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Triboelectric nanogenerators (TENGs) represent an autonomous and sustainable technology for power generation, aiming to convert small vibrations into electricity. Through molecular control of Si crystals and the application of conductive atomic force microscopy, I have addressed crucial unresolved inquiries about the influencing factors of tribocurrent. This understanding has been leveraged to elucidate the operational principles of semiconductor-based direct-current triboelectric nanogenerators (DC-TENGs).

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture. The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model's superior performance to the current state-of-the-art approaches. Persona-Gestor improves the system's usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology. Supplementary videos and code can be accessed at https://zf223669.github.io/Diffmotion-v2-website, Comment: 12 pages

One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010). In this problem, we aim to track the number of events that occur over time, while hiding the existence of every single event. More specifically, in every time step $t\in[T]$ we learn (in an online fashion) that $\Delta_t\geq 0$ new events have occurred, and must respond with an estimate $n_t\approx\sum_{j=1}^t \Delta_j$. The privacy requirement is that all of the outputs together, across all time steps, satisfy event level differential privacy. The main question here is how our error needs to depend on the total number of time steps $T$ and the total number of events $n$. Dwork et al. (2015) showed an upper bound of $O\left(\log(T)+\log^2(n)\right)$, and Henzinger et al. (2023) showed a lower bound of $\Omega\left(\min\{\log n, \log T\}\right)$. We show a new lower bound of $\Omega\left(\min\{n,\log T\}\right)$, which is tight w.r.t. the dependence on $T$, and is tight in the sparse case where $\log^2 n=O(\log T)$. Our lower bound has the following implications: $\bullet$ We show that our lower bound extends to the "online thresholds problem", where the goal is to privately answer many "quantile queries" when these queries are presented one-by-one. This resolves an open question of Bun et al. (2017). $\bullet$ Our lower bound implies, for the first time, a separation between the number of mistakes obtainable by a private online learner and a non-private online learner. This partially resolves a COLT'22 open question published by Sanyal and Ramponi. $\bullet$ Our lower bound also yields the first separation between the standard model of private online learning and a recently proposed relaxed variant of it, called private online prediction.

We study the cost of parallelizing weak-to-strong boosting algorithms for learning, following the recent work of Karbasi and Larsen. Our main results are two-fold: - First, we prove a tight lower bound, showing that even "slight" parallelization of boosting requires an exponential blow-up in the complexity of training. Specifically, let $\gamma$ be the weak learner's advantage over random guessing. The famous \textsc{AdaBoost} algorithm produces an accurate hypothesis by interacting with the weak learner for $\tilde{O}(1 / \gamma^2)$ rounds where each round runs in polynomial time. Karbasi and Larsen showed that "significant" parallelization must incur exponential blow-up: Any boosting algorithm either interacts with the weak learner for $\Omega(1 / \gamma)$ rounds or incurs an $\exp(d / \gamma)$ blow-up in the complexity of training, where $d$ is the VC dimension of the hypothesis class. We close the gap by showing that any boosting algorithm either has $\Omega(1 / \gamma^2)$ rounds of interaction or incurs a smaller exponential blow-up of $\exp(d)$. -Complementing our lower bound, we show that there exists a boosting algorithm using $\tilde{O}(1/(t \gamma^2))$ rounds, and only suffer a blow-up of $\exp(d \cdot t^2)$. Plugging in $t = \omega(1)$, this shows that the smaller blow-up in our lower bound is tight. More interestingly, this provides the first trade-off between the parallelism and the total work required for boosting., Comment: appeared in SODA 2024

