ML Complexes Research Articles

ECBoA-OFS: An Ensemble Classification Model for Botnet Attacks based on Optimal Feature Selection using CPR in IoT

The rapid growth of the Internet of Things (IoT) has indeed introduced new security challenges, and the proliferation of compromised IoT devices has become a significant concern. Botnet attacks, where multiple corrupted devices are managed by a particular object, have become a widespread threat in IoT environments. These are used for a variety of malicious activities, including distributed DDoS attacks, data breaches, and malware distribution. However, detecting IoT botnets poses several challenges due to the resource constraints inherent in many IoT devices. The limitations in computation, storage, and communication capabilities make it challenging to deploy complex ML and deep learning models directly on these devices. This paper proposes an ensemble classification model ECBoA-OFS (Ensemble Classification for Botnet Attack Prediction using Optimal Feature Selection). It focuses on enhancing the accuracy of botnet attack prediction through the integration of ensemble methods and optimal feature selection. It describes a method for optimal feature selection in the context of analyzing the behavior of BoA and malicious traffic flow features in a network using Central Pivot Ranges (CPR). Feature selection is an important step in machine learning and data analysis because it supports to identification of the most important features for a given problem, thereby improving model performance and interpretation. The extracted features are used for model training and ensemble classification for prediction. To evaluate ECBoA-OFS, the N-BaIoT-2021 dataset consisting of regular IoT network traffic and BoA traffic records of corrupted IoT devices is utilized, considering detection precision, sensitivity, specificity, accuracy, and F1-score. Although all ensemble classifier models achieved better detection accuracy through optimal feature selection, the proposed ECBA-OFS shows better results compared to other ensemble classifier results.

Influence of Sodium Lauryl Sulfate Anionic Micelles on the Complex Equilibria of Divalent Metal Ions with Isoleucine Amino Acid

The main objective of the present investigation is the formation analysis of metal-ligand complexes of isoleucine with divalent calcium, magnesium and zinc metal ions in various concentrations of sodium dodecyl sulphate (0.0, 0.5, 1.0, 1.5, 2.0 and 2.5 percent w/v). To look into the impact of the concentration of negatively charged micelles, the parameters were changed and the possible chemical species of isoleucine that interact with the metal ions, are being studied. The experiment was performed using pH meter at 303 K using NaCl to keep ionic strength stable at 0.16 mol dm-3 in the presence of anionic surfactant sodium dodecyl sulphate (SDS). The determination of the correction factor was conducted via the computer program SCPHD. The computational software MINIQUAD75 is utilized to determine the stability constants of ligand-metal complexes through the analysis of titration data. Based on statistical features such as skewness, 2, kurtosis and crystallographic R-factor, the best fit to the complicated speciation was selected. The metal ions Ca (II), Mg (II) and Zn (II) formed complexes ML, ML2 and ML2H2 for Ca (II), Mg(II) and ML, ML2 and ML2H for Zn (II) through the complexation of isoleucine. In order to validate the accuracy of the final established model, deliberate defects were introduced into the relevant components.

Influence of Sodium Dodecyl Sulfate Anionic Micelles on The Complex Equilibria of Divalent Metal Ions with L-Leucine Amino Acid

The main objectives of the present investigation are the formation analysis of metal-ligand complexes of L-Leucine with divalent calcium, magnesium, and zinc metal ions in various concentrations of sodium dodecyl sulphate (0.0, 0.5, 1.0, 1.5, 2.0, and 2.5 percent w/v). To also look into the impact of the concentration of negatively charged micelles, the parameters that were changed, and the possible chemical species of L-Leucine that interact with the metal ions that were being studied. The experiment was performed using pH meter at 303 K using NaCl to keep your ionic strength stable of 0.16mol dm-3 in the presence of anionic surfactant, sodium dodecyl sulphate (SDS). The determination of the correction factor was conducted via the computer program SCPHD. The computational software MINIQUAD75 is utilized to determine the stability constants of ligand-metal complexes through the analysis of titration data. Based on statistical features such as skewness, 2, kurtosis, and crystallographic R-factor, the best fit to the complicated speciation was selected. The metal ions Ca (II), Mg (II), and Zn (II) formed complexes ML, ML2, and ML2H2 for Ca (II), and ML, ML2, and ML2H for Mg (II) and Zn (II) through the complexation of L-Leucine. In order to validate the accuracy of the final established model, deliberate defects were introduced into the relevant components.

