Short-form video (SFV) platforms are increasingly popular, yet the rapid context switching and their potential effects on children’s cognitive functions are not well understood. In this work, we conducted a between-subjects experiment (N = 180) to examine how YouTube Shorts affects young children’s short-term memory (STM) and working memory (WM), measured using the forward and backward digit span tasks. The study focused on two core platform features of SFV: the easy-to-use swipe interface and the recommendation system. Using a 2 × 2 factorial design, we compared four SFV group conditions that varied by interaction mode and content source, complemented by two long-form video baseline conditions (one with constant context switching and one without). Our results show that the feature combinations and the baseline comparisons were not associated with changes in STM or WM. However, swipe interaction increased video switching, while recommendation-based content increased category switching. The higher combined levels of video and category switching across participants were associated with marginal effects on working memory performance, while STM remained unaffected.

This study investigates the impact of network capabilities and deep learning techniques on companies' financial performance within buyer-supplier networks. It broadens the scope by incorporating network measures such as closeness and network constraint, whereas previous studies have primarily focused on suitable buyer-supplier relationships. The analysis evaluates the effects of these network measures on companies' financial performance metrics, including asset growth rate, return on assets, and more. In addition, this study explores the impact of extended networks on company performance using deep learning techniques. The results show that companies' network capabilities are positively associated with their financial performance, highlighting the critical role of network positions in driving success. Furthermore, the findings suggest that extending the network through deep learning offers significant benefits for companies.

Augmentative and alternative communication (AAC) technologies are widely used to help non-verbal children enable communication. For AAC-aided communication to be successful, caregivers should support children with consistent intervention strategies in various settings. As such, caregivers need to continuously observe and discuss children's AAC usage to create a shared understanding of these strategies. However, caregivers often find it challenging to effectively collaborate with one another due to a lack of family involvement and the unstructured process of collaboration. To address these issues, we present TalkingBoogie, which consists of two mobile apps: TalkingBoogie-AAC for caregiver-child communication, and TalkingBoogie-coach supporting caregiver collaboration. Working together, these applications provide contextualized layouts for symbol arrangement, scaffold the process of sharing and discussing observations, and induce caregivers' balanced participation. A two-week deployment study with four groups (N=11) found that TalkingBoogie helped increase mutual understanding of strategies and encourage balanced participation between caregivers with reduced cognitive loads.

Continuous tracking young children's development is important for parents because early detection of developmental delay can lead to better treatment through early intervention. Screening tests, often based on questions answered by a parent, are used to assess children's development, but responses from only one parent can be subjective and even inaccurate due to limited memory and observations. In this work, we propose a collaborative child development tracking system, where screening test responses are collected through collaboration between parents or caregivers. We implement BebeCODE, a mobile system that encourages parents to independently answer all developmental questions for a given age and resolve disagreements through chatting, image/video sharing, or asking a third person. A 4-week deployment study of BebeCODE with 12 families found that parents had approximately 22% disagreements about questions regarding their children's developmental and BebeCODE helped them reach a consensus. Parents also reported that their awareness of their child's development, increased with BebeCODE.

Parent-training is commonly used to support intervention of children with language delay. Unfortunately, parents find it difficult to apply the training to their child in everyday life and often give up on their parent-child interaction. In this work, we propose and evaluate TalkLIME -- a mobile system that provides real-time feedback to improve the parent-child interaction and reinforce parent-training intervention. We first conduct a survey to understand parents' feedback preference for the mobile system and determine that a non-invasive feedback using the mobile phones screen is preferable. TalkLIME was developed to provide real-time feedback through the mobile phone screen while also providing motivation to the parents to consistently continue parent-child interaction through both short-term and long-term goals. A six-weeks user study was conducted with eight parents and their children with language delay. Our results show that the experimental group who used TalkLIME showed a significant improvement in the child's initiation ratio, an important metric in the language development of children.