Triboelectric nanogenerators (TENGs) are an emerging energy harvesting technology able to convert ubiquitous mechanical energy into electricity. Friction, static charging and flexoelectricity are all involved in the mechanism underpinning TENG operation, but their relative contribution has remained elusive. Here we used dynamic and static conductive atomic force microscopy (C-AFM) measurements on monolayer-modified silicon crystals to detect evidence of a relationship between friction and zero-bias current, and between pressure and the direction of the putative flexovoltage. We demonstrate that a static electricity-related tribovoltage is probably responsible for a friction excess, and that surprisingly this friction excess is found to be dependent on the doping level and type of the silicon substrate. Such friction excess is however no longer measurable once current is allowed to flow across the junction. This observation points to an electrostatic origin of friction in silicon-based Schottky TENGs, and suggests that the zero external bias DC current is at least in part an electronic flow to neutralize static charges. Further, the sign of the zero-bias current, but not its magnitude, is independent of the semiconductor doping type, which is again suggestive of surface statics being a main contributor to the zero-bias output rather than exclusively a space-charge effect. We also reveal the presence of a junction flexovoltage under pressures common in AFM experiments (GPa), even for negligible lateral friction. In a static Pt–monolayer–n-type Si junction the flexovoltage carries the same sign as the tribovoltage, and can reach such magnitude to overwrite external voltages as high as 2 V. The immediate implication is that the flexovoltage is likely to have i) a strong contribution to the zero-bias output of a n-Si Schottky TENG, ii) a negative effect on the output of a p-Si TENG, and iii) its detection can be straightforward, as we discovered that flexoelectricity man

We propose the \emph{Target Charging Technique} (TCT), a unified privacy analysis framework for interactive settings where a sensitive dataset is accessed multiple times using differentially private algorithms. Unlike traditional composition, where privacy guarantees deteriorate quickly with the number of accesses, TCT allows computations that don't hit a specified \emph{target}, often the vast majority, to be essentially free (while incurring instead a small overhead on those that do hit their targets). TCT generalizes tools such as the sparse vector technique and top-$k$ selection from private candidates and extends their remarkable privacy enhancement benefits from noisy Lipschitz functions to general private algorithms.

We show a new PRG construction fooling depth-$d$, size-$m$ $\mathsf{AC}^0$ circuits within error $\varepsilon$, which has seed length $O(\log^{d-1}(m)\log(m/\varepsilon)\log\log(m))$. Our PRG improves on previous work (Trevisan and Xue 2013, Servedio and Tan 2019, Kelley 2021) from various aspects. It has optimal dependence on $\frac{1}{\varepsilon}$ and is only one ``$\log\log(m)$'' away from the lower bound barrier. For the case of $d=2$, the seed length tightly matches the best-known PRG for CNFs (De et al. 2010, Tal 2017). There are two technical ingredients behind our new result; both of them might be of independent interest. First, we use a partitioning-based approach to construct PRGs based on restriction lemmas for $\mathsf{AC}^0$, which follows and extends the seminal work of (Ajtai and Wigderson 1989). Second, improving and extending prior works (Trevisan and Xue 2013, Servedio and Tan 2019, Kelley 2021), we prove a full derandomization of the powerful multi-switching lemma for a family of DNFs (H{\aa}stad 2014)., Comment: The conference version appeared in CCC2022

Composition theorems are general and powerful tools that facilitate privacy accounting across multiple data accesses from per-access privacy bounds. However they often result in weaker bounds compared with end-to-end analysis. Two popular tools that mitigate that are the exponential mechanism (or report noisy max) and the sparse vector technique, generalized in a recent private selection framework by Liu and Talwar (STOC 2019). In this work, we propose a flexible framework of private selection and testing that generalizes the one proposed by Liu and Talwar, supporting a wide range of applications. We apply our framework to solve several fundamental tasks, including query releasing, top-k selection, and stable selection, with improved confidence-accuracy tradeoffs. Additionally, for online settings, we apply our private testing to design a mechanism for adaptive query releasing, which improves the sample complexity dependence on the confidence parameter for the celebrated private multiplicative weights algorithm of Hardt and Rothblum (FOCS 2010).

