-free conditions. We propose a novel approach to extending the GCC-PHAT, where the received signals are filtered using a shift equivariant neural network that preserves the timing information contained in the signals. By extensive experiments we show that our model consistently reduces the error of the GCC-PHAT in adverse environments, with guarantees of exact time delay recovery in ideal conditions.
Freestanding LiPON: from Fundamental Study to Uniformly Dense Li Metal Deposition Under Zero External Pressure

Lithium phosphorus oxynitride (LiPON) is a well-known amorphous thin film solid electrolyte that has been extensively studied in the last three decades. Despite the promises to pair with Li metal anode and various cathode materials, the presence of rigid substrate and LiPONs unique amorphous, air-sensitive nature set limitations to comprehensively understand its intrinsic properties for future development and applications. This work demonstrates a methodology to synthesize LiPON in a freestanding form that exhibits remarkable flexibility and a Young s modulus of ~33 GPa. Solid-state nuclear magnetic resonance (ss-NMR) and differential scanning calorimetry (DSC) results with unprecedented high signal-to-noise ratio could be obtained with such freestanding LiPON (FS-LiPON), revealing the Li-LiPON interface bonding environments quantitatively and a well-defined glass transition temperature for LiPON. Combining interfacial stress and a seeding layer, FS-LiPON demonstrates a uniform and fully dense Li metal deposition without the aid of external pressure. Such a FS-LiPON film offers new opportunities for fundamental study of LiPON material and associated interfaces, and provides perspectives for interface engineering in bulk solid-state battery.
A highly accurate positivity-preserving thermally-relaxed Eulerian two-phase approach for compressible flows with a liquid and gases

A positivity-preserving fractional algorithm is presented for solving the four-equation homogeneous relaxation model (HRM) with an arbitrary number of ideal gases and a liquid governed by the stiffened gas equation of state. The fractional algorithm consists of a time step of the hyperbolic five-equation model by Allaire et al. and an algebraic numerical thermal relaxation step at an infinite relaxation rate. Interpolation and flux limiters are proposed for the use of high-order Cartesian finite difference or finite volume schemes in a general form such that the positivity of the partial densities and squared sound speed, as well as the boundedness of the volume fractions and mass fractions, are preserved with the algorithm. A conservative solution update for the four-equation HRM is also guaranteed by the algorithm which is advantageous for certain applications such as those with phase transition. The accuracy and robustness of the algorithm with a high-order explicit finite difference weighted compact nonlinear scheme (WCNS) using the incrementalstencil weighted essentially non-oscillatory (WENO) interpolation, are demonstrated with various numerical tests.
A continued fraction for

Let (a) r = (1 − a)(1 − ax) · · · (1 − ax r−1), (x) r = (1 − x)(1 − x 2) · · · (1 − x r), and r s = (x) r (x) s (x) r−s. We will show that (1) 1 + a + b + cx − a 1 + a + bx+ · · · cx n − a 1 + a + bx n = n+1 r=0 x 1 2 (r 2 −r) min(r,n+1−r) s=0 b r−s c s x 1 2 (s 2 +s) r s n+1−r−s t=0 a t r + t t n + 1 − s − t r , Q n (a, b, c, x) = n r=0 x 1 2 (r 2 +r) min(r,n−r) s=0 b r−s c s x 1 2 (s 2 +s) r s n−r−s t=0 a t r + t t n − s − t r. If we take |x| < 1, |a| < 1, let n → ∞, and make use of the well-known identities ∞ s=0 b r−s c s r s = (b + cx)(b + cx 2) · · · (b + cx r) and ∞ t=0 a t r + t r = 1 (a) r+1 , we obtain (2) 1 + a + b + cx − a 1 + a + bx+ cx 2 − a 1 + a + bx 2 +
The genus distribution of cubic graphs and asymptotic number of rooted cubic maps with high genus