High performance for a GPGPU workload is obtained by maximizing parallelism and fully utilizing the available resources. However, this is not necessarily energy efficient, especially for memory-intensive GPGPU workloads. In this work, we propose Throttle CTA (cooperative-thread array) Scheduling (TCS) where we leverage two type of throttling - throttling the number of actives cores and throttling of warp execution in the cores - to improve energy-efficiency for memory-intensive GPGPU workloads. The algorithm requires the global CTA or thread block scheduler to reduce the number of cores with assigned thread blocks while leveraging the local warp scheduler to throttle memory requests for some of the cores to further reduce power consumption. The proposed TCS scheduling does not require off-line analysis but can be done dynamically during execution. Instead of relying on conventional metrics such as miss-per-kilo-instruction (MPKI), we leverage the memory access latency metric to determine the memory intensity of the workloads. Our evaluations show that TCS reduces energy by up to 48% (38% on average) across different memory-intensive workload while having very little impact on performance for compute-intensive workloads.

High performance in GPGPU workloads is obtained by maximizing parallelism and fully utilizing the available resources. The thousands of threads are assigned to each core in units of CTA (Cooperative Thread Arrays) or thread blocks - with each thread block consisting of multiple warps or wavefronts. The scheduling of the threads can have significant impact on overall performance. In this work, explore alternative thread block or CTA scheduling; in particular, we exploit the interaction between the thread block scheduler and the warp scheduler to improve performance. We explore two aspects of thread block scheduling - 1) LCS (lazy CTA scheduling) which restricts the maximum number of thread blocks allocated to each core, and 2) BCS (block CTA scheduling) where consecutive thread blocks are assigned to the same core. For LCS, we leverage a greedy warp scheduler to help determine the optimal number of thread blocks by only measuring the number of instructions issued while for BCS, we propose an alternative warp scheduler that is aware of the “block” of CTAs allocated to a core. With LCS and the observation that maximum number of CTAs does not necessary maximize performance, we also propose mixed concurrent kernel execution that enables multiple kernels to be allocated to the same core to maximize resource utilization and improve overall performance.

Nonverbal children with communication disorders have difficulties communicating through oral language. To facilitate communication, Augmentative and Alternative Communication (AAC) is commonly used in intervention settingss. Different forms of AAC have been used; however, one key aspect of AAC is that children have different preferences and needs in the intervention process. One particular AAC method does not necessarily work for all children. Although robots have been used in different applications, this is one of the first times that robots have been used for improvement of communication in nonverbal children. In this work, we explore robot-based AAC through humanoid robots that assist therapists in interventions with nonverbal children. Through playing activities, our study assessed changes in gestures, vocalization, speech, and verbal expression in children. Our initial results show that robot-based AAC intervention has a positive impact on the communication skills of nonverbal children.

The scalability trends of modern semiconductor technology lead to increasingly dense multicore chips. Unfortunately, physical limitations in area, power, off-chip bandwidth, and yield constrain single-chip designs to a relatively small number of cores, beyond which scaling becomes impractical. Multi-chip designs overcome these constraints, and can reach scales impossible to realize with conventional single-chip architectures. However, to deliver commensurate performance, multi-chip architectures require a cross-chip interconnect with bandwidth, latency, and energy consumption well beyond the reach of electrical signaling. We propose Galaxy, an architecture that enables the construction of a many-core "virtual chip" by connecting multiple smaller chiplets through optical fibers. The low optical loss of fibers allows the flexible placement of chiplets, and offers simpler packaging, power, and heat requirements. At the same time, the low latency and high bandwidth density of optical signaling maintain the tight coupling of cores, allowing the virtual chip to match the performance of a single chip that is not subject to area, power, and bandwidth limitations. Our results indicate that Galaxy attains speedup of 2.2x over the best single-chip alternatives with electrical or photonic interconnects (3.4x maximum), and 2.6x smaller energy-delay product (6.8x maximum). We show that Galaxy scales to 4K cores and attains 2.5x speedup at 6x lower laser power compared to a Macrochip with silicon waveguides.

Intraorganizational units play a critical role in KM processes of acquiring, creating, exchanging, and utilizing knowledge assets. While much attention has been directed to effective knowledge strategies for supporting organizational KM processes, there is a lack of insightful research on knowledge strategy and its implementation at the work-unit level. This study examines two types of work unit knowledge processing styles (i.e., codification and personalization) and explores the relationship between critical determinants (i.e., task, organizational culture, and technology) and knowledge processing styles. The results showed that task variety and task analyzability were strongly associated with both knowledge processing styles. Interestingly, task interdependence and autonomy were significantly related only to personalization, whereas IT support was strongly associated with codification. The findings from this study suggest that the unit’s organizational variables should harmonize appropriately with its knowledge processing styles.