The Private Aggregation of Teacher Ensembles (PATE) framework is a versatile approach to privacy-preserving machine learning. In PATE, teacher models that are not privacy-preserving are trained on distinct portions of sensitive data. Privacy-preserving knowledge transfer to a student model is then facilitated by privately aggregating teachers' predictions on new examples. Employing PATE with generative auto-regressive models presents both challenges and opportunities. These models excel in open ended \emph{diverse} (aka hot) tasks with multiple valid responses. Moreover, the knowledge of models is often encapsulated in the response distribution itself and preserving this diversity is critical for fluid and effective knowledge transfer from teachers to student. In all prior designs, higher diversity resulted in lower teacher agreement and thus -- a tradeoff between diversity and privacy. Prior works with PATE thus focused on non-diverse settings or limiting diversity to improve utility. We propose \emph{hot PATE}, a design tailored for the diverse setting. In hot PATE, each teacher model produces a response distribution that can be highly diverse. We mathematically model the notion of \emph{preserving diversity} and propose an aggregation method, \emph{coordinated ensembles}, that preserves privacy and transfers diversity with \emph{no penalty} to privacy or efficiency. We demonstrate empirically the benefits of hot PATE for in-context learning via prompts and potential to unleash more of the capabilities of generative models.

In his breakthrough paper, Raz showed that any parity learning algorithm requires either quadratic memory or an exponential number of samples [FOCS'16, JACM'19]. A line of work that followed extended this result to a large class of learning problems. Until recently, all these results considered learning in the streaming model, where each sample is drawn independently, and the learner is allowed a single pass over the stream of samples. Garg, Raz, and Tal [CCC'19] considered a stronger model, allowing multiple passes over the stream. In the $2$-pass model, they showed that learning parities of size $n$ requires either a memory of size $n^{1.5}$ or at least $2^{\sqrt{n}}$ samples. (Their result also generalizes to other learning problems.) In this work, for any constant $q$, we prove tight memory-sample lower bounds for any parity learning algorithm that makes $q$ passes over the stream of samples. We show that such a learner requires either $\Omega(n^{2})$ memory size or at least $2^{\Omega(n)}$ samples. Beyond establishing a tight lower bound, this is the first non-trivial lower bound for $q$-pass learning for any $q\ge 3$. Similar to prior work, our results extend to any learning problem with many nearly-orthogonal concepts. We complement the lower bound with an upper bound, showing that parity learning with $q$ passes can be done efficiently with $O(n^2/\log q)$ memory., Comment: To appear at FOCS 2023

Triboelectric nanogenerators (TENGs) based on sliding silicon-organic monolayer-metal Schottky diodes are an emerging autonomous direct-current (DC) current supply technology. Herein, using conductive atomic force microscopy and electrochemical techniques, we explore the optimal etching conditions toward the preparation of DC TENGs on Si(211), a readily available, highly conductive, and underexplored silicon crystallographic cut. We report optimized conditions for the chemical etching of Si(211) surfaces with subnanometer root-mean-square roughness, explore Si(211) chemical passivation, and unveil a relationship between the electrochemical charge-transfer behavior at the silicon-liquid interface and the zero-applied bias current output from the corresponding dynamic silicon-organic monolayer-platinum system. The overall aim is to optimize the etching and functionalization of the relatively underexplored Si(211) facet, toward its application in out-of-equilibrium Schottky diodes as autonomous power supplies. We also propose the electrochemical behavior of surface-confined redox couples as a diagnostic tool to anticipate whether or not a given surface will perform satisfactorily when used in a TENG design.

Over the past decade, triboelectric nanogenerators (TENGs) – small and portable devices designed to harvest electricity from mechanical vibrations and friction – have matured from a niche theme of electrical engineering research into multidisciplinary research encompassing materials science, physics, and chemistry. Recent advances in both the fundamental understanding and performances of TENGs have been made possible by surface chemistry, electrochemistry, and theoretical chemistry research entering this active and promising field. This short review focuses on the recent developments of direct-current (DC) TENGs, where sliding friction or repetitive contact–separation cycles between the surface of polymers, metals, chemically modified semiconductors, and more recently even by the simple contact of surfaces with water solutions, can output DC suitable to power electronic devices without the need of additional rectification. We critically analyze the role of surface chemistry toward maximizing DC TENG outputs and device longevity. The major current hypotheses about their working mechanism(s) are also discussed.