Let $C_{n,g}$ be the number of rooted cubic maps with $2n$ vertices on the orientable surface of genus $g$. We show that the sequence $(C_{n,g}:g\ge 0)$ is asymptotically normal with mean and variance asymptotic to $(1/2)(n-\ln n)$ and $(1/4)\ln n$, respectively. We derive an asymptotic expression for $C_{n,g}$ when $(n-2g)/\ln n$ lies in any closed subinterval of $(0,2)$. Using rotation systems and Bender's theorem about generating functions with fast-growing coefficients, we derive simple asymptotic expressions for the numbers of rooted regular maps, disregarding the genus. In particular, we show that the number of rooted cubic maps with $2n$ vertices, disregarding the genus, is asymptotic to $\frac{3}{\pi}\,n! 6^n$.
Overleaf Example

Parameter tuning for robotic systems is a timeconsuming and challenging task that often relies on domain expertise of the human operator. Moreover, existing learning methods are not well suited for parameter tuning for many reasons including: the absence of a clear numerical metric for ‘good robotic behavior’; limited data due to the reliance on real-world experimental data; and the large search space of parameter combinations. In this work, we present an open-source MATLAB Preference Optimization and Learning Algorithms for Robotics toolbox (POLAR) for systematically exploring high-dimensional parameter spaces using human-inthe-loop preference-based learning. This aim of this toolbox is to systematically and efficiently accomplish one of two objectives: 1) to optimize robotic behaviors for human operator preference; 2) to learn the operator’s underlying preference landscape to better understand the relationship between adjustable parameters and operator preference. The POLAR toolbox achieves these objectives using only subjective feedback mechanisms (pairwise preferences, coactive feedback, and ordinal labels) to infer a Bayesian posterior over the underlying reward function dictating the user’s preferences. We demonstrate the performance of the toolbox in simulation and present various applications of human-in-the-loop preference-based learning.
Motif-based Graph Representation Learning with Application to Chemical Molecules

This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.
Multi Purpose Routing: New Perspectives and Approximation Algorithms

The cost due to delay in services may be intrinsically different for various applications of vehicle routing such as medical emergencies, logistical operations, and ride-sharing. We study a fundamental generalization of the Traveling Salesman Problem, namely $L_p$ TSP, where the objective is to minimize an aggregated measure of the delay in services, quantified by the Minkowski $p$-norm of the delay vector. We present efficient combinatorial and Linear Programming algorithms for approximating $L_p$ TSP on general metrics. We provide several approximation algorithms for the $L_p$ TSP problem, including $4.27$&$10.92$-approximation algorithms for single&multi vehicle $L_2$ TSP, called the Traveling Firefighter Problem. Among other contributions, we provide an $8$-approximation and a $1.78$ inapproximability for All-Norm TSP problem, addressing scenarios where one does not know the ideal cost function, or is seeking simultaneous approximation with respect to any cost function.
Robotic Untangling of Herbs and Salads with Parallel Grippers

The picking of one or more objects from an unsorted pile continues to be non-trivial for robotic systems. This is especially so when the pile consists of a granular material (GM) containing individual items that tangle with one another, causing more to be picked out than desired. One of the key features of such tangle-prone GMs is the presence of protrusions extending out from the main body of items in the pile. This work characterises the role the latter play in causing mechanical entanglement and their impact on picking consistency. It reports experiments in which picking GMs with different protrusion lengths (PLs) results in up to 76% increase in picked mass variance, suggesting PL to be an informative feature in the design of picking strategies. Moreover, to counter this effect, it proposes a new spread-and-pick (SnP) approach that significantly reduces tangling, making picking more consistent. Compared to prior approaches that seek to pick from a tangle-free point in the pile, the proposed method results in a decrease in picking error (PE) of up to 51%, and shows good generalisation to previously unseen GMs.
Autonomous screening for Diabetic Macular Edema using deep learning processing of retinal images