FSDC: Flow Samples and Dimensions Compression for Efficient Detection of DNS-over-HTTPS Tunnels

This paper proposes an innovative approach capitalized on the distinctive characteristics of command and control (C&C) beacons, namely, time intervals and frequency between consecutive unique connections, to compress the network flow dataset. While previous studies on the same matter used single technique, we propose a multi-technique approach for efficient detection of DoH tunnels. We use a baseline public dataset, CIRA-CIC-DoHBrw-2020, containing over a million network flow properties and statistical features of DoH, tunnels, benign DoH and normal browsing (HTTPS) traffic. Each sample is represented by 33 features with a timestamp. Our methodology combines star graph and bar plot visualizations with supervised and unsupervised learning techniques. The approach underscores the importance of C&C beacon characteristic features in compressing a dataset and reducing a flow dimension while enabling efficient detection of DoH tunnels. Through compression, the original dataset size and dimensions are reduced by approximately 95% and 94% respectively. For supervised learning, RF emerges as the top-performing algorithm, attaining precision and recall scores of 100% each, with speed increase of ≈6796 times faster in training and ≈55 in testing. For anomaly detection models, OCSVM emerges as the most suitable choice for this purpose, with precision (88.89) and recall (100). Star graph and bar graph models also show a clear difference between normal traffic and DoH tunnels. The reduction in flow sample size and dimension, while maintaining accuracy, holds promise for edge networks with constrained resources and aids security analysts in interpreting complex ML models to identify Indicators of Compromise (IoC).

Metal Complexes in Biology and Medicine: The System Cadmium (II)/ Iron (II)/ Zinc (II)- α- Aminobutyric Acid

Metal ions play an important role in biological systems. The importance of metal ions to essential functions of living systems and for wellbeing of living organisms is well known. Complexation reactions of α– aminobutyric acid with cadmium (II), iron (II) and zinc (II) have been studied in solution phase using paper ionophoretic technique. The present method is based upon the migration of a spot of metal ion on a paper strip at different pH values of background electrolyte containing 0.01M α -aminobutyric acid. A graph of pH against mobility was used to obtained information in the binary complexes and to calculate its stability constants. The logarithm stability constants of ML and ML2 complexes of α -aminobutyric acid were found to be (4.01 ± 0.02; 2.72 ± 0.01), (3.73 ± 0.03; 2.65 ± 0.09), (4.45 ± 0.02; 2.80 ± 0.03) for cadmium (II), iron (II) and zinc (II) complexes, respectively at ionic strength 0.1 molL-1 and a temperature of 35º C. Metal based drugs bioactivity can be increase by metal chelation, which in turn increase their absorbance and stability. Metal complexes can offer their action such as anti-inflammatory, anti-diabetic, antimicrobial, anticancer and ant thyroid compounds.

Metal Complexes in Biology and Medicine the System Cadmium (II) / Iron (II) /Zinc (II) - Proline

Metal ions are fundamental elements for the maintenance of the life span’ s of the humans, animals and plants. In coordination compound studies, a knowledge of the magnitude of the stability constants of complexes is necessary for preliminary quantitative treatment. Present work described method that involves the use of paper electrophoretic technique for the study of the equilibria in binary complex systems in solution. This method is based on the movement of a spot of metal ion in an electric field at various pHs of the background electrolyte. A plot of overall mobility of metal / complex ion versus pH was used to obtain information on the formation of binary complexes and to calculate their stability constants. The stability constant of the ML and ML2 complexes of cadmium (II) – proline,iron(II) – proline and zinc(II) – proline, have been found to be (4.61 ± 0.01; 3.04 ± 0.03), (4.21 ± 0.01; 2.89 ± 0.09) and (4.98 ± 0.02; 2.44 ± 0.05) (logarithm constant values), respectively at 35 ºC and ionic strength 0.1 mol L-1. The first and second stability constants follow the order zinc (II) >cadmium (II) > iron (II). Metal complexes can offer their action such as anti-inflammatory, antidiabetic, antimicrobial, anticancer and ant thyroid compounds. Metal based drugs bioactivity can be increase by metal chelation, which in turn increase their absorbance and stability.

Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

BackgroundMachine learning (ML) models are evaluated in a test set to estimate model performance after deployment. The design of the test set is therefore of importance because if the data distribution after deployment differs too much, the model performance decreases. At the same time, the data often contains undetected groups. For example, multiple assessments from one user may constitute a group, which is usually the case in mHealth scenarios.MethodsIn this work, we evaluate a model’s performance using several cross-validation train-test-split approaches, in some cases deliberately ignoring the groups. By sorting the groups (in our case: Users) by time, we additionally simulate a concept drift scenario for better external validity. For this evaluation, we use 7 longitudinal mHealth datasets, all containing Ecological Momentary Assessments (EMA). Further, we compared the model performance with baseline heuristics, questioning the essential utility of a complex ML model.ResultsHidden groups in the dataset leads to overestimation of ML performance after deployment. For prediction, a user’s last completed questionnaire is a reasonable heuristic for the next response, and potentially outperforms a complex ML model. Because we included 7 studies, low variance appears to be a more fundamental phenomenon of mHealth datasets.ConclusionsThe way mHealth-based data are generated by EMA leads to questions of user and assessment level and appropriate validation of ML models. Our analysis shows that further research needs to follow to obtain robust ML models. In addition, simple heuristics can be considered as an alternative for ML. Domain experts should be consulted to find potentially hidden groups in the data.

Late transition metal complexes of ferrocene‐containing nitrogen ligands: Coordination chemistry, electron transfer properties, and tumor cell viability

Bis(2‐picolyl)amine (bpa), iminodiacetamide (imda), and bis‐1,2,3‐triazole (bta) ferrocene ligands (L) with and without an aliphatic linker were prepared by multi‐step synthesis. The cis‐fac, trans‐fac, or mer stereochemistry of their ML2 complexes with Ni(II), Cu(II), Cd(II), and Zn(II) was studied in the solid state (infrared [IR] and single‐crystal X‐ray diffraction [SC‐XRD]), in solution (nuclear magnetic resonance [NMR] and cyclic voltammetry [CV]) and by density functional theory (DFT) calculations. Crystal structures were determined for bpa ligand 7, and complexes [Ni(1)2](NO3)2 (1Ni), [Cu(8)2]OTf2 (8Cu), [Ni(10b)2](NO3)2 (10bNi), and [Cu(10b)2]OTf2 (10bCu). The bond strength of the central metal ion to the ligand amine nitrogen atom was studied by NMR, electrochemistry, and DFT. The information on redox‐active centers, electron transfer properties of ferrocene ligands (L), and their in situ complexation with zinc(II) and nickel(II) ions were obtained by voltammetric analysis. In addition, DFT calculations showed that the electron ionization in ML2 complexes occurs from one of the ferrocene units, leaving the electronic structure of the other ligand intact, while some of the expelled electron density is recovered by the adjacent amine through resonance. This effect is more pronounced in the free ligands, because the eventual amine resonance in ML2 needs to balance its Zn(II) coordination participation, which justifies why they show higher ionization energies over free ligands. Moreover, due to lower steric hindrance, the N(amino)–Zn(II) coordination is additionally stronger in 1:1 ML complexes, which makes their electron depletion further more demanding. If compared with the clinical drug cisplatin, complexes of bpa 1Ni and imda 2Ni showed a better effect on the viability of different tumor cell lines and better selectivity towards normal cells. Treatment with 1Ni and 2Ni causes an increase of cells in the S phase of the cell cycle and leads to the accumulation of cells in G0/G1. A decrease in the expression level of anti‐apoptotic marker Bcl‐2 upon treatment with both compounds together with increased amount of Annexin V‐FITC positive cells implied apoptosis as the mode of cell death.

Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches.

Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. The model's performance was compared on a training-test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13-19) features.

Experimental and Theoretical Insight into the Ionic Liquid-Mediated Complexation of Trivalent Lanthanides with β-Diketone and Its Fluorinated Analogue.