Triboelectric nanogenerators (TENGs) are an autonomous and sustainable power-generation technology, seeking to harvest small vibrations into electricity. Here, by achieving molecular control of oxide-free Si crystals and using conductive atomic force microscopy, we address key open questions and use this knowledge to demonstrate zero-applied-bias current densities as high as 109 A/m2. Key to achieve this output, is to use a proton-exchangeable organic monolayer that simultaneously introduces a sufficiently high density of surface states (assessed as changes to carrier recombination velocities) coupled to a strong surface dipole in the form of a surface alkoxide anion (Si–monolayer–O−). We also demonstrate that the DC output of a Schottky diode TENG does not track the energy released as friction. This removes the complexity of controlling an unavoidable stick–slip motion, bypassing the requirement of aligning sliding motion and substrate topographical features. We reveal that there is no apparent correlation between the current of a static (biased) junction and the tribocurrent of the same junction when under motion and unbiased.

Composition theorems are general and powerful tools that facilitate privacy accounting across multiple data accesses from per-access privacy bounds. However they often result in weaker bounds compared with end-to-end analysis. Two popular tools that mitigate that are the exponential mechanism (or report noisy max) and the sparse vector technique. They were generalized in a couple of recent private selection/test frameworks, including the work by Liu and Talwar (STOC 2019), and Papernot and Steinke (ICLR 2022). In this work, we first present an alternative framework for private selection and testing with a simpler privacy proof and equally-good utility guarantee. Second, we observe that the private selection framework (both previous ones and ours) can be applied to improve the accuracy/confidence trade-off for many fundamental privacy-preserving data-analysis tasks, including query releasing, top-$k$ selection, and stable selection. Finally, for online settings, we apply the private testing to design a mechanism for adaptive query releasing, which improves the sample complexity dependence on the confidence parameter for the celebrated private multiplicative weights algorithm of Hardt and Rothblum (FOCS 2010)., Comment: Appeared in ITCS 2023; This version: revised introduction and related works sections

The problem of learning threshold functions is a fundamental one in machine learning. Classical learning theory implies sample complexity of $O(\xi^{-1} \log(1/\beta))$ (for generalization error $\xi$ with confidence $1-\beta$). The private version of the problem, however, is more challenging and in particular, the sample complexity must depend on the size $|X|$ of the domain. Progress on quantifying this dependence, via lower and upper bounds, was made in a line of works over the past decade. In this paper, we finally close the gap for approximate-DP and provide a nearly tight upper bound of $\tilde{O}(\log^* |X|)$, which matches a lower bound by Alon et al (that applies even with improper learning) and improves over a prior upper bound of $\tilde{O}((\log^* |X|)^{1.5})$ by Kaplan et al. We also provide matching upper and lower bounds of $\tilde{\Theta}(2^{\log^*|X|})$ for the additive error of private quasi-concave optimization (a related and more general problem). Our improvement is achieved via the novel Reorder-Slice-Compute paradigm for private data analysis which we believe will have further applications.

In the Element Distinctness problem, one is given an array $a_1,\dots, a_n$ of integers from $[poly(n)]$ and is tasked to decide if $\{a_i\}$ are mutually distinct. Beame, Clifford and Machmouchi (FOCS 2013) gave a low-space algorithm for this problem running in space $S(n)$ and time $T(n)$ where $T(n) \le \widetilde{O}(n^{3/2}/S(n)^{1/2})$, assuming a random oracle (i.e., random access to polynomially many random bits). A recent breakthrough by Chen, Jin, Williams and Wu (SODA 2022) showed how to remove the random oracle assumption in the regime $S(n) = polylog(n)$ and $T(n) = \widetilde{O}(n^{3/2})$. They designed the first truly $polylog(n)$-space, $\widetilde{O}(n^{3/2})$-time algorithm by constructing a small family of hash functions $\mathcal{H} \subseteq \{h | h:[poly(n)]\to [n]\}$ with a certain pseudorandom property. In this paper, we give a significantly simplified analysis of the pseudorandom hash family by Chen et al. Our analysis clearly identifies the key pseudorandom property required to fool the BCM algorithm, allowing us to explore the full potential of this construction. As our main result, we show a time-space tradeoff for Element Distinctness without random oracle. Namely, for every $S(n),T(n)$ such that $T\approx \widetilde{O}(n^{3/2}/S(n)^{1/2})$, our algorithm can solve the problem in space $S(n)$ and time $T(n)$. Our algorithm also works for a related problem Set Intersection, for which this tradeoff is tight due to a matching lower bound by Dinur (Eurocrypt 2020). As two additional contributions, we show a more general pseudorandom property of the hash family, and slightly improve the seed length to sample the pseudorandom hash function., Comment: To appear in SODA 2023. Abstract shortened to fit into the requirement of arXiv