Background: Diabetic Macular Edema (DME) is a complication of diabetes which, when untreated, leads to vision loss. Screening for DME is recommended for diabetic patients every 1-2 years, however compliance rates are low. Though there is currently no high-efficacy camera-agnostic system for DME detection, an AI system may improve compliance. Methods: A deep learning model was trained for DME detection using the EyePacs dataset. Data was randomly assigned, by participant, into development (n= 14,246) and validation (n= 1,583) sets. Analysis was conducted at the single image, eye, and patient levels. Model performance was evaluated using sensitivity, specificity, and AUC. Findings: At the patient level, sensitivity of 0.901 (CI 95% 0.879-0.917), specificity of 0.900 (CI 95% 0.883-0.911), and AUC of 0.962 (CI 95% 0.955-0.968) were achieved. At the image level, sensitivity of 0.889 (CI 95% 0.878-0.900), specificity of 0.889 (CI 95% 0.877-0.900), and AUC of 0.954 (CI 95% 0.949-0.959) were achieved. At the eye level, sensitivity of 0.905 (CI 95% 0.890- 0.920), specificity of 0.902 (CI 95% 0.890-0.913), and AUC of 0.964 (CI 95% 0.958-0.969) were achieved. Interpretation: DME can be detected from color fundus imaging with high performance on all analysis metrics. Automatic DME detection may simplify screening, leading to more comprehensive screening for diabetic patients. Further prospective studies are necessary.
Non-conducting functions of ion channels: The case of integrin-ion channel complexes

ABSTRACT Started as an academic curiosity more than two decades ago, the idea that ion channels can regulate cellular processes in ways that do not depend on their conducting properties (non-ionic functions) gained traction and is now a flourishing area of research. Channels can regulate physiological processes including actin cytoskeletal remodeling, cell motility, excitation-contraction coupling, non-associative learning and embryogenesis, just to mention some, through non-ionic functions. When defective, non-ionic functions can give rise to channelopathies involved in cancer, neurodegenerative disease and brain trauma. Ion channels exert their non-ionic functions through a variety of mechanisms that range from physical coupling with other proteins, to possessing enzymatic activity, to assembling with signaling molecules. In this article, we take stock of the field and review recent findings. The concept that emerges, is that one of the most common ways through which channels acquire non-ionic attributes, is by assembling with integrins. These integrin-channel complexes exhibit broad genotypic and phenotypic heterogeneity and reveal a pleiotropic nature, as they appear to be capable of influencing both physiological and pathological processes.
Resistance of a ship undergoing oscillatory motion

In this paper, we describe extensions to the research of Doctors, Day and Clelland (2008) and Day, Clelland and Doctors (2008), in which the oscillations in the wave resistance during the constant-velocity phase of a towing-tank resistance test on a ship model were measured and predicted, in the cases of relatively deep and relatively shallow water. In the current study, the ship model was towed with a harmonic velocity component superimposed on the usual constant forward velocity. This work constitutes a ¯rst step in the understanding of the unsteady hydrodynamics of a racing shell (rowing boat). We show here that the unsteady wave resistance varies considerably from the traditional (steady) average value. Indeed, the wave resistance is frequently negative during part of the oscillatory cycle. However, the general effect is an increase in the temporal mean value of the wave resistance; this suggests that every effort should be made to reduce the unsteadiness of the motion. We also demonstrate that the unsteady wave-resistance theory provides an excellent prediction of the measured effects summarized here. These predictions are often within a few percent of the measured values of the resistance.
Analysis of Changes in High-order Aberration and Contrast Sensitivity After Epiblepharon Surgery