A multitechnique approach with theoretical insights has been employed to understand the complexation of trivalent lanthanides with two β-diketones, viz. 1-phenyl-1,3-butanedione (L1) and 4,4,4-trifluoro-1-phenyl-1,3-butanedione (L2), in an ionic liquid (C6mim·NTf2). UV-vis spectral analysis of complexation using Nd3+ revealed the predominance of ML2+ and ML4- species. The stability constants for the PB complexes were higher (β2 ∼ 10.45 ± 0.05, β4 ∼ 15.51 ± 0.05) than those for the TPB (β2 ∼ 7.56 ± 0.05, β4 ∼ 13.19 ± 0.06). The photoluminescence titration using Eu3+ corroborated the same observations with slightly higher stability constants, probably due to the higher ionic potential of Eu3+. The more asymmetric (AL2ML4 ∼ 5.2) Eu-L2 complex was found to contain one water molecule in the primary coordination sphere of Eu3+ with more covalency of the Eu3+-O bond (Ω2L1 = 8.5 × 10-20, Ω4L1 = 1.3 × 10-20) compared to the less asymmetric Eu-L1 complex (AL1ML4 ∼ 3.5) with two water molecules having less Eu-O covalency (Judd-Offelt parameters: Ω2L1 = 7.3 × 10-20, Ω4L1 = 1.0 × 10-20). Liquid-liquid extraction studies involving Nd3+ and Eu3+ revealed the formation of the ML4- complex following an 'anion exchange' mechanism. The shift of the enol peak from 1176 to 1138 cm-1 on the complexation of the β-diketones with Eu3+ was confirmed from the FTIR spectra. 1H NMR titration of the β-diketones with La(NTf2)3 evidenced the participation of α-H of the β-diketones and protons at C2, C4, and C5 positions of the methylimidazolium ring. For the ML2 complex, 4 donor O atoms are suggested to coordinate to the trivalent lanthanides with bond distances of 2.3297-2.411 Å for La-O, 2.206-2.236 Å for Eu-O, and 2.217-2.268 Å for Nd-O, respectively, while for the ML4 complex, 8 donor O atoms were coordinated with bond lengths of 2.506-2.559 Å for La-O, 2.367-2.447 Å for Eu-O, and 2.408-2.476 Å for Nd-O. The Nd3+ ion was higher by 9.7 kcal·mol-1 than that of the La3+ ion for the 1:4 complex. The complexation energy with L1 was quite higher than that with L2 for both 1:2 and 1:4 complexes. Using cyclic voltammetry, the redox behavior of trivalent lanthanides Eu and Gd with β-diketonate in ionic liquid medium was probed and their redox energetic and kinetic parameters were determined.

An Interpretable Approach with Explainable AI for Heart Stroke Prediction.

Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model's comprehensibility for healthcare practitioners. This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. Applying these techniques, including model interpretability measures such as permutation importance and explainability methods like LIME, has achieved impressive results. While permutation importance provides insights into feature importance globally, LIME complements this by offering local and instance-specific explanations. Together, they contribute to a comprehensive understanding of the Artificial Neural Network (ANN) model. The combination of these techniques not only aids in understanding the features that drive overall model performance but also helps in interpreting and validating individual predictions. The ANN model has achieved an outstanding accuracy rate of 95%.

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces.

Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that are relevant to the model's decision. These explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by extracting at some intermediate layer of a neural network, subspaces that capture the multiple and distinct activation patterns (e.g. visual concepts) that are relevant to the prediction. To automatically extract these subspaces, we propose two new analyses, extending principles found in PCA or ICA to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), maximize relevance instead of e.g. variance or kurtosis. This allows for a much stronger focus of the analysis on what the ML model actually uses for predicting, ignoring activations or concepts to which the model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.