An interactive mechanism is an algorithm that stores a data set and answers adaptively chosen queries to it. The mechanism is called differentially private, if any adversary cannot distinguish whether a specific individual is in the data set by interacting with the mechanism. We study composition properties of differential privacy in concurrent compositions. In this setting, an adversary interacts with k interactive mechanisms in parallel and can interleave its queries to the mechanisms arbitrarily. Previously, Vadhan and Wang [2021] proved an optimal concurrent composition theorem for pure-differential privacy. We significantly generalize and extend their results. Namely, we prove optimal parallel composition properties for several major notions of differential privacy in the literature, including approximate DP, R\'enyi DP, and zero-concentrated DP. Our results demonstrate that the adversary gains no advantage by interleaving its queries to independently running mechanisms. Hence, interactivity is a feature that differential privacy grants us for free. Concurrently and independently of our work, Vadhan and Zhang [2022] proved an optimal concurrent composition theorem for f-DP [Dong et al., 2022], which implies our result for the approximate DP case., Comment: To appear in NeurIPS 2022; Revised according to reviewers' feedback; Mentioned a concurrent and independent work

CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements. The sketch supports recovering $\ell_2$-heavy hitters of a vector (entries with $v[i]^2 \geq \frac{1}{k}\|\boldsymbol{v}\|^2_2$). We study the robustness of the sketch in adaptive settings where input vectors may depend on the output from prior inputs. Adaptive settings arise in processes with feedback or with adversarial attacks. We show that the classic estimator is not robust, and can be attacked with a number of queries of the order of the sketch size. We propose a robust estimator (for a slightly modified sketch) that allows for quadratic number of queries in the sketch size, which is an improvement factor of $\sqrt{k}$ (for $k$ heavy hitters) over prior work.

In the range avoidance problem, the input is a multi-output Boolean circuit with more outputs than inputs, and the goal is to find a string outside its range (which is guaranteed to exist). We show that well-known explicit construction questions such as finding binary linear codes achieving the Gilbert-Varshamov bound or list-decoding capacity, and constructing rigid matrices, reduce to the range avoidance problem of log-depth circuits, and by a further recent reduction [Ren, Santhanam, and Wang, FOCS 2022] to NC⁰₄ circuits where each output depends on at most 4 input bits. On the algorithmic side, we show that range avoidance for NC⁰₂ circuits can be solved in polynomial time. We identify a general condition relating to correlation with low-degree parities that implies that any almost pairwise independent set has some string that avoids the range of every circuit in the class. We apply this to NC⁰ circuits, and to small width CNF/DNF and general De Morgan formulae (via a connection to approximate-degree), yielding non-trivial small hitting sets for range avoidance in these cases.

Functionalizing Si with self-assembled monolayers (SAMs) paves the way for integrating the semiconducting properties of Si with the diverse properties of organic molecules. Highly packed SAMs such as those formed from alkyl chains protect Si from reoxidation in an ambient environment. Such monolayers have been largely considered oxide-free, but the effect of nanoscale reoxidation on the electrochemical kinetics of Si-based SAMs remains unknown. Here, we systematically study the effect of the oxide growth on the electrochemical charge-transfer kinetics of ferrocene-terminated SAMs on Si by exposing the surfaces to ambient conditions for controlled periods of time. X-ray photoelectron spectroscopy and atomic force microscopy revealed a gradual growth of silicon oxide (SiOx) on the surfaces over time. The oxide growth is accompanied by a decrease in the ferrocene surface coverage and a concomitant decrease in the electron transfer rate constant (ket) measured by electrochemical impedance spectroscopy. The drop in ket is attributed to a greater spacing between the ferrocene moieties induced by the surface oxide, which in turn blocks lateral electron transfer between neighboring ferrocene moieties. These findings explain the highly scattered literature data on electron transfer kinetics for monolayers on Si and have implications for the proper design of Si-based molecular electronic devices.

