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U l d c l i c k t h e “... Page 33: H O W T O U S E D I G I T A L A U D I O M A N A G E R S O F T W A R How to use Digital Audio Manager Software How to use Digital Audio Man- ager Software First connect the Digital Audio and your PC with the USB cable. Insert the bigger end o f t h e c a b l e t o a U S B P o r t o f P C , t h e s m a l l e r t o the USB port of the Digital Audio. Page 34 How to use Digital Audio Manager Software Click the icon on the Desktop, then the interface of Digital Audio manage system will be shown.. Page 35: Sending Files To Digital Audio Sending files to Digital Audio B e f o r e s e n d i n g , p l e a s e t u r n o n t h e D i g i t a l Audio and connect it to your PC with presented USB cable. Page 36 S e n d i n g f i l e s t o D i g i t a l A u d i o Click the Download button or select the “Download” option in the tool menu. An interface of Copy will appear. System will set up an EDISK directory a u t o m a t i c a l l y a n d p l a c e t h e n o n - a u d i o f i l e s to EDISK in the removable disk of the... Page 37 Digital Audio after you download the non- MP3 or WMA files. The management system will examine the Digital Audio automatically. When the USB c a b l e i s p u l l e d o u t f r o m t h e D i g i t a l A u d i o , the management system won’t work. Page 38 S e n d i n g f i l e s t o D i g i t a l A u d

N 1 − Δ S N 2 ) , σ 1 = σ K , σ 2 = σ R , where C S is a constant. According to the competition results of neural networks N 1 and N 2 , we update μ 1 , μ 2 , σ 1 , σ 2 as follows: μ w i n n e r + = σ w i n n e r 2 c · v μ w i n n e r − μ l o s e r c , ε c μ l o s e r = σ l o s e r 2 c · v μ w i n n e r − μ l o s e r c , ε c σ w i n n e r 2 ∗ = 1 − σ w i n n e r 2 c 2 · ω μ w i n n e r − μ l o s e r c , ε c σ l o s e r 2 ∗ = 1 − σ w i n n e r 2 c 2 · ω μ w i n n e r − μ l o s e r c , ε c After updating μ 1 , μ 2 , σ 1 , σ 2 , we can update λ K and λ R as follows: λ K = μ 1 Δ K N 1 − Δ K N 2 λ R = μ 2 + C S Δ R ^ N 2 − Δ R ^ N 1 C K , R = 2 β 2 + σ w i n n e r 2 + σ l o s e r 2 Similarly, we update R ^ N and S N via the competition between two neural networks, N 2 and N 3 :Let μ 3 = λ R ( Δ R ^ N 2 − Δ R ^ N 3 ) , μ 4 = − [ λ s ( Δ S N 2 − Δ S N 3 ) + C K ] , C K = λ K ( Δ K N 2 − Δ K N 3 ) , σ 3 = σ R , σ 4 = σ S , where C K is a constant. According to the competition results of two neural networks, N 2 and N 3 , we update μ 3 , μ 4 , σ 3 , σ 4 .After updating μ 3 , μ 4 , σ 3 , σ 4 , we can update λ K and λ R as follows: λ R = μ 3

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R + = σ w i n n e r 2 c · v ( μ w i n n e r − μ l o s e r c , ε c ) μ l o s e r − = σ l o s e r 2 c · v ( μ w i n n e r − μ l o s e r c , ε c ) σ w i n n e r 2 ∗ = [ 1 − σ w i n n e r 2 c 2 · w ( μ w i n n e r − μ l o s e r c , ε c ) ] σ l o s e r 2 ∗ = [ 1 − σ l o s e r 2 c 2 · w ( μ w i n n e r − μ l o s e r c , ε c ) ] update λ K , σ K , λ R σ R as: λ K = μ 1 K N 1 − K N 2 λ R = μ 2 R N 2 − R N 1 σ K = σ 1 σ R = σ 2 c 2 = 2 β 2 + σ w i n n e r 2 + σ l o s e r 2 return λ K , σ K , λ R , σ R − g e t _ a c c u r a c y ( N ) : train the neural architecture N with the dataset and return the final test accuracy. 3.2.6. Pruning NetworkTraditional network pruning typically requires pre-trained weights and specific strategies to ensure successful learning of the pruned structure. However, recent advancements in pruning-from-scratch, as demonstrated in works such as [20,21], have shown that the structure of pruned models can be directly learned from randomly initialized weights without sacrificing performance. This breakthrough enables network pruning without the need for pre-trained weights, streamlining the pruning process and reducing dependencies on external resources.Inspired by the prune-from-scratch approach, we extend our methodology to propose a pruning-operation-by-importance NAS method aimed at reducing the search space and enhancing search efficiency. In our approach, we iteratively prune one candidate operation with the least importance on each edge in a single round. This strategy effectively reduces the algorithmic complexity from | O | E to | O | · E . We encapsulate our training-free and pruning-based algorithm, referred to as TTNAS, in Algorithm 2. This algorithmic framework streamlines the search process and facilitates efficient exploration of architectural configurations, leading to improved performance and computational efficiency. Algorithm 2 Training-free TrueSkill NASInput: λ