New Complexes of Isoniazid Derivative with 3d Metals: Synthesis, Structure and Molecular Docking

This article reports the results of our study aimed at synthesizing and describing the structure and properties of new transition metal complexes with potential anti-tuberculosis and other related activities. For the first time, five bis-complexes of the ligand N’-(1-(6-methylpyridine-2-yl)ethylidene)isonicotinohydrazide (LH) with ions of five 3d metals (Cu(II), Mn(II), Co(II), Ni(II), and Zn(II)) were synthesized. The structures of all synthesized complexes in crystalline form were identified by X-ray diffraction (XRD) analysis. The resulting compounds were characterized by NMR, mass spectrometry, and electron spectroscopy. DFT/PCM calculations were performed on the structures of the five ML(OH) hydroxocomplexes formed during the hydrolysis of ML2 complexes in an aqueous medium within the physiological pH range. The binding energies of the ML(OH) complexes with the biotarget, the InhA protein of the Mycobacterium tuberculosis strain, were determined by molecular docking. The data obtained suggest that the studied complexes have high biological activity and warrant further biomedical research.

The Rossby Normal Mode as a Physical Linkage in a Machine Learning Forecast Model for the SST and SSH of South China Sea Deep Basin

AbstractMachine learning (ML) has been widely applied in Earth sciences, but its black‐box nature still remains. Existing techniques for interpretable ML developed by deep learning researchers are not satisfactory in understanding physical problems. In this study, our objective is to establish the physical linkage between features extracted from ML model and results derived from physical equations that describe the problem. This approach aims to provide a more comprehensible way of understanding an ML model. We select a less complex ML forecasting model, consisting of traditional statistical algorithms, which effectively forecast sea surface temperature anomaly (SSTA) and sea level anomaly (SLA) of the South China Sea (SCS) in 30 days ahead. Here, we focus on the SLA prediction and detect the physical mechanism of model capability to predict SCS SLA. Previous study has identified Rossby normal modes as the physical mechanism for long‐lived eddies in SCS in previous study. We here demonstrate the relationship between the ML model and Rossby normal modes in SCS in terms of temporal variations and spatial distributions of water mass. The model's skill in predicting Rossby normal modes partially explains its skill in predicting SLA, thereby providing a more transparent and interpretable basis for forecasts in SCS.

Truthful meta-explanations for local interpretability of machine learning models

Automated Machine Learning-based systems’ integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical or high-risk applications. To address this issue, researchers and businesses have been focusing on finding ways to improve the explainability of complex ML systems, and several such methods have been developed. Indeed, there are so many developed techniques that it is difficult for practitioners to choose the best among them for their applications, even when using evaluation metrics. As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent. In this paper, we present a local meta-explanation technique which builds on top of the truthfulness metric, which is a faithfulness-based metric. We demonstrate the effectiveness of both the technique and the metric by concretely defining all the concepts and through experimentation.

The Homoleptic Curcumin-Copper Single Crystal (ML2): A Long Awaited Breakthrough in the Field of Curcumin Metal Complexes.

The first single crystal structure of the homoleptic copper (II) ML2 complex (M=Cu (II), L = curcumin) was obtained and its structure was elucidated by X-ray diffraction showing a square planar geometry, also confirmed by EPR. The supramolecular arrangement is supported by C-H···O interactions and the solvent (MeOH) plays an important role in stabilizing the crystal packing Crystallinity was additionally assessed by XRD patterns. The log P value of the complex (2.3 ± 0.15) was determined showing the improvement in water solubility. The cytotoxic activity of the complex against six cancer cell lines substantially surpasses that of curcumin itself, and it is particularly selective against leukemia (K562) and human glioblastoma (U251) cell lines, with similar antioxidant activity to BHT. This constitutes the first crystal structure of pristine curcumin complexed with a metal ion.

Implicatons of Metal Complexes in Biology and Medicine the System Cadmium (II)/ Iron (II)/ Zinc (II)-Hydroxyproline

The importance of metal ions to essential functions of living systems and for the well being of living organisms is known. Metal ions are fundamental elements for the maintenance of the life spans of the human, animals and plants. The stability constants of Cd2+, Fe2+ and Zn2+ complexes with hydroxyproline were determined by Paper Electrophoretic Technique (PET). This method is based on the movement of a spot of metal ion in an electric field at various pH of background electrolyte. A graph of pH against mobility gives information about the formation of binary complexes and permit to calculate their stability constants. The stability constant of the ML and ML2 complexes of Cd (II) – hydroxyproline, Fe (II) – hydroxyproline and Zn (II) – hydroxyproline, have been found to be (4.41 ± 0.01; 2.95 ± 0.06) (4.11 ± 0.01; 2.81 ± 0.11 and (4.83 ± 0.02; 3.28 ± 0.07) ( logarithm stability constant values), respectively at ionic strength 0.1 mole L-1 (per chloric acid as background electrolyte) and a temperature of 35 0C, The first and second stability constants of metal complexes follow the order Zn (II) > Cd (II) > Fe (II). Metal complexes can offer their action such as anti-inflammatory, antibiotic, anti-thyroid and anticancer compounds. Metal based drugs bioactivity can be increased by metal chelation, which in turn increase their absorbance and stability. Recent advances in inorganic chemistry have made possible formation of a number of metal complexes with organic ligands of interest which can be use as therapeutic agents.