Optimizing the kinetics of an electrode reaction is central to the design of devices whose function spans from sensing to energy conversion. Electrode kinetics depends strongly on electrode surface properties, but the search for optimal materials is often a trial-and-error process. Recent research has revealed a pronounced facet-dependent electrical conductivity for silicon, implicitly suggesting that rarely used crystallographic cuts of this technologically relevant material had been entirely overlooked for the fabrication of electrodes. By first protecting silicon from anodic decomposition through Si-C-bound organic monolayers, conductive atomic force microscopy demonstrates that conductivity decreases in the order (211) ≫ (110) > (111). However, charge-transfer rates for a model electrochemical reaction are similar on all these crystal orientations. These findings reveal the absence of a relationship between surface conductivity and kinetics of a surface-confined redox reaction and expand the range of silicon crystallographic orientations viable as electrode materials.

Herein we demonstrate that ionic liquids can form long-lived double layers, generating electric fields detectable by straightforward open circuit potential (OCP) measurements. In imidazolium-based ionic liquids an external negative voltage pulse leads to an exceedingly stable near-surface dipolar layer, whose field manifests as long-lived (∼1-100 h) discrete plateaus in OCP versus time traces. These plateaus occur within an ionic liquid-specific and sharp potential window, defining a simple experimental method to probe the onset of interfacial ordering phenomena, such as overscreening and crowding. Molecular dynamics modeling reveals that the OCP arises from the alignment of the individual ion dipoles to the external electric field pulse, with the magnitude of the resulting OCP correlating with the product of the projected dipole moment of the cation and the ratio between the cation diffusion coefficient and its volume. Our findings also reveal that a stable overscreened structure is more likely to form if the interface is first forced through crowding, possibly accounting for the scattered literature data on relaxation kinetics of near-surface structures in ionic liquids.

Xin Lyu, Chen Zhao, Zhi-min Yan, Hong HuaDepartment of Oral Medicine, Peking University School and Hospital of Stomatology, Beijing, People’s Republic of ChinaObjective: To systematically review and assess the efficacy, different treatment protocols (formulation, dosage, and duration), and safety of nystatin for treating oral candidiasis.Methods: Four electronic databases were searched for trials published in English till July 1, 2015. Randomized controlled trials comparing nystatin with other antifungal therapies or a placebo were included. Clinical and/or mycological cure was the outcome evaluation. A meta-analysis or descriptive study on the efficacy, treatment protocols, and safety of nystatin was conducted.Results: The meta-analysis showed that nystatin pastille was significantly superior to placebo in treating denture stomatitis. Nystatin suspension was not superior to fluconazole in treating oral candidiasis in infants, children, or HIV/AIDS patients. The descriptive investigations showed that administration of nystatin suspension and pastilles in combination for 2 weeks might achieve a higher clinical and mycological cure rate, and using the nystatin pastilles alone might have a higher mycological cure rate, when compared with using nystatin suspensions alone. Nystatin pastilles at a dose of 400,000 IU resulted in a significantly higher mycological cure rate than that administrated at a dose of 200,000 IU. Furthermore, treatment with nystatin pastilles for 4 weeks seemed to have better clinical efficacy than treatment for 2 weeks. Descriptive safety assessment showed that poor taste and gastrointestinal adverse reaction are the most common adverse effects of nystatin.Conclusion: Nystatin pastille was significantly superior to placebo in treating denture stomatitis, while nystatin suspension was not superior to fluconazole in treating oral candidiasis in infants, children, or HIV/AIDS patients. Indirect evidence from a descriptive study demonstrated