Purpose: To investigate changes in corneal anterior high-order aberration (HOA) and contrast sensitivity (CS) before and after epiblepharon surgery. Methods: A retrospective observational analysis of the degree of corneal erosion, HOAs and CS was conducted in the OD and OS, respectively, before and after epiblepharon surgery. The correlations between corneal erosion, HOAs, and CS were analyzed. Results: Forty-nine patients were included in the study. Among the anterior HOAs, total HOA, coma, and trefoil showed significant improvement after surgery (P = 0.003, P = 0.009, and P = 0.018, respectively). In the CS test, there was a significant improvement in CS after surgery at 1.1 cycles per degree (cpd) under photopic conditions, regardless of glare. Preoperative correlation analysis between HOAs and corneal erosion showed a significant positive correlation with total HOA (P = 0.001) and coma (P = 0.001). Preoperative correlation analysis between CS and corneal erosion showed a significant negative correlation at 1.1 cpd with glare under photopic conditions (P = 0.049). A negative correlation was also observed between CS under mesopic and photopic conditions and total HOA both before and after surgery. Conclusion: Significant improvement in corneal anterior HOAs and CS at 1.1 cpd under photopic conditions was observed after epiblepharon surgery. Total HOA of anterior cornea showed a negative correlation with CS. A decrease in HOAs and recovery of corneal erosion after epiblepharon surgery will help improve CS. Total high-order aberration, coma, and contrast sensitivity at 1.1 cpd under photopic condition with glare can reflect the state of corneal erosion. High-order aberration and contrast sensitivity test can help to evaluate visual function in epiblepharon.
The 9C Survey: a deeper source count at 15 GHz

Foreground radio sources are a major contaminant for centimetre-wave cosmic microwave background (CMB) measurements and the 9C survey was set up as part of the observing strategy of the CMB telescope, the Very Small Array. Prior to this survey with the Ryle Telescope at 15 GHz there was no comparable high-frequency radio survey of any extent. Our first published source count reached a limit of ~25mJy but we have now surveyed some areas more deeply, to a completenesss limit of ~5mJy. We present the results from this deeper survey.
Muscle degeneration in chronic massive rotator cuff tears of the shoulder: Addressing the real problem using a graphene matrix

Significance Currently, patients with rotator cuff tears associated with fat accumulation and muscle atrophy suffer from a high retear rate after rotator cuff repair. The development of new treatments to reverse these degenerative changes may significantly reduce the rate of retear and obviate the need for ultimate shoulder replacement surgery. This paper describes the effects of a GnP matrix that enhances muscle regeneration and reduces fat formation in vitro. This paper also shows the efficacy of the implantation of the GnP matrix in reducing fat accumulation, muscle atrophy, and fibrosis formation following chronic MRCTs in a rat model. This study showed the potential of the GnP matrix to be used in a clinical setting to reverse muscle degenerative changes following MRCTs.
Tissue plasminogen activator in left ventricular assist device-related intravascular hemolysis after failed augmented anticoagulation

Objectives: We sought to examine the efficacy and safety of adding fibrinogen-guided low-dose multi-day Alteplase™ tissue plasminogen activator (tPA) in the management of intravascular hemolysis (IVH) in patients with the HeartMate II (HM-II) continuous flow (CF) left ventricular assist device (LVAD) who failed to achieve IVH resolution with conventional augmented anticoagulation (AAC). Background: IVH in patients with LVAD is often treated with AAC, failing which pump exchange is considered. We hypothesized that a trial of low-dose tPA after failed AAC therapy could resolve IVH and prevent pump exchange in some patients. Methods: We performed a retrospective study of 31 HM-II CF LVAD patients admitted to our center from January 2015 to January 2020 for IVH management who received tPA following failed AAC. Primary 6-month outcomes included successful IVH resolution, unsuccessful IVH resolution requiring pump exchange, gastrointestinal bleeding, ischemic and hemorrhagic cerebrovascular accident (CVA), and death. Results: Thirty-one patients with IVH were treated with tPA following failed AAC. Successful resolution of IVH occurred in 22/31 (71%) patients. Pump exchange occurred in 9/31 (29%) patients. Gastrointestinal bleeding occurred in 7/31 (22.6%) patients. Ischemic CVA occurred in 6/31 (19.4%) patients. Conclusions: Management of IVH with administration of low-dose tPA after failed AAC is feasible and may prevent pump exchange in some patients.