StrategyAtlas: Strategy Analysis for Machine Learning Interpretability.

Businesses in high-risk environments have been reluctant to adopt modern machine learning approaches due to their complex and uninterpretable nature. Most current solutions provide local, instance-level explanations, but this is insufficient for understanding the model as a whole. In this work, we show that strategy clusters (i.e., groups of data instances that are treated distinctly by the model) can be used to understand the global behavior of a complex ML model. To support effective exploration and understanding of these clusters, we introduce StrategyAtlas, a system designed to analyze and explain model strategies. Furthermore, it supports multiple ways to utilize these strategies for simplifying and improving the reference model. In collaboration with a large insurance company, we present a use case in automatic insurance acceptance, and show how professional data scientists were enabled to understand a complex model and improve the production model based on these insights.

SplitRPC: A {Control + Data} Path Splitting RPC Stack for ML Inference Serving

The growing adoption of hardware accelerators driven by their intelligent compiler and runtime system counterparts has democratized ML services and precipitously reduced their execution times. This motivates us to shift our attention to efficiently serve these ML services under distributed settings and characterize the overheads imposed by the RPC mechanism ('RPC tax') when serving them on accelerators. The RPC implementations designed over the years implicitly assume the host CPU services the requests, and we focus on expanding such works towards accelerator-based services. While recent proposals calling for SmartNICs to take on this task are reasonable for simple kernels, serving complex ML models requires a more nuanced view to optimize both the data-path and the control/orchestration of these accelerators. We program today's commodity network interface cards (NICs) to split the control and data paths for effective transfer of control while efficiently transferring the payload to the accelerator. As opposed to unified approaches that bundle these paths together, limiting the flexibility in each of these paths, we design and implement SplitRPC - a control + data path optimizing RPC mechanism for ML inference serving. SplitRPC allows us to optimize the datapath to the accelerator while simultaneously allowing the CPU to maintain full orchestration capabilities. We implement SplitRPC on both commodity NICs and SmartNICs and demonstrate how GPU-based ML services running different compiler/runtime systems can benefit. For a variety of ML models served using different inference runtimes, we demonstrate that SplitRPC is effective in minimizing the RPC tax while providing significant gains in throughput and latency over existing kernel by-pass approaches, without requiring expensive SmartNIC devices.

Investigation of microwave assisted synthesis of Schiff Base derived Metal-Chelates by liquid invert sugar containing D-Glucose

The ethanol-soluble (6E,6'E)-6,6'-(1,2-phenylenebis(azan-1-yl-1-ylidene))dihexane-1,2,3,4,5-pentaol (LISPDA) and (E)-6-(2-hydroxyphenylimino)hexane-1,2,3,4,5-pentaol (LISAP) were investigated in this study. Condensation of 1,2-phenylenediamine and 2-aminophenol with 2,3,4,5,6-pentahydroxyhexanal (GL) in the presence of weak acid as a catalyst under mono-mode microwave heating yielded the glucose-Schiff base product. A one-pot microwave-assisted reaction in a short reaction time was required to complete the imine condensation reaction with 90% yield and no byproducts. The molecular structures of these were discovered to be in an extended conformation, with the aromatic moiety coplanar with that of the imine bond and the imine bond exhibiting trans-geometry. Schiff base metal complexes were successfully synthesized by combining 1,2-phenylenediamine and 2-aminophenol with D-glucose and Co(II), Ni(II), and Cu(II). Elemental analysis, magnetic susceptibility, thermal analysis, and electronic and infrared spectral studies were used to determine the structural assessment of ML2 